Signal processing device, signal processing method and program

Abstract

This signal processing device comprises a simulated SAR image generation unit and an evaluation calculation unit. The simulated SAR image generation unit generates simulation information including: a simulated SAR image formed from prediction values of pixels predicted to be obtained when a target region is observed in a steady state; and prediction reliability information indicating the reliability of the prediction values of the pixels. The evaluation calculation unit uses the simulation information to determine the evaluation index whereby a target SAR image, which is an analysis target, is evaluated.

Description

Signal processing device, signal processing method, and recording medium

[0001] The present invention relates to a signal processing device, a signal processing method, and a recording medium.

[0002] Various techniques have been proposed for detecting changes in a target area using a SAR image of the target area (e.g., Patent Documents 1 to 5). Here, the SAR image is a complex image in which each pixel value constituting the SAR image contains complex values ​​representing the amplitude and phase of backscattered signals observed using a SAR (synthetic aperture radar).

[0003] Patent Document 1 discloses an interferometric synthetic aperture radar device that emits radio waves from a flying object toward the ground and receives reflected waves from the ground to detect terrain changes. This interferometric synthetic aperture radar device has two antennas, a transmitter / receiver, an image processor, an interference processor, and a terrain change analysis processor.

[0004] In Patent Document 1, two antennas emit radio waves to the ground and simultaneously receive reflected waves from the ground. A transmitter / receiver unit simultaneously outputs transmitted waves to the two antennas and simultaneously inputs received waves. An image processor performs SAR reconstruction processing on the received output signals from the transmitter / receiver units. An interference processor causes interference between the output signals from the image processor and outputs a 3D image. A terrain change analysis processor extracts terrain changes in real time by comparing 3D image data acquired in real time by the interference processor with 3D image data of the same area previously acquired by the interference processor.

[0005] For example, Patent Document 1 describes that contour maps are obtained based on phase contrast images for each piece of data acquired at different times, and changes are detected based on the contour maps corresponding to the different times.

[0006] Patent Document 2 discloses a technology in which radar image data captured by a SAR (synthetic aperture radar mounted on a satellite) is subjected to synthetic aperture processing using a radar analysis device to generate a reconstructed image, and then differential processing or the like is performed on the obtained image to analyze changes in the earth's surface.

[0007] In Patent Document 2, a difference calculation unit calculates the difference in backscattering intensity of microwaves, thereby calculating the difference in multiple characteristic values ​​(scattering intensity, amount of ground change, etc.) that represent the state of the ground surface that is the subject of imaging.

[0008] U.S. Patent No. 6,277,633 discloses a computer-implemented method for determining coherency between composite images having phase and amplitude components, and states that this coherency can be determined based on the amplitude components of the images.

[0009] Patent document 4 discloses a method for detecting at least one target in an image (I) obtained by SAR, the image (I) comprising a set of pixels, each pixel having a magnitude assigned to it.

[0010] U.S. Patent No. 6,277,949 discloses a method for processing synthetic aperture radar (SAR) image data comprising a plurality of frames for each of a plurality of image geometries, the method including, for each image geometry, applying change detection to the frames corresponding to that image geometry to generate a plurality of change products.

[0011] Japanese Patent Application Publication No. 07-072244 International Publication No. 2008 / 016153 European Patent Application Publication No. 3540462 US Patent No. 10571560 British Patent Application Publication No. 2553284

[0012] Generally, for example, in an area where the signal-to-noise ratio (SNR) of the received signal used to generate a SAR image is small, the received signal corresponding to that area may change significantly due to the influence of noise, etc., even if no change occurs in that area.

[0013] However, Patent Documents 1 to 5 do not contain any description of noise contained in the received signal for generating the SAR image, so the techniques described in Patent Documents 1 to 5 may result in a decrease in the accuracy of detecting changes in the target region when the received signal changes significantly due to the influence of noise or the like.

[0014] In view of the above-mentioned problems, an example of an object of the present invention is to provide a signal processing device, a signal processing method, a program, etc. that solve the problem of accurately detecting changes in a target region using SAR images.

[0015] According to one aspect of the present invention, there is provided a signal processing device comprising: a simulated SAR image generating means for generating simulated information including a simulated SAR image composed of predicted values ​​of each pixel that are predicted to be obtained when a target region in a steady state is observed, and predicted reliability information indicating the reliability of the predicted values ​​of each pixel; and an evaluation calculation means for calculating an evaluation index for evaluating a target SAR image that is the object of analysis, using the simulated information.

[0016] According to one aspect of the present invention, there is provided a signal processing method in which one or more computers generate simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when a target region is observed in a steady state, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel, and using the simulated information, determine an evaluation index for evaluating the target SAR image that is the object of analysis.

[0017] According to one aspect of the present invention, a program is provided for causing one or more computers to generate simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when a target region is observed in a steady state, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel, and to use the simulated information to determine an evaluation index for evaluating the target SAR image that is the subject of analysis.

[0018] According to one aspect of the present invention, it is possible to accurately detect changes in a target area using SAR images.

[0019] FIG. 1 is a diagram illustrating an overview of a signal processing device according to a first embodiment. FIG. 2 is a diagram illustrating an overview of a signal processing method according to the first embodiment. FIG. 3 is a diagram illustrating an example of the functional configuration of a signal processing device according to the first embodiment. FIG. 4 is a diagram illustrating a first example of simulated information according to the first embodiment. FIG. 5 is a diagram illustrating a second example of simulated information according to the first embodiment. FIG. 6 is a diagram illustrating an example of the physical configuration of a signal processing device according to the first embodiment. FIG. 7 is a flowchart illustrating an example of signal processing according to the first embodiment. FIG. 8 is a diagram illustrating an example of a display screen according to the first embodiment. FIG. 9 is a diagram illustrating a relationship between a correlation between reflection intensity and phase and a coherence value in a general coherent change detection technique. FIG. 10 is a diagram illustrating an overview of a signal processing system SPS according to a second embodiment. FIG. 11 is a diagram illustrating an example of the configuration of a signal processing system SPS according to the second embodiment. FIG. 12 is a flowchart illustrating an example of signal processing according to the second embodiment. FIG. 13 is a diagram for explaining an example of estimating a complex reflection intensity distribution at an azimuth-range position using SAR tomography. FIG. 14 is a diagram illustrating an example of the functional configuration of an evaluation calculation unit according to a fourth embodiment. FIG. 15 is a flowchart illustrating a detailed example of evaluation index acquisition processing according to the fourth embodiment. FIG. 16 is a diagram illustrating an example of the functional configuration of an evaluation calculation unit according to a fifth embodiment. FIG. 17 is a flowchart illustrating a detailed example of evaluation index acquisition processing according to the fifth embodiment.

[0020] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In all the drawings, like components are designated by like reference numerals, and the description thereof will be omitted as appropriate.

[0021] 1 is a diagram showing an overview of a signal processing device 100 according to embodiment 1. The signal processing device 100 includes a simulated SAR image generating unit 101 and an evaluation calculating unit 102.

[0022] The simulated SAR image generation unit 101 generates simulated information including a simulated SAR image composed of predicted values ​​of each pixel that are predicted to be obtained when observing a target area in a steady state, and prediction reliability information indicating the reliability of the predicted values ​​of each pixel.

[0023] The evaluation calculation unit 102 uses the simulation information to calculate an evaluation index for evaluating the target SAR image that is the analysis target.

[0024] According to this signal processing device 100, it is possible to accurately detect changes in a target region using SAR images.

[0025] FIG. 2 is a diagram showing an outline of a signal processing method according to the first embodiment.

[0026] The simulated SAR image generation unit 101 generates simulated information including a simulated SAR image composed of predicted values ​​of each pixel that are predicted to be obtained when observing a target area in a steady state, and prediction reliability information indicating the reliability of the predicted values ​​of each pixel (step S101).

[0027] The evaluation calculation unit 102 uses the simulation information to calculate an evaluation index for evaluating the target SAR image to be analyzed (step S102).

[0028] This signal processing method makes it possible to accurately detect changes in a target area using SAR images.

[0029] A detailed example of the signal processing device 100 according to the first embodiment will be described below.

[0030] (Details) (Example of Functional Configuration of Signal Processing Device 100 According to Embodiment 1) Fig. 3 is a diagram showing an example of the functional configuration of the signal processing device 100 according to Embodiment 1. The signal processing device 100 is a device for detecting changes in a target region using a target SAR image that is an analysis target.

[0031] The target SAR image is a SAR image to be analyzed. The SAR image is a complex image in which the pixel values ​​constituting the SAR image contain complex values ​​representing reflection intensity and phase.

[0032] The target SAR image may be appropriately selected from, for example, observed SAR images. In this embodiment, a case where there is one target SAR image will be described as an example. However, there may be multiple target SAR images.

[0033] An observation SAR image is a two-dimensional complex image generated based on a received signal from an airborne SAR (synthetic aperture radar) mounted on an airborne vehicle such as a satellite or an aircraft. The received signal is, for example, a signal (e.g., microwave) transmitted from the airborne SAR and reflected by the ground surface in the target area, and represents an observed value including a reflection intensity component and a phase component. In other words, an observation SAR image is a complex image composed of the observed values ​​of each pixel that actually observed the target area. The observed values ​​of the observation SAR image are complex values ​​including a reflection intensity component and a phase component.

[0034] The signal processing device 100 includes a simulated SAR image generating unit 101 , an evaluation calculating unit 102 , a movement detecting unit 103 , a display unit 104 , and a display control unit 105 .

[0035] The simulated SAR image generating unit 101 generates simulated information including a simulated SAR image and predicted reliability information indicating the reliability of the simulated SAR image.

[0036] The simulated SAR image is a two-dimensional complex image composed of predicted values ​​for each pixel that are predicted to be obtained when a target region in a steady state is observed under the same observation conditions as the target SAR image.

[0037] The observation conditions include at least one of the position of the flying object SAR when the observation value corresponding to the target SAR image is observed, the coordinate position of the target area, the resolution, and the like.

[0038] The predicted value for each pixel corresponds to a pixel value of the simulated SAR image and includes, for example, complex values ​​representing the reflected intensity and phase components of the predicted received signal (i.e., the predicted signal).

[0039] The steady state is a state in which there is no change in the target region, or a state in which even if there is a change in the target region, the change cannot be detected.

[0040] The prediction reliability information is information indicating the reliability of the predicted value of each pixel that constitutes the simulated SAR image.

[0041] First and second examples of such simulation information are shown in Figures 4 and 5. Figure 5 is a diagram showing a second example of simulation information according to the first embodiment.

[0042] 4 is a diagram showing a first example of simulated information according to the first embodiment. The figure shows a predicted value and a predicted distribution associated with a pixel included in a simulated SAR image. That is, the figure shows an example in which prediction reliability information includes a predicted distribution (a probabilistic distribution of predicted values). The average value P of the predicted distribution shown in the figure corresponds to the predicted value. For example, if the reflection intensity component is Rp and the phase component is θp, the average value P of the predicted distribution is expressed as Rpe ｊθｐ where e is Napier's constant and j is the imaginary unit. The predictive distribution is shown by the 1σ, 2σ, and 3σ dotted circles, with σ as the standard deviation. The axis perpendicular to the paper surface indicates probability.

[0043] 5A and 5B are diagrams illustrating a second example of simulated information according to the first embodiment. Each of Figures 5A and 5B illustrates a predicted value and a standard deviation of each pixel included in a simulated SAR image. The predicted value is, for example, the average value P of the predicted distribution. In other words, the diagram illustrates an example of the prediction reliability information being a statistical index of the predicted distribution.

[0044] The predicted value may be a value other than the mean value P of the prediction distribution. The prediction reliability information may include at least one of a prediction distribution indicating a probabilistic distribution of the predicted value of each pixel, a statistical index of the prediction distribution, and a probability of the predicted value associated with each pixel. The statistical index may be at least one of the mean value, mode, variance, standard deviation, and confidence interval, for example.

[0045] 3 again, the evaluation calculation unit 102 calculates an evaluation index for evaluating the target SAR image using the simulated information generated by the simulated SAR image generation unit 101. That is, the evaluation calculation unit 102 calculates the evaluation index using the simulated SAR image and the predicted reliability information.

[0046] The evaluation index is an index used to evaluate the target SAR image that is the subject of analysis. The evaluation index is, for example, an index that probabilistically evaluates the degree to which the observed values ​​constituting the target SAR image are consistent with the predicted values ​​constituting the simulated SAR image. Furthermore, for example, the evaluation index is calculated for each corresponding pixel in the simulated SAR image and the target SAR image, and typically includes multiple evaluation indexes.

[0047] The change detection unit 103 detects a change in the target area using the evaluation index calculated by the evaluation calculation unit 102. In detail, for example, when the evaluation index is a numerical value, the change detection unit 103 detects a change in the target area based on the result of comparing the evaluation index with a predetermined threshold value.

[0048] The display unit 104 displays various types of information under the control of the display control unit 105. The display control unit 105 causes the display unit 104 to display, for example, a simulated SAR image, a target SAR image, an evaluation index, a detection result regarding a change in the target region, and the like.

[0049] So far, an example of the functional configuration of the signal processing device 100 according to the first embodiment has been described. From here, an example of the physical configuration of the signal processing device 100 according to the first embodiment will be described.

[0050] 6 is a diagram showing an example of the physical configuration of the signal processing device 100 according to embodiment 1. The signal processing device 100 physically includes, for example, a bus 1010, a processor 1020, a memory 1030, a storage device 1040, a network interface 1050, an input interface 1060, and an output interface 1070.

[0051] The bus 1010 is a data transmission path for transmitting and receiving data among the processor 1020, memory 1030, storage device 1040, network interface 1050, input interface 1060, and output interface 1070. However, the method of connecting the processor 1020 and the like to each other is not limited to bus connection.

[0052] The processor 1020 is implemented as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit).

[0053] The memory 1030 is a main storage device realized by a RAM (Random Access Memory) or the like.

[0054] The storage device 1040 is an auxiliary storage device realized by a hard disk drive (HDD), a solid state drive (SSD), a memory card, a read-only memory (ROM), or the like. The storage device 1040 stores program modules for realizing the functions of the device that includes the storage device 1040. The processor 1020 loads each of these program modules into the memory 1030 and executes them to realize the function corresponding to that program module.

[0055] The network interface 1050 is an interface for connecting a device equipped with the network interface 1050 to the network NT.

[0056] The input interface 1060 is an interface for the user to input information, and is configured from, for example, a touch panel, a keyboard, a mouse, and the like.

[0057] The output interface 1070 is an interface for presenting information to the user, and is configured, for example, by a liquid crystal panel, an organic EL (Electro-Luminescence) panel, or the like.

[0058] So far, an example of the configuration of the signal processing device 100 according to the first embodiment has been described. From here, an example of the operation of the signal processing device 100 according to the first embodiment will be described.

[0059] (Example of Operation of the Signal Processing Device 100 According to the First Embodiment) Fig. 7 is a flowchart showing an example of signal processing according to the first embodiment. The signal processing is processing for detecting changes in a target region using a target SAR image that is the analysis target. The signal processing device 100 starts the signal processing upon receiving, for example, an instruction from a user. This instruction may include information for identifying the target SAR image.

[0060] The trigger for starting the signal processing is not limited to this, and the signal processing may be repeatedly performed in real time on a target SAR image obtained by observing the target region.

[0061] (Step S101: Example of Simulation Information Generation Process) The simulated SAR image generation unit 101 generates simulation information (step S101).

[0062] In more detail, for example, the simulated SAR image generating unit 101 acquires reliable 3D information and generates simulated information using the acquired reliable 3D information. As described above, the simulated information includes a simulated SAR image and prediction reliability information indicating the reliability of a predicted value of each pixel constituting the simulated SAR image.

[0063] The reliable three-dimensional information includes three-dimensional information and estimated reliability information indicating the reliability of the three-dimensional information.

[0064] The three-dimensional information includes a three-dimensional image. More specifically, the three-dimensional information includes a three-dimensional complex image reconstructed using steady-state observation SAR images. A detailed example of such reliable three-dimensional information will be described in another embodiment.

[0065] The source information for generating the simulation information (source information) is not limited to the reliable 3D information. For example, the source information may be information indicating the steady state of the target area, such as a steady-state image based on observations by another flying object SAR. Furthermore, for example, the source information may include one or more of the temperature, displacement, etc. of the target area.

[0066] Generally, as described above, an observation SAR image is obtained based on observations by a flying object SAR. The position of the flying object SAR changes from moment to moment, and the direction in which the flying object SAR observes the target area also changes. If the observation values ​​corresponding to the observation SAR image are observed at different times, the observation conditions, such as the position of the flying object SAR, will change. Therefore, if the observation time is different, the observation SAR image will change slightly even if the target area is observed in a steady state.

[0067] As can be seen from this, it is difficult to accurately detect changes in a target area based on a target SAR image, even when comparing it with an observed SAR image obtained by observing the target area in the past.

[0068] In step S101, the simulated SAR image generator 101 generates a simulated SAR image using three-dimensional information (e.g., a three-dimensional image) generated using a steady-state observed SAR image, thereby generating a simulated SAR image under the same observation conditions as the target SAR image.

[0069] On the other hand, a three-dimensional image is composed of a plurality of three-dimensional elements (hereinafter also referred to as "voxels") and includes a value (hereinafter also referred to as "voxel value") associated with each of the plurality of voxels. The voxel value generally includes uncertainty due to SNR, layover, etc. The layover phenomenon is a phenomenon in which the positional relationship between high and low altitude locations in a SAR image is reversed. The layover phenomenon causes uncertainty when a three-dimensional image is generated using observed SAR images.

[0070] The reliable 3D information includes reliability information indicating the likelihood of such voxel values. The estimated reliability information described above is an example of reliability information when the voxel values ​​are estimated values, which will be described in other embodiments.

[0071] By including reliability information in the reliable three-dimensional information, the simulated SAR image generating unit 101 can generate prediction reliability information that indicates the reliability of the prediction value of each pixel that constitutes the generated simulated SAR image.

[0072] An example of the reliability information included in such reliable three-dimensional information is the estimated reliability information described above. A detailed example of the estimated reliability information will be described in another embodiment together with a detailed example of reliable three-dimensional information.

[0073] In addition, when there are multiple target SAR images, the simulated SAR image generating unit 101 may generate multiple simulated SAR images having the same target conditions as the multiple target SAR images, and may generate multiple pieces of simulated information corresponding to the multiple target SAR images by generating predicted reliability information corresponding to each of the multiple target SAR images.

[0074] (Step S102: Example of Evaluation Index Acquisition Process) The evaluation calculation unit 102 uses the simulation information generated in step S101 to obtain an evaluation index for evaluating the target SAR image (step S102).

[0075] In more detail, for example, the evaluation calculation unit 102 compares the target SAR image with the simulated SAR image (observed values ​​and predicted values). In this comparison, the evaluation calculation unit 102 uses prediction reliability information to evaluate changes in the target SAR image relative to the simulated SAR image. Then, as a result of the comparison, the evaluation calculation unit 102 calculates an evaluation index.

[0076] Detailed examples of the evaluation index will be described in other embodiments. Note that the evaluation index may be an index used to evaluate the target SAR image using simulation information, and is not limited to the examples given in this embodiment and other embodiments.

[0077] (Step S103: Example of Change Detection Process) The change detection unit 103 detects a change in the target region using the evaluation index calculated in step S103 (step S103).

[0078] In more detail, for example, the change detection unit 103 compares the evaluation index with a threshold value. For example, in step S102, when the evaluation calculation unit 102 compares pixel values ​​(predicted values ​​and observed values) for each corresponding pixel in the target SAR image and the simulated SAR image, an evaluation index for each pixel constituting the target SAR image is calculated. In this case, the change detection unit 103 compares each evaluation index with a threshold value for each pixel constituting the target SAR image. The threshold value may be determined in advance and may be common to all pixels, for example.

[0079] For example, the change detection unit 103 determines that a change has occurred in a pixel whose evaluation index is equal to or less than a threshold, and detects a change in a region of the target region corresponding to the pixel. Note that the method of detecting a change is not limited to this. For example, the change detection unit 103 may determine that a change has occurred in a pixel whose evaluation index is equal to or greater than a threshold, and detect a change in a region of the target region corresponding to the pixel.

[0080] The methods for determining the evaluation index and detecting changes described here are merely examples and may be modified as appropriate.

[0081] (Step S104: Example of Display Processing) The display control unit 105 displays a display screen on the display unit 104 (step S104). The display screen includes, for example, the simulated SAR images obtained in each of steps S101 to S103, the step evaluation indexes and detection results, and the target SAR image.

[0082] 8 is a diagram illustrating an example of a display screen according to the first embodiment. The display screen illustrated in the figure includes a simulated SAR image and a target SAR image. In the target SAR image, the evaluation index for each pixel calculated in step S102 is represented by, for example, color. By referring to this, a user can easily understand the change that an airplane has been observed in the target SAR image.

[0083] In step S103, a change is detected in the area corresponding to the airplane between the simulated SAR image and the target SAR image shown in FIG. 8 , and therefore, the area may be highlighted in the target SAR image. For example, the display screen may also include prediction reliability information for each pixel. The display screen is not limited to this and may be changed as appropriate.

[0084] By performing this signal processing, it is possible to obtain the evaluation index of the target SAR image using the simulated SAR image and the predicted reliability information. Furthermore, by displaying the obtained information, the user can easily recognize the evaluation index and other various information.

[0085] (Actions and Effects) As described above, according to this embodiment, the signal processing device 100 includes the simulated SAR image generation unit 101 and the evaluation calculation unit 102. The simulated SAR image generation unit 101 generates simulated information including a simulated SAR image configured from predicted values ​​of each pixel that are predicted to be obtained when a target region in a steady state is observed, and prediction reliability information indicating the reliability of the predicted values ​​of each pixel. The evaluation calculation unit 102 uses the simulated information to obtain an evaluation index that evaluates the target SAR image that is the analysis target.

[0086] This allows the evaluation index of the target SAR image to be calculated using the simulated SAR image and the predicted reliability information. Therefore, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it is possible to accurately detect changes in the target area using the SAR image.

[0087] A common technique for detecting changes by comparing two SAR images is coherent change detection, which detects minute changes from a steady state based on the value of complex correlation (coherence), which indicates the similarity between the SAR images.

[0088] Generally, when the coherence value is high, it is determined that the similarity of the intensity and phase between the SAR images is high and that there is no change in the local region. On the other hand, when the coherence value is low, it is determined that the similarity of the intensity and phase between the SAR images is low and that there is a change in the local region. Such a coherence value can be said to be an index showing the correlation of the reflection intensity and phase in the local region between the SAR images.

[0089] However, the coherence value tends to be more sensitive to changes in phase than in reflection intensity. Therefore, as shown in Figure 9, when the phase correlation is low, the coherence value will be low even if the reflection intensity correlation is high. Figure 9 shows the relationship between the correlation of reflection intensity and phase and the coherence value in a typical coherent change detection technique.

[0090] When using such coherence values, for example, in regions with low signal-to-noise ratios (SNRs), the coherence values ​​tend to be low due to noise, and therefore typical coherent change detection techniques may erroneously detect changes even when no changes have occurred in those regions.

[0091] In contrast, in this embodiment, as described above, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image, such as in areas with a low signal-to-noise ratio (SNR). For example, in areas with a low signal-to-noise ratio (SNR), the target SAR image can be evaluated taking into account the ambiguity of the predicted value in the simulated SAR image corresponding to the area. This makes it possible to obtain an evaluation index that can accurately detect changes in the area. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0092] According to this embodiment, each of the simulated SAR image and the target SAR image is a two-dimensional complex image.

[0093] A typical SAR image is a two-dimensional complex image, so this configuration makes it possible to apply this to a typical SAR image and accurately detect changes in the target region.

[0094] According to this embodiment, the predicted value of each pixel is a complex value including a reflection intensity component and a phase component that are predicted to be obtained when the target region in a steady state is observed under the same observation conditions as the target SAR image.

[0095] This allows the observation conditions assumed in the simulated SAR image to be the same as those in the target SAR image, thereby obtaining a more accurate evaluation index, and thus enabling the accurate detection of changes in the target area using the SAR image.

[0096] According to this embodiment, the prediction reliability information includes at least one of a prediction distribution indicating a probabilistic distribution of the prediction value, a statistical index of the prediction distribution, and a probability of the prediction value.

[0097] By using such predicted reliability information, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image, thereby enabling accurate detection of changes in the target region using the SAR image.

[0098] According to this embodiment, the target SAR image is composed of observed values ​​of each pixel obtained by actually observing the target region. There are multiple evaluation indices. The evaluation indices are indices that probabilistically evaluate the degree to which the observed values ​​constituting the target SAR image are consistent with the predicted values ​​constituting the simulated SAR image.

[0099] This allows the target SAR image to be evaluated taking into account the reliability of the simulated SAR image, thereby enabling changes in the target region to be detected with high accuracy using the SAR image.

[0100] According to this embodiment, the signal processing device 100 further includes a change detection unit 103 that detects a change in the target region using an evaluation index.

[0101] This allows changes in the target area to be automatically detected using the evaluation index, making it possible to easily know the changes in the target area.

[0102] Second Embodiment In this embodiment, a detailed example of reliable three-dimensional information will be described. In this embodiment, an example will be described in which reliable three-dimensional information is generated using a plurality of observation SAR images obtained by observing a target region in a steady state.

[0103] In this embodiment, for the sake of simplicity, descriptions that overlap with those of the first embodiment will be omitted as appropriate.

[0104] 10 is a diagram showing an overview of a signal processing system SPS according to embodiment 2. The signal processing system SPS includes a simulated SAR image generation unit 101, an evaluation calculation unit 102, an SAR image storage unit 211, and a reconstruction unit 206.

[0105] The simulated SAR image generator 101 generates simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when a target region in a steady state is observed, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel. The simulated SAR image generator 101 generates the simulated information using reliable 3D information and observation conditions when actual measurements are observed.

[0106] The evaluation calculation unit 102 uses the simulation information to calculate an evaluation index for evaluating the target SAR image that is the analysis target.

[0107] The SAR image storage unit 211 is a storage unit for storing an observed SAR image of a target area that is configured from observation values ​​obtained by observing the target area using a synthetic aperture radar.

[0108] The reconstruction unit 206 generates reliable three-dimensional information including three-dimensional information and estimated reliability information using a plurality of observed SAR images.

[0109] The three-dimensional information includes estimated values ​​of reflection intensity and phase for each of the three-dimensional elements that make up the target region, and the estimation reliability information indicates the reliability of the estimated value for each of the three-dimensional elements.

[0110] This signal processing system SPS makes it possible to accurately detect changes in a target area using SAR images.

[0111] A detailed example of the signal processing system SPS according to the second embodiment will be described below.

[0112] (Details) (Configuration Example of Signal Processing System SPS According to Embodiment 2) Fig. 11 is a diagram showing a configuration example of a signal processing system SPS according to embodiment 2. The signal processing system SPS is a system for detecting changes in a target region using a target SAR image that is an analysis target. The signal processing system SPS includes a signal processing device 200 and an information processing device 210.

[0113] The signal processing device 200 and the information processing device 210 are connected to each other via a network NT that may be wired, wireless, or a combination of these. The signal processing device 200 and the information processing device 210 transmit and receive information to and from each other via the network NT.

[0114] (Example of functional configuration of information processing device 210 according to embodiment 2) The information processing device 210 includes a SAR image storage unit 211. As described above, the SAR image storage unit 211 is a storage unit for storing observed SAR images of a target area. The SAR image storage unit 211 stores observed SAR images of a target area in a steady state.

[0115] The SAR image storage unit 211 may further store the observation conditions under which the observed SAR image was observed, in association with the observed SAR image. The SAR image storage unit 211 may further store an observed SAR image obtained by observing the target area in a state that is not a steady state (i.e., a state in which there is a change from the steady state).

[0116] The information stored in the SAR image storage unit 211 may be generated by the information processing device 210 or may be generated by an external device (not shown).

[0117] (Example of functional configuration of signal processing device 200 according to embodiment 2) Functionally, the signal processing device 200 includes a simulated SAR image generation unit 101 that replaces the simulated SAR image generation unit 101 according to embodiment 1, an evaluation calculation unit 102, a change detection unit 103, a display unit 104 and a display control unit 105 similar to those of embodiment 1, and a reconstruction unit 206.

[0118] The signal processing device 200 may further include an SAR image storage unit 211. In this case, the signal processing system SPS does not need to include the information processing device 210.

[0119] The simulated SAR image generating unit 101 generates simulated information using the reliable 3D information generated by the reconstruction unit 206 and the observation conditions when the actual measured values ​​are observed. In this way, the simulated SAR image generating unit 101 may be configured similarly to the simulated SAR image generating unit 101 according to the first embodiment, except that the simulated SAR image generating unit 101 acquires reliable 3D information for generating simulated information from the reconstruction unit 206.

[0120] The reconstruction unit 206 generates the above-mentioned reliable 3D information using a plurality of observation SAR images in a steady state. Note that the reconstruction unit 206 is not limited to generating the above-mentioned reliable 3D information using a plurality of observation SAR images in a steady state, and may also generate the above-mentioned reliable 3D information using one or a plurality of observation SAR images in a non-steady state.

[0121] So far, we have described an example of the functional configuration of the signal processing system SPS according to embodiment 2. From here, we will describe an example of the physical configuration of the signal processing system SPS according to embodiment 2.

[0122] (Example of physical configuration of signal processing system SPS according to embodiment 2) The signal processing system SPS according to this embodiment is physically configured from a signal processing device 200 and an information processing device 210 connected via a network NT. Each of the signal processing device 200 and the information processing device 210 is configured as a physically different single device. Each of the signal processing device 200 and the information processing device 210 may be physically configured in the same way as the signal processing device 100 according to embodiment 1.

[0123] The signal processing device 200 and the information processing device 210 may be physically configured as a single device, and in this case, the signal processing device 200 and the information processing device 210 may be connected using an internal bus 1010 instead of the network NT. Furthermore, one or both of the signal processing device 200 and the information processing device 210 may be physically configured as a plurality of devices connected via an appropriate communication line such as the network NT.

[0124] So far, we have described an example of the configuration of the signal processing system SPS according to embodiment 2. From here, we will explain an example of the operation of the signal processing system SPS according to embodiment 2.

[0125] (Example of operation of signal processing system SPS according to embodiment 2) Fig. 12 is a flowchart showing an example of signal processing according to embodiment 2. The signal processing according to this embodiment includes three-dimensional information generation processing (step S201) in addition to the processing included in the signal processing according to embodiment 1.

[0126] (Step S201: Example of 3D Information Generation Process) The reconstruction unit 206 generates reliable 3D information including 3D information and estimated reliability information using a plurality of observed SAR images (step S201).

[0127] In more detail, for example, the reconstruction unit 206 acquires a plurality of combinations of observed SAR images in a steady state and the associated observation conditions stored in the SAR image storage unit 211. Note that the reconstruction unit 206 may acquire one or more combinations of observed SAR images in a non-steady state and the associated observation conditions.

[0128] The reconstruction unit 206 generates reliable three-dimensional information using the acquired observation SAR images and observation conditions.

[0129] As described above, the reliable three-dimensional information includes three-dimensional information and estimated reliability information indicating the reliability of the three-dimensional information. The three-dimensional information includes a three-dimensional image. The three-dimensional image includes voxel values ​​corresponding to each of a plurality of voxels that constitute the three-dimensional image. The voxel values ​​are, for example, complex values ​​including reflection intensity and phase. Alternatively, for example, the voxel values ​​may be a three-dimensional complex reflection intensity distribution.

[0130] The three-dimensional information may further include temperature, displacement, etc. The three-dimensional image may be represented by three-dimensional point cloud data including information on the reflection intensity and phase of each point.

[0131] (Example of a method for generating a three-dimensional image) One technique for generating a three-dimensional image is SAR tomography. SAR tomography is a method for estimating the complex reflection intensity distribution in the elevation direction for each pixel using multiple observed SAR images. Note that any general technique may be used to generate a three-dimensional image, and is not limited to the SAR tomography described here.

[0132] The elevation direction is, for example, a direction perpendicular to the azimuth-range plane (a plane formed by the direction of travel and the line of sight of the aircraft carrying the SAR). That is, the three-dimensional image includes multiple voxel values ​​in a three-dimensional space having azimuth, range, and elevation directions. Each voxel value includes a reflection intensity (an estimated value of the reflection intensity) and a phase (an estimated value of the phase).

[0133] 13 is a diagram illustrating an example of estimating the complex reflection intensity distribution of a voxel located at an azimuth-range position x using SAR tomography, where x is a vector quantity, and the same applies hereinafter.

[0134] In the figure, s represents the elevation direction. The plane perpendicular to the direction s is the azimuth-range plane. The azimuth-range position x is the intersection of the axis representing the elevation direction in FIG. 13 and the axis representing the line of sight of the satellite ST, which is the flying object. Note that satellites ST_1...ST_n, ST_N in the figure represent satellites ST at different times. N is an integer greater than 1. n is an integer between 1 and N.

[0135] The reconstruction unit 206 uses multiple observation SAR images to estimate, for each pixel, a complex reflection intensity distribution that indicates the height, reflection intensity, and phase of buildings that are constantly present throughout the observation period. The reconstruction unit 206 combines the complex reflection intensities obtained for each pixel to generate a three-dimensional image that includes a three-dimensional complex reflection intensity distribution in the target region.

[0136] The reconstruction unit 206 uses multiple observation SAR images based on observations from slightly different orbits to generate a three-dimensional steady-state image. Therefore, the reconstruction unit 206 acquires multiple observation SAR images.

[0137] The upper part of Fig. 13 shows the first to Nth observations by the SAR satellite, where N corresponds to the total number of observations. The first to Nth observations shown in Fig. 13 correspond to a synthetic aperture in the elevation direction. In the nth observation, the relationship between the received signal (complex signal) recorded in the pixel corresponding to the azimuth-range position x and the complex reflection intensity distribution at that pixel is expressed, for example, by the following equation (1):

[0138]

[0139] g in formula (1) ｏｂｓ (x, n) represents the received signal (complex signal) recorded at the pixel corresponding to the azimuth-range position x. r(x, n) in equation (1) represents the steering vector at the pixel corresponding to the azimuth-range position x. α(x, n) in equation (1) is a vector representing the complex reflection intensity distribution at the pixel corresponding to the azimuth-range position x.

[0140] The steering vector r(x, n) can be obtained from, for example, the imaging conditions, and is expressed by the following equation (2).

[0141]

[0142] k in Equation (2) ｎ represents the altitude-phase conversion coefficient (coefficient for converting between phase and altitude). ｌ (l=1, . . . , L) represents the position in the elevation direction.

[0143] The steering vector may be expressed by an equation other than equation (2). For example, a steering vector that takes into account the influence of temperature or displacement may be used. j is the imaginary unit. π is the constant of the circumference of a circle. Exp represents an exponential function with Napier's constant as the base. C represents a complex number.

[0144] The reconstruction unit 206 solves an optimization problem using equations (1) and (2), which are determined based on the 1st to Nth observations, to obtain the complex reflection intensity distribution α of a constantly existing building at the pixel corresponding to the azimuth-range position x.

[0145] In other words, the reconstruction unit 206 determines the complex reflection intensity distribution α of the building by determining the complex reflection intensity distribution so that it matches N sets of observation data obtained by photographing the target area including the building, etc. with N sets of steering vectors.

[0146] The lower part of Figure 13 shows an example of the absolute value |αbg| of the complex reflection intensity distribution α of buildings calculated by the reconstruction unit 206. These buildings are permanently present at the pixel corresponding to the azimuth-range position x. The vertical axis of the graph shown in the lower part of Figure 13 represents the reflection intensity, which corresponds to the absolute value |αbg|. The horizontal axis of the graph represents the elevation position.

[0147] The complex reflection intensity distribution α shown in Figure 13 shows large values ​​at elevation positions sl1 (ground), sl2 (house), and sl3 (building). That is, the received signal at position x is a signal in which the complex reflection intensities at elevation positions sl1, sl2, and sl3 overlap. More precisely, the received signal at position x corresponds to the result of a Fourier transform of the complex reflection intensity distribution in the elevation direction.

[0148] When SAR tomography is used, a three-dimensional image including a three-dimensional complex reflection intensity distribution in the target region can be generated by combining the complex reflection intensities calculated for each pixel in this manner.

[0149] (Detailed Example of Reliable 3D Information) The reconstruction unit 206 generates reliable 3D information using SAR tomography or the like. The reliable 3D information may be either first reliable 3D information or second reliable 3D information, as exemplified below. Note that the reliable 3D information is not limited to these.

[0150] (Regarding the first reliable three-dimensional information) The first reliable three-dimensional information includes three-dimensional information including estimated values ​​of reflection intensity and phase at each azimuth, range, and elevation position, and estimated reliability information including an index value indicating the reliability of each estimated value. The first reliable three-dimensional information may further include at least one of temperature, displacement, etc.

[0151] (1) For example, the reconstruction unit 206 may obtain estimated reliability information by evaluating the difference between the received signal and the predicted signal predicted from the three-dimensional information for each of the reflection intensity and the phase.

[0152] There are various methods for assessing the difference.

[0153] For example, the discrepancy between the received signal and the predicted signal may be evaluated using the difference between the received signal and the predicted signal (e.g., the squared difference, the absolute difference, etc.). In this case, the estimated reliability information may include the difference between the received signal and the predicted signal.

[0154] For example, the difference between the received signal and the predicted signal may be evaluated using a function that adds a term expressing the complexity of the reconstructed three-dimensional information to the difference between the received signal and the predicted signal. In this case, the estimated reliability information may include a value (e.g., a value of a loss function in LASSO regression, Ridge regression, etc.) that uses a function that adds a term expressing the complexity of the reconstructed three-dimensional information to the difference between the received signal and the predicted signal.

[0155] An example of the loss function is given by the following equation (3).

[0156] ||g ｏｂｓ (x,n)-r(x,n)・α(x)|| ２ ２ +λ||α(x)|| p ...Formula (3)

[0157] In equation (3), ||| ｐ Is L ｐ represents the norm, and λ represents a regularization constant. In equation (3), the case where p = 1 is an example of a loss function in LASSO regression. In equation (3), the case where p = 2 is an example of a loss function in Ridge regression. Note that the loss function is not limited to this.

[0158] For example, the difference between the received signal and the predicted signal may be evaluated using cross-validation. Cross-validation is a method for evaluating the generalization performance of the reconstructed three-dimensional information by using a received signal used to evaluate the difference from the predicted value that is different from the received signal used to generate the three-dimensional information. In this case, the estimated reliability information may include a value indicating the generalization performance of the reconstructed three-dimensional information by using a received signal used to evaluate the difference from the predicted value that is different from the received signal used to generate the three-dimensional information.

[0159] In more detail, for example, the received signal different from the received signal used to generate the three-dimensional information is a received signal that has not been used to reconstruct the three-dimensional information. In this case, the value obtained by cross-validation is a value obtained by evaluating, for example, the difference between the predicted signal and the received signal for the received signal that has not been used to reconstruct the three-dimensional information.

[0160] More specifically, for example, the reconstruction unit 206 reconstructs the three-dimensional information using received signals (observation values) obtained from N-1 observations, excluding the m-th observation, among the 1st to Nth observations. In this case, the reconstruction unit 206 may obtain a value for evaluating reliability using the following equation (4) and generate estimated reliability information including this value. Note that the cross-validation method is not limited to this.

[0161] ||g ｏｂｓ (x,m)-r(x,m)・α(x)|| ２ ２ ...Formula (4)

[0162] In equation (4), ||| ２ Is L ２ Also, g ｏｂｓ (x, m) represents the received signal obtained in the mth observation, i.e., the received signal not used to reconstruct the 3D information, and r(x, m)·α(x) represents the predicted signal based on the 3D information reconstructed from N−1 observations excluding the mth observation.

[0163] (2) For example, the reconstruction unit 206 may obtain the estimation reliability information by evaluating the degree of likelihood of each estimated value in the three-dimensional information among possible values. This evaluation may be expressed using, for example, parameters (variance, confidence interval, etc.) of the posterior distribution of each estimated value obtained by Bayesian estimation, the shape of the posterior distribution, etc. In this case, the estimation reliability information may include a value indicating the degree of likelihood of each estimated value in the three-dimensional information among possible values.

[0164] (Regarding the Second Reliable 3D Information) The second reliable 3D information may include a function that represents the relationship between the reflection intensity and phase of each of the three-dimensional elements and their respective likelihoods. That is, the reconstruction unit 206 may obtain, as the reliable 3D information, a function that represents the relationship between the reflection intensity and phase of each of the three-dimensional elements and their respective likelihoods.

[0165] The function representing the relationship between the reflection intensity and the phase and their respective likelihoods may include, for example, at least one of a posterior distribution, a statistical index of the posterior distribution, and a candidate set of post-sampled three-dimensional information (estimated values ​​of reflection intensity and phase). The statistical index may be at least one of the mean, mode, variance, standard deviation, and confidence interval.

[0166] (Step S101 according to the second embodiment: Example of simulation information generation process) Referring again to Fig. 12, the simulated SAR image generation unit 101 generates simulation information (step S101).

[0167] In step S101 according to this embodiment, the simulated SAR image generating unit 101 generates simulated information using the reliable 3D information generated in step S201. For example, the simulated SAR image generating unit 101 obtains a steering vector from the measurement conditions of the target SAR image and generates simulated information from the reliable 3D information.

[0168] In detail, for example, when the first reliable three-dimensional information is generated in step S201, the simulated SAR image generation unit 101 may obtain predicted reliability information by statistical processing using the simulated SAR image and estimated reliability information.

[0169] When the second reliable three-dimensional information is generated in step S201, the simulated SAR image generation unit 101 may estimate a plurality of simulated complex signal candidates estimated from the reliable three-dimensional information and the likelihood of each of the plurality of simulated complex signal candidates.

[0170] The simulated SAR image generator 101 may then select or generate a plausible simulated complex signal as the reliability information. For example, assume that a function representing the relationship between the reflection intensity and phase of each three-dimensional element and their respective likelihoods is input to the simulated SAR image generator 101. In this case, the plausible simulated complex signal is, for example, a simulated complex signal that has the highest probability when a predicted signal in the simulated SAR image is estimated under the conditions of the input function.

[0171] Steps S102 to S104 may be the same as those described in the first embodiment.

[0172] By performing this signal processing, reliable 3D information can be generated using multiple observed SAR images, and the reliable 3D information can be used to generate simulated information, which can then be used to determine an evaluation index for the target SAR image.

[0173] (Operations and Effects) As described above, according to this embodiment, the signal processing device 200 includes the reconstruction unit 206 .

[0174] The reconstruction unit 206 generates reliable 3D information including 3D information and estimated reliability information using multiple observed SAR images. The 3D information includes estimated values ​​of reflection intensity and phase for each of the 3D elements that make up the target region. The estimated reliability information indicates the reliability of the estimated value for each of the 3D elements.

[0175] The simulated SAR image generator 101 generates simulated information using the reliable 3D information and the observation conditions under which actual measurements are observed (target SAR image). The target SAR image may be one or more. The target SAR image may include, for example, one or more appropriately designated observation SAR images stored in the SAR image storage unit 211, or may include a new observation SAR image not stored in the SAR image storage unit 211.

[0176] This allows reliable 3D information to be generated using multiple observed SAR images. Then, simulated information is generated using the reliable 3D information, and an evaluation index for the target SAR image can be calculated using the simulated information. Therefore, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0177] According to this embodiment, the reconstruction unit 206 obtains estimated reliability information by evaluating the difference between the received signal and the predicted signal predicted from the three-dimensional information for each of the reflection intensity and the phase.

[0178] This allows reliable 3D information to be generated using multiple observed SAR images. Therefore, as described above, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0179] According to this embodiment, the reconstruction unit 206 obtains estimation reliability information by evaluating how likely each estimated value in the three-dimensional information is among the possible values.

[0180] This allows reliable 3D information to be generated using multiple observed SAR images. Therefore, as described above, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0181] According to this embodiment, the simulated SAR image generating unit 101 obtains predicted reliability information by statistical processing using the simulated SAR image and estimated reliability information.

[0182] This allows the reliable 3D information to be used to generate simulated information including predicted reliability information, and the simulated information to be used to determine an evaluation index for the target SAR image. Therefore, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0183] According to this embodiment, the reconstruction unit 206 obtains, as reliable three-dimensional information, a function that represents the relationship between the reflection intensity and phase of each three-dimensional element and the likelihood of each.

[0184] This allows reliable 3D information to be generated using multiple observed SAR images. Therefore, as described above, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0185] According to this embodiment, the simulated SAR image generation unit 101 estimates a plurality of simulated complex signal candidates estimated from reliable three-dimensional information and the likelihood of each of the plurality of simulated complex signal candidates, and selects or generates a plausible simulated complex signal as reliability information.

[0186] This allows the reliable 3D information to be used to generate simulated information including predicted reliability information, and the simulated information to be used to determine an evaluation index for the target SAR image. Therefore, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0187] Third Embodiment In this embodiment, an example will be described in which the evaluation index is the posterior probability that an observation value is observed for each corresponding pixel in the simulated SAR image and the target SAR image.

[0188] In this embodiment, for the sake of simplicity, descriptions that overlap with those of the first embodiment will be omitted as appropriate.

[0189] The evaluation calculation unit 102 (see, for example, FIG. 3 ) according to this embodiment calculates an evaluation index for each corresponding pixel using the simulated information and the target SAR image, where each evaluation index is a posterior probability that an observation value is observed for each corresponding pixel in the simulated SAR image and the target SAR image.

[0190] (Step S102 according to embodiment 3: Example of evaluation index acquisition process) In step S102 according to embodiment 3 (see, for example, Figure 7), the evaluation calculation unit 102 uses the simulation information and the target SAR image to obtain an evaluation index (posterior probability) for each corresponding pixel (step S102).

[0191] In detail, for example, when the simulation information includes a predicted distribution, the evaluation calculation unit 102 may calculate the conditional probability that the observed value included in the target SAR image will be observed under measurement conditions corresponding to the predicted distribution as a posterior probability.

[0192] Furthermore, for example, when the simulation information includes a simulated SAR image and a statistical index (prediction reliability information) of the predicted distribution, the evaluation calculation unit 102 may calculate, as the posterior probability, a value obtained by normalizing the distance between the observed value and the predicted value on the complex plane by the standard deviation of the predicted distribution. The observed value corresponds to the pixel value of each pixel of the target SAR image. The predicted value corresponds to the pixel value of each pixel of the simulated SAR image. In this case, the predicted distribution may be assumed to be a circularly symmetric complex normal distribution.

[0193] Note that the method for calculating the posterior probability is not limited to these. In addition, although the present embodiment has been described with reference to FIGS. 3 and 7, the evaluation calculation unit 102 and the evaluation index acquisition process (step S102) according to the present embodiment may be applied to, for example, the second embodiment.

[0194] According to the present embodiment, each evaluation index is a posterior probability that the observed value is observed for each corresponding pixel in the simulated SAR image and the target SAR image. The evaluation calculation unit 102 calculates the evaluation index for each corresponding pixel using the simulated information and the target SAR image.

[0195] This allows the posterior probability to be calculated as an evaluation index for the target SAR image using the simulated SAR image and the predicted reliability information. Therefore, the target SAR image can be evaluated taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0196] Fourth Embodiment In this embodiment, an example will be described in which the evaluation index is the joint probability that an observation value is observed for each corresponding pixel in a simulated SAR image and multiple target SAR images. In this embodiment, there are multiple target SAR images.

[0197] In this embodiment, for the sake of simplicity, descriptions that overlap with those of the first embodiment will be omitted as appropriate.

[0198] The evaluation calculation unit 102 (see, for example, FIG. 3 ) according to this embodiment calculates a plurality of evaluation indices, each of which is a joint probability of observing an observation value for each corresponding pixel in the simulated SAR image and a plurality of target SAR images.

[0199] 14 is a diagram illustrating an example of the functional configuration of the evaluation calculation unit 102 according to embodiment 4. The evaluation calculation unit 102 according to this embodiment includes a posterior probability calculation unit 102a and a joint probability calculation unit 102b.

[0200] The posterior probability calculation unit 102a uses the simulation information and each of the plurality of target SAR images to calculate the posterior probability that an observation value will be observed for each corresponding pixel.

[0201] The joint probability calculation unit 102b combines the posterior probabilities calculated using each of the target SAR images for each corresponding pixel, thereby calculating an evaluation index for each corresponding pixel.

[0202] (Step S102 according to the fourth embodiment: Example of evaluation index acquisition process) In step S102 according to the fourth embodiment (see, for example, FIG. 7), the evaluation calculation unit 102 calculates, as an evaluation index, the joint probability that an observation value is observed for each corresponding pixel in the simulated SAR image and multiple target SAR images (step S102).

[0203] FIG. 15 is a flowchart showing a detailed example of the evaluation index acquisition process (step S102) according to the fourth embodiment.

[0204] The posterior probability calculation unit 102a repeats steps S402 and S403 for each of all target SAR images (step S401; loop A). ​​The posterior probability calculation unit 102a repeats step S403 for each corresponding pixel between the target SAR image and the simulated SAR image to be processed (step S402; loop B).

[0205] The posterior probability calculation unit 102a uses the simulation information and the target SAR image to be processed in loop A to calculate the posterior probability that an observation value corresponding to a pixel to be processed in loop B will be observed (step S403).

[0206] The observed values ​​correspond to pixel values ​​included in the target SAR image to be processed. The method for calculating the posterior probability in step S403 may be, for example, any of the methods used in step S102 of embodiment 3. Note that the method for calculating the posterior probability is not limited to the method described in embodiment 3.

[0207] By executing steps S401 to S403, the posterior probability calculation unit 102a can use the simulation information and each of the plurality of target SAR images to calculate the posterior probability that an observation value will be observed for each corresponding pixel.

[0208] The joint probability calculation unit 102b combines the posterior probabilities calculated using each of the target SAR images for each corresponding pixel, thereby calculating the joint probability for each corresponding pixel in the target SAR images as an evaluation index (step S404).

[0209] This joint probability is the probability that corresponding pixel-by-pixel observations will be observed simultaneously in multiple target SAR images under conditions in which simulated SAR images of the target region are obtained.

[0210] Although the present embodiment has been described with reference to FIGS. 3 and 7, the evaluation calculation unit 102 and the evaluation index acquisition process (step S102) according to the present embodiment may be applied to, for example, the second embodiment.

[0211] (Operations and Effects) As described above, according to this embodiment, each of the evaluation indices is the joint probability that an observation value is observed for each corresponding pixel in the simulated SAR image and a plurality of target SAR images.

[0212] The evaluation calculation unit 102 includes a posterior probability calculation unit 102 a and a joint probability calculation unit 102 b. The posterior probability calculation unit 102 a calculates the posterior probability that an observation value will be observed for each corresponding pixel using the simulation information and each of the multiple target SAR images. The joint probability calculation unit 102 b calculates an evaluation index for each corresponding pixel by combining the posterior probabilities calculated using each of the multiple target SAR images for each corresponding pixel.

[0213] This allows the joint probability as an evaluation index for multiple target SAR images to be calculated using the simulated SAR image and the predicted reliability information. Therefore, the multiple target SAR images can be evaluated more accurately by taking into account the reliability of the simulated SAR image. Therefore, it becomes possible to accurately detect changes in the target area using the SAR image.

[0214] Fifth Embodiment In this embodiment, an example will be described in which the evaluation index is the joint probability that an observation value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image.

[0215] In this embodiment, for the sake of simplicity, descriptions that overlap with those of the first embodiment will be omitted as appropriate.

[0216] The evaluation calculation unit 102 (see, for example, FIG. 3 ) according to this embodiment calculates a plurality of evaluation indices, each of which is a joint probability of observing an observation value for each corresponding pixel group in the simulated SAR image and the target SAR image.

[0217] 16 is a diagram illustrating an example of the functional configuration of the evaluation calculation unit 102 according to embodiment 5. The evaluation calculation unit 102 according to this embodiment includes a posterior probability calculation unit 102c and a joint probability calculation unit 102d.

[0218] The posterior probability calculation unit 102c uses the simulated information and the target SAR image to calculate the posterior probability that an observation value will be observed for each corresponding pixel in the simulated SAR image and the target SAR image.

[0219] The joint probability calculation unit 102d calculates an evaluation index for each corresponding pixel group between the simulated SAR image and the target SAR image by combining the posterior probabilities calculated for each pixel that constitutes the pixel group.

[0220] The corresponding pixel groups in the simulated SAR image and the target SAR image may be composed of a plurality of pixels within one or more predetermined ranges, for example, the predetermined ranges may be defined to include the entire simulated SAR image and the entire target SAR image without overlapping each other.

[0221] The predetermined ranges may partially overlap each other, or may not overlap each other at all. Furthermore, the predetermined range or ranges may be determined for a corresponding portion of the simulated SAR image and the target SAR image.

[0222] (Step S102 according to embodiment 5: Example of evaluation index acquisition process) In step S102 according to embodiment 5 (see, for example, Figure 7), the evaluation calculation unit 102 calculates the joint probability that an observation value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image as an evaluation index (step S102).

[0223] FIG. 17 is a flowchart showing a detailed example of the evaluation index acquisition process (step S102) according to the fifth embodiment.

[0224] The posterior probability calculation unit 102c repeats step S502 for each corresponding pixel between the simulated SAR image and the target SAR image included in the simulation information (step S501; loop C).

[0225] The posterior probability calculation unit 102c uses the simulation information and the target SAR image to calculate the posterior probability that an observation value corresponding to a pixel to be processed in loop C will be observed (step S502).

[0226] The observed values ​​correspond to pixel values ​​included in the target SAR image. The method for calculating the posterior probability in step S502 may be, for example, any of the methods used in step S102 of embodiment 3. Note that the method for calculating the posterior probability is not limited to the method described in embodiment 3.

[0227] By executing steps S501 to S502, the posterior probability calculation unit 102c can use the simulated information and the target SAR image to calculate the posterior probability that an observation value will be observed for each corresponding pixel in the simulated SAR image and the target SAR image.

[0228] The joint probability calculation unit 102d repeats step S504 for each corresponding pixel group between the simulated SAR image and the target SAR image (step S503; loop D).

[0229] The joint probability calculation unit 102d combines the posterior probabilities calculated in step S502 for each pixel constituting the pixel group to be processed, thereby calculating an evaluation index for the pixel group to be processed (step S504).

[0230] By executing steps S501 and S502, the joint probability calculation unit 102d can calculate the joint probability that observed values ​​are observed for each corresponding pixel group in the simulated SAR image and the target SAR image.

[0231] This joint probability is the probability that observed values ​​of a group of pixels included in a target SAR image are observed simultaneously under conditions under which a simulated SAR image of the target area is obtained.

[0232] Although the present embodiment has been described with reference to FIGS. 3 and 7, the evaluation calculation unit 102 and the evaluation index acquisition process (step S102) according to the present embodiment may be applied to, for example, the second embodiment.

[0233] (Operations and Effects) As described above, according to this embodiment, each of the evaluation indices is the joint probability that the observed value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image.

[0234] The evaluation calculation unit 102 includes a posterior probability calculation unit 102c and a joint probability calculation unit 102d. The posterior probability calculation unit 102c uses the simulation information and the target SAR image to calculate the posterior probability of obtaining an observation value for each corresponding pixel. The joint probability calculation unit 102d calculates an evaluation index for each corresponding pixel group by combining the posterior probabilities calculated for each pixel constituting the pixel group.

[0235] This allows the joint probability, which serves as an evaluation index for a group of pixels included in the target SAR image, to be calculated using the simulated SAR image and the predicted reliability information. Therefore, it is possible to evaluate multiple target SAR images with higher accuracy by taking into account the reliability of the simulated SAR image. Therefore, it is possible to accurately detect changes in the target area using the SAR image.

[0236] Although the embodiments and modifications of the present invention have been described above with reference to the drawings, these are merely examples of the present invention, and various other configurations can also be adopted.

[0237] In addition, although the flowcharts used in the above description show multiple steps (processes) in a sequential order, the order of steps performed in each embodiment is not limited to the order shown. In each embodiment, the order of steps shown in the drawings can be changed as long as it does not cause any problems in terms of content. Furthermore, the above-described embodiments and variations can be combined as long as the content is not contradictory.

[0238] Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0239] 1. A signal processing device comprising: a simulated SAR image generating means for generating simulated information including a simulated SAR image composed of predicted values ​​of each pixel predicted to be obtained when a target area in a steady state is observed, and predicted reliability information indicating the reliability of the predicted value of each pixel; and an evaluation calculation means for calculating an evaluation index for evaluating a target SAR image to be analyzed using the simulated information. 2. The signal processing device described in 1., in which each of the simulated SAR image and the target SAR image is a two-dimensional complex image. 3. The signal processing device described in 1. or 2., in which the predicted value of each pixel is a complex value including a reflection intensity component and a phase component predicted to be obtained when the target area in the steady state is observed under the same observation conditions as the target SAR image. 4. The signal processing device described in any one of 1. to 3., in which the predicted reliability information includes at least one of a predictive distribution indicating a probabilistic distribution of the predicted values, a statistical index of the predictive distribution, and a probability of the predicted value being the predicted value. 5. The signal processing device according to any one of 1. to 4., wherein the target SAR image is composed of observed values ​​of each pixel that actually observed the target region, and there are a plurality of evaluation indices, and the evaluation indices are indices that probabilistically evaluate the degree to which the observed values ​​that constitute the target SAR image are consistent with the predicted values ​​that constitute the simulated SAR image. 6. The signal processing device according to 5., wherein each of the evaluation indices is a posterior probability that the observed value is observed for each corresponding pixel in the simulated SAR image and the target SAR image, and the evaluation calculation means uses the simulated information and the target SAR image to determine the evaluation index for each corresponding pixel. 5. The signal processing device according to claim 5, wherein each of the evaluation indices is a joint probability that the observation value is observed for each corresponding pixel in the simulated SAR image and the plurality of target SAR images, and the evaluation calculation means includes: a posterior probability calculation means that uses the simulated information and each of the plurality of target SAR images to calculate a posterior probability that the observation value is observed for each corresponding pixel; and a joint probability calculation means that calculates the evaluation index for each corresponding pixel by combining the posterior probabilities calculated using each of the plurality of target SAR images for each corresponding pixel.8. The signal processing device according to 5., wherein each of the evaluation indices is a joint probability that the observation value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image, and the evaluation calculation means includes: a posterior probability calculation means that calculates a posterior probability that the observation value is obtained for each corresponding pixel using the simulated information and the target SAR image, and a joint probability calculation means that calculates the evaluation index for each corresponding pixel group by combining the posterior probabilities calculated for each pixel constituting the corresponding pixel group. 9. The signal processing device according to any one of 1. to 8., further comprising change detection means that detects a change in the target region using the evaluation indices. 10. The signal processing device according to any one of 1. to 9., further comprising a reconstruction means for generating, using a plurality of observed SAR images, reliable three-dimensional information including three-dimensional information including estimated values ​​of reflection intensity and phase for each of three-dimensional elements constituting the target region, and estimated reliability information indicating the reliability of the estimated values ​​of each of the three-dimensional elements, wherein the simulated SAR image generation means generates the simulated information using the reliable three-dimensional information and observation conditions when the actual measured values ​​are observed. 11. The signal processing device according to 10., wherein the reconstruction means obtains the estimated reliability information by evaluating, for each of reflection intensity and phase, a difference between a received signal and a predicted signal predicted from the three-dimensional information. 12. The signal processing device according to 10., wherein the reconstruction means obtains the estimated reliability information by evaluating how likely each estimated value in the three-dimensional information is among possible values. 13. The simulated SAR image generation means obtains the predicted reliability information by statistical processing using the simulated SAR image and the estimated reliability information. Or the signal processing device according to 12. 14. The signal processing device according to 10., wherein the reconstruction means obtains, as the reliable three-dimensional information, a function representing the relationship between the reflection intensity and phase for each of the three-dimensional elements and the likelihood of each.15. The signal processing device according to 14., wherein the simulated SAR image generating means estimates a plurality of simulated complex signal candidates estimated from the reliable three-dimensional information and the likelihood of each of the plurality of simulated complex signal candidates, and selects or generates a plausible simulated complex signal as the reliability information. 16. A signal processing system further comprising: simulated SAR image generation means for generating simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when a target area in a steady state is observed, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel; evaluation calculation means for using the simulated information to determine an evaluation index for evaluating a target SAR image that is the object of analysis; SAR image storage means for storing the observed SAR image of the target area composed of observed values ​​obtained by observing the target area using a synthetic aperture radar; and reconstruction means for using a plurality of the observed SAR images to generate reliable three-dimensional information including three-dimensional information including estimated values ​​of reflection intensity and phase for each of three-dimensional elements that make up the target area, and estimated reliability information indicating the reliability of the estimated values ​​of each three-dimensional element, wherein the simulated SAR image generation means generates the simulated information using the reliable three-dimensional information and observation conditions when the actual measured values ​​were observed. A signal processing method in which one or more computers generate simulated information including a simulated SAR image composed of predicted values ​​for each pixel predicted to be obtained when a target area is observed in a steady state, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel, and use the simulated information to determine an evaluation index for evaluating a target SAR image to be analyzed. 18. The signal processing method described in 17., in which each of the simulated SAR image and the target SAR image is a two-dimensional complex image. 19. The signal processing method described in 17. or 18., in which the predicted value for each pixel is a complex value including a reflection intensity component and a phase component predicted to be obtained when the target area in a steady state is observed under the same observation conditions as the target SAR image. 20. The signal processing method described in any one of 17. to 19., in which the predicted reliability information includes at least one of a predictive distribution indicating a probabilistic distribution of the predicted values, a statistical indicator of the predictive distribution, and a probability of the predicted value being the predicted value.21. The signal processing method according to any one of 17. to 20., wherein the target SAR image is composed of observed values ​​of each pixel of the target region that have actually been observed, and there are a plurality of evaluation indices, and the evaluation indices are indices that probabilistically evaluate the degree to which the observed values ​​that make up the target SAR image are consistent with the predicted values ​​that make up the simulated SAR image. 22. The signal processing method according to 21., wherein each of the evaluation indices is a posterior probability that the observed value will be observed for each corresponding pixel in the simulated SAR image and the target SAR image, and wherein calculating the evaluation indices involves using the simulated information and the target SAR image to calculate the evaluation indices for each corresponding pixel. 23. 21. The signal processing method according to 21., wherein each of the evaluation indices is a joint probability that the observation value will be observed for each corresponding pixel in the simulated SAR image and the plurality of target SAR images, and wherein calculating the evaluation index involves using the simulated information and each of the plurality of target SAR images to calculate a posterior probability that the observation value will be observed for each corresponding pixel, and combining the posterior probabilities calculated using each of the plurality of target SAR images for each corresponding pixel to calculate the evaluation index for each corresponding pixel. 24. The signal processing method according to 21., wherein each of the evaluation indices is a joint probability that the observation value will be observed for each corresponding pixel group in the simulated SAR image and the target SAR image, and wherein calculating the evaluation index involves using the simulated information and the target SAR image to calculate a posterior probability that the observation value will be obtained for each corresponding pixel, and combining the posterior probabilities calculated for each pixel constituting the pixel group to calculate the evaluation index for each corresponding pixel group. The signal processing method according to any one of 17. to 24., further comprising detecting a change in the target region using the evaluation index.26. The signal processing method according to any one of 17. to 25., further comprising using a plurality of observed SAR images to generate reliable 3D information including 3D information including estimated values ​​of reflection intensity and phase for each of 3D elements constituting the target region, and estimated reliability information indicating the reliability of the estimated values ​​of each of the 3D elements, wherein generating the simulated information involves generating the simulated information using the reliable 3D information and observation conditions under which the actual measured values ​​were observed. 27. The signal processing method according to 26., wherein generating the reliable 3D information involves determining the estimated reliability information by evaluating the difference between a received signal and a predicted signal predicted from the 3D information for each of reflection intensity and phase. 28. The signal processing method according to 26., wherein generating the reliable 3D information involves determining the estimated reliability information by evaluating the degree of likelihood of each estimated value in the 3D information among possible values. 29. The signal processing method according to 27. or 28., wherein generating the simulated information involves determining the predicted reliability information through statistical processing using the simulated SAR image and the estimated reliability information. 30. The signal processing method according to 26., wherein generating the reliable three-dimensional information involves determining, as the reliable three-dimensional information, a function expressing the relationship between the reflection intensity and phase for each of the three-dimensional elements and their respective likelihoods. 31. The signal processing method according to 30., wherein generating the simulated information involves estimating a plurality of simulated complex signal candidates estimated from the reliable three-dimensional information and the likelihoods of each of the plurality of simulated complex signal candidates, and selecting or generating a plausible simulated complex signal as the reliability information. 32. 33. A program for causing one or more computers to execute the following: generating simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when a target region in a steady state is observed, and predicted reliability information indicating the reliability of the predicted values ​​for each pixel; and using the simulated information to determine an evaluation index for evaluating the target SAR image that is the object of analysis. 32. The program described in 32, wherein each of the simulated SAR image and the target SAR image is a two-dimensional complex image.34. The program described in 32. or 33., wherein the predicted value of each pixel is a complex value including a reflection intensity component and a phase component predicted to be obtained when the target area in the steady state is observed under the same observation conditions as the target SAR image. 35. The program described in any one of 32. to 34., wherein the prediction reliability information includes at least one of a prediction distribution indicating a probabilistic distribution of the predicted values, a statistical index of the prediction distribution, and a probability of the predicted value. 36. The program described in any one of 32. to 35., wherein the target SAR image is composed of observed values ​​of each pixel obtained by actually observing the target area, and there are multiple evaluation indices, and the evaluation indices are indices that probabilistically evaluate the degree to which the observed values ​​constituting the target SAR image are consistent with the predicted values ​​constituting the simulated SAR image. 37. 36. The program according to claim 36, wherein each of the evaluation indices is a posterior probability that the observation value will be observed for each corresponding pixel in the simulated SAR image and the target SAR image, and determining the evaluation indices involves using the simulated information and the target SAR image to determine the evaluation indices for each corresponding pixel. 38. The program according to claim 36, wherein each of the evaluation indices is a joint probability that the observation value will be observed for each corresponding pixel in the simulated SAR image and multiple target SAR images, and determining the evaluation indices involves using the simulated information and each of the multiple target SAR images to determine the posterior probability that the observation value will be observed for each corresponding pixel, and combining the posterior probabilities determined using each of the multiple target SAR images for each corresponding pixel to determine the evaluation indices for each corresponding pixel. 39. 36. The program described in Item 36, wherein each of the evaluation indices is a joint probability that the observation value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image, and the evaluation index is calculated by using the simulated information and the target SAR image to calculate a posterior probability that the observation value is obtained for each corresponding pixel, and by combining the posterior probabilities calculated for each pixel constituting each corresponding pixel group to calculate the evaluation index for each corresponding pixel group.40. The program according to any one of items 32 to 39, further causing the program to detect changes in the target region using the evaluation index. 41. The program according to any one of items 32 to 40, further causing the program to generate, using a plurality of observed SAR images, reliable three-dimensional information including three-dimensional information including estimated values ​​of reflection intensity and phase for each of three-dimensional elements constituting the target region, and estimated reliability information indicating the reliability of the estimated values ​​of each of the three-dimensional elements, wherein generating the simulated information involves generating the simulated information using the reliable three-dimensional information and observation conditions when the actual measured values ​​are observed. 42. The program according to item 41, further causing the program to obtain the estimated reliability information by evaluating the difference between a received signal and a predicted signal predicted from the three-dimensional information for each of reflection intensity and phase. 43. The program according to item 41, further causing the program to obtain the estimated reliability information by evaluating the degree of likelihood of each estimated value in the three-dimensional information among possible values. 44. The program described in 42. or 43., wherein generating the simulated information involves determining the predicted reliability information through statistical processing using the simulated SAR image and the estimated reliability information. 45. The program described in 41., wherein generating the reliable three-dimensional information involves determining, as the reliable three-dimensional information, a function representing the relationship between the reflection intensity and phase of each of the three-dimensional elements and their respective likelihoods. 46. The program described in 45., wherein generating the simulated information involves estimating a plurality of simulated complex signal candidates estimated from the reliable three-dimensional information and the likelihoods of each of the plurality of simulated complex signal candidates, and selecting or generating a plausible simulated complex signal as the reliability information. 47. A recording medium having recorded thereon the program described in any one of 32 to 46.

[0240] This application claims priority based on Japanese Patent Application No. 2023-001674, filed on January 10, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0241] 100, 200 Signal processing device 101 Simulated SAR image generation unit 102 Evaluation calculation unit 102a, 102c Posterior probability calculation unit 102b, 102d Joint probability calculation unit 103 Change detection unit 104 Display unit 105 Display control unit 206 Reconstruction unit 210 Information processing device 211 SAR image storage unit

Claims

1. a simulated SAR image generating means for generating simulated information including a simulated SAR image composed of predicted values ​​of each pixel that are predicted to be obtained when observing a target region in a steady state, and prediction reliability information indicating the reliability of the predicted values ​​of each pixel; and an evaluation calculation means for calculating an evaluation index for evaluating a target SAR image to be analyzed using the simulation information. Signal processing device.

2. the predicted value of each pixel is a complex value including a reflection intensity component and a phase component predicted to be obtained when the target area in the steady state is observed under the same observation conditions as those of the target SAR image, The prediction reliability information includes at least one of a prediction distribution indicating a probabilistic distribution of the prediction value, a statistical index of the prediction distribution, and a probability of the prediction value. The signal processing device according to claim 1 .

3. the target SAR image is composed of observation values ​​of each pixel that actually observes the target region; The evaluation indexes are plural, The evaluation index is an index that probabilistically evaluates the degree to which the observed values ​​constituting the target SAR image are consistent with the predicted values ​​constituting the simulated SAR image.

3. The signal processing device according to claim 1 or 2.

4. each of the evaluation indexes is a posterior probability that the observation value is observed for each corresponding pixel in the simulated SAR image and the target SAR image; The evaluation calculation means calculates the evaluation index for each of the corresponding pixels using the simulation information and the target SAR image. The signal processing device according to claim 3 .

5. each of the evaluation indexes is a joint probability that the observed value is observed for each corresponding pixel in the simulated SAR image and the plurality of target SAR images; The evaluation calculation means a posterior probability calculation means for calculating a posterior probability that the observed value will be observed for each of the corresponding pixels using the simulation information and each of the plurality of target SAR images; and a joint probability calculation means for calculating the evaluation index for each of the corresponding pixels by combining the posterior probabilities calculated using each of the plurality of target SAR images for each of the corresponding pixels. The signal processing device according to claim 3 .

6. each of the evaluation indices is a joint probability that the observed value is observed for each corresponding pixel group in the simulated SAR image and the target SAR image; The evaluation calculation means a posterior probability calculation means for calculating a posterior probability that the observed value is obtained for each corresponding pixel using the simulation information and the target SAR image; and a joint probability calculation means for calculating the evaluation index for each of the corresponding pixel groups by combining the posterior probabilities calculated for each pixel constituting the pixel group. The signal processing device according to claim 3 .

7. The method further includes a change detection means for detecting a change in the target region using the evaluation index.

3. The signal processing device according to claim 1 or 2.

8. a reconstruction means for generating, using a plurality of observed SAR images, reliable three-dimensional information including three-dimensional information including estimated values ​​of reflection intensity and phase for each of three-dimensional elements constituting the target region, and estimated reliability information indicating the reliability of the estimated values ​​of each of the three-dimensional elements; The simulated SAR image generating means generates the simulated information using the reliable three-dimensional information and observation conditions when the actual measurement values ​​are observed.

3. The signal processing device according to claim 1 or 2.

9. One or more computers generating simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when observing a target region in a steady state, and prediction reliability information indicating the reliability of the predicted values ​​for each pixel; Using the simulated information, an evaluation index is obtained for evaluating the target SAR image to be analyzed. Signal processing methods.

10. On one or more computers, generating simulated information including a simulated SAR image composed of predicted values ​​for each pixel that are predicted to be obtained when observing a target region in a steady state, and prediction reliability information indicating the reliability of the predicted values ​​for each pixel; A program for executing the step of determining an evaluation index for evaluating a target SAR image to be analyzed using the simulation information.