A SAR image interpretation method, device, equipment and storage medium

Abstract

The application discloses a SAR image interpretation method, device and equipment and a storage medium, and relates to the technical field of image data processing. The method comprises the following steps: acquiring a SAR image of a disaster area and imaging information of the SAR image, wherein the imaging information at least indicates imaging time, data resolution, ascending / descending track, side-looking direction and incident angle; processing the SAR image by using an image processing software to obtain a corrected image of the SAR image; interpreting the corrected image according to the imaging information based on image features associated with image reference features in the corrected image, wherein the image reference features indicate common image features in the SAR image which are convenient for interpretation; and identifying the SAR image by using the interpretation result to draw a plot image, wherein the plot image indicates a disaster situation of the disaster area, and the disaster situation at least comprises multiple information such as a specific position of the disaster area, a disaster range, a disaster facility position and size, and a surrounding mountain, river and road position. The embodiment of the application can reduce the interpretation difficulty of the SAR image.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of image data processing technology, and particularly to a SAR image interpretation method, apparatus, device, and computer-readable storage medium. Background Technology

[0002] SAR (Synthetic Aperture Radar) is an active remote sensing technology that primarily uses microwaves for imaging. It acquires information about the Earth's surface by emitting microwave signals and receiving their reflected signals. Due to its characteristic of actively emitting electromagnetic energy, it is highly sensitive to the physical properties and geometry of the Earth's surface and can be widely used in geological monitoring, especially for monitoring geological hazards.

[0003] In areas that have experienced geological disasters, continuous rainy weather may occur, preventing optical satellites from imaging. SAR is then the only reliable satellite data source. However, because SAR images are obtained through coherent accumulation of complex electromagnetic scattering, they contain intricate interactions between electromagnetic waves and the environment and targets, making SAR image interpretation challenging. The accurate interpretation of SAR images will significantly impact disaster monitoring, damage assessment, post-disaster relief, and reconstruction.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] In a first aspect of this disclosure, a SAR image interpretation method is provided. The method includes: acquiring a SAR image of a disaster-stricken area and its imaging information, the imaging information indicating at least imaging time, data resolution, ascent / descent trajectory, side-view direction, and incident angle; processing the SAR image using image processing software to obtain a corrected image of the SAR image; interpreting the corrected image based on image features associated with image reference features in the corrected image, according to the imaging information, to obtain an interpretation result, the image reference features indicating commonly used image features in the SAR image that are easy to interpret; and using the interpretation result to label the SAR image to draw a plotted image, the plotted image indicating the disaster situation in the disaster-stricken area, the disaster situation including at least the specific location of the disaster-stricken area, the disaster area's extent, the location and size of disaster-stricken facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads.

[0006] In a second aspect of this disclosure, an apparatus for interpreting SAR images is provided. The apparatus includes: an acquisition module configured to acquire SAR images of a disaster-stricken area and imaging information of the SAR images, the imaging information indicating at least imaging time, data resolution, ascent / descent trajectory, side-view direction, and incident angle; a processing module configured to process the SAR images using image processing software to obtain a corrected image of the SAR images; an interpretation module configured to interpret the corrected image based on image features associated with image reference features in the corrected image, according to the imaging information, to obtain an interpretation result, the image reference features indicating commonly used image features in the SAR images that are easy to interpret; and a plotting module configured to label the SAR images using the interpretation result to create a plotted image, the plotted image indicating the disaster situation in the disaster-stricken area, the disaster situation including at least the specific location of the disaster-stricken area, the disaster extent, the location and size of disaster-stricken facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads.

[0007] In a third aspect of this disclosure, a computing device is provided. The device includes at least one processing unit and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0008] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0009] It should be understood that the content described in this content section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram is shown of an example environment in which embodiments of the present disclosure may be implemented;

[0012] Figure 2 A flowchart illustrating an example interpretation process for SAR images according to some embodiments of the present disclosure is shown;

[0013] Figure 3 A flowchart illustrating an example interpretation process of SAR images according to some embodiments of this disclosure is shown;

[0014] Figure 4 A schematic diagram of an example structure of perspective contraction according to some embodiments of the present disclosure is shown;

[0015] Figure 5 A schematic diagram of an example structure resulting from overlay according to some embodiments of the present disclosure is shown;

[0016] Figure 6 A schematic diagram of SAR images according to some embodiments of the present disclosure is shown;

[0017] Figure 7 A schematic diagram illustrating the interpretation results according to some embodiments of the present disclosure is shown;

[0018] Figure 8 A schematic structural block diagram of an example apparatus for SAR image interpretation according to some embodiments of the present disclosure is shown; and

[0019] Figure 9 A block diagram of a computing device capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0020] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0021] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0022] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0023] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0024] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information other than that necessary for basic functions will not affect the user's use of basic functions.

[0025] As mentioned above, some areas that have experienced geological disasters may experience continuous rainy weather, preventing optical satellites from imaging. SAR is then the only reliable satellite data source. However, because SAR images are obtained through coherent accumulation of complex electromagnetic scattering, they contain complex interactions between electromagnetic waves and the environment and targets, making SAR image interpretation quite challenging. The accurate interpretation of SAR images will significantly impact disaster monitoring, damage assessment, post-disaster relief, and reconstruction.

[0026] This disclosure proposes a SAR image interpretation method. The method includes: acquiring SAR images of a disaster-stricken area and their imaging information, the imaging information indicating at least imaging time, data resolution, ascent / descent trajectory, side-view direction, and incident angle; processing the SAR images using image processing software to obtain a corrected SAR image; interpreting the corrected image based on image features associated with image reference features in the corrected image, according to the imaging information, to obtain an interpretation result, the image reference features indicating commonly used image features in the SAR image that are easy to interpret; and using the interpretation result to label the SAR image to create a plotted image, the plotted image indicating the disaster situation in the disaster-stricken area, the disaster situation including at least the specific location of the disaster-stricken area, the disaster area's extent, the location and size of damaged facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads.

[0027] In this way, embodiments of this disclosure interpret the corrected image by identifying image features that reflect facilities such as buildings and roads in the corrected image and combining them with imaging information. This method transforms SAR images, which are difficult to interpret, into easily quantifiable features, reducing the difficulty of SAR image interpretation. Furthermore, it allows the obtained interpretation results to include accurate information.

[0028] The following section provides a detailed description of various example implementations of this scheme, with reference to the accompanying drawings.

[0029] Example Environment

[0030] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, example environment 100 may include electronic device 110 and image acquisition device 120.

[0031] In this example environment 100, the image acquisition device 120 can be used to acquire SAR remote sensing images. As an example, the image acquisition device 120 can be a SAR satellite.

[0032] In some embodiments, electronic device 110 communicates with image acquisition device 120. Electronic device 110 can be any type of mobile terminal, fixed terminal, or portable terminal, including mobile phones, desktop computers, laptop computers, notebook computers, netbook computers, tablet computers, media computers, multimedia tablets, handheld computers, portable gaming terminals, VR / AR devices, personal communication system (PCS) devices, personal navigation devices, personal digital assistants (PDAs), audio / video players, digital cameras / camcorders, positioning devices, television receivers, radio receivers, e-book devices, gaming devices, or any combination thereof, including accessories and peripherals of these devices or any combination thereof. In some embodiments, electronic device 110 can also support any type of user-facing interface (such as "wearable" circuitry).

[0033] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0034] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure.

[0035] Example process

[0036] The following combination Figure 2 and Figure 3 This describes the specific interpretation process of SAR images. Figure 2 A flowchart of an example interpretation process 200 for SAR images according to some embodiments of the present disclosure is shown. Figure 3 A flowchart illustrating an example interpretation process 300 for SAR images according to some embodiments of the present disclosure is shown. Process 300 can be implemented at electronic device 110. Reference is made below. Figure 1 and Figure 2 To describe process 300.

[0037] like Figure 3 As shown in block 310, electronic device 110 acquires SAR images of the disaster-stricken area and SAR image imaging information. In some embodiments, the imaging information at least indicates the imaging time, data resolution, ascent / descent trajectory, side-view direction, and incident angle. The data resolution can be the image resolution or the resolution of other devices, etc.

[0038] In some embodiments, when a geological disaster occurs in a region, the affected area can be the area where the geological disaster occurred, or it can be the area where the geological disaster occurred and the area of ​​human activity. As an example, for earthquake disasters, since the epicenter may not necessarily be damaged, it is necessary to infer the affected area of ​​the earthquake based on the latitude and longitude of the epicenter, the direction of seismic wave movement, and the distribution of cities, villages and roads around the epicenter.

[0039] In some embodiments, the electronic device 110 can determine the affected area of ​​a geological disaster through open-source intelligence information. For example, the approximate area of ​​the geological disaster can be determined first by using a web search engine. This method can narrow down the scope to the town or village level. Then, by using open-source media intelligence such as text, images, and videos, and other software such as Google Earth, the specific location of the disaster can be further determined, thereby determining the vector of the affected area.

[0040] In some embodiments, when acquiring SAR images of a disaster-stricken area, the electronic device 110 can acquire SAR images through the image acquisition device 120. As an example, the image acquisition device 120 may be a SAR satellite.

[0041] In some embodiments, the process by which electronic device 110 acquires SAR images of the disaster-stricken area using image acquisition device 120 can be: controlling SAR satellite imaging through programming. Specifically, electronic device 110 can query satellite transit time and orbit information, and control the SAR satellite to adopt a spotlight imaging mode. Acquiring SAR images in this way by electronic device 110 has the following advantages: when a geological disaster occurs in a mountainous area, by controlling the movement of the SAR satellite, the SAR images acquired by the SAR satellite can avoid phenomena such as perspective distortion, overlay, and shadows caused by mountains, thereby reducing the difficulty of interpreting SAR images. At the same time, electronic device 110 can also control the SAR satellite to adjust its left and right side views according to its ascent and descent orbit information so that radar waves can illuminate the disaster-stricken area, thereby acquiring high-quality SAR remote sensing images.

[0042] In addition, in some other embodiments, SAR images of the disaster-stricken area can also be obtained by purchasing remote sensing data from commercial SAR satellites. As an example, one can choose to purchase sub-meter resolution post-disaster high-resolution SAR remote sensing images, or one can choose to purchase sub-meter resolution post-disaster high-resolution SAR remote sensing images along with pre-disaster historical SAR images.

[0043] In box 320, electronic device 110 uses image processing software to process SAR images to obtain corrected images of SAR images.

[0044] In some embodiments, the processing of SAR images by the electronic device 110 may include a variety of different processing steps, such as data import, multi-view processing, filtering, geocoding, etc. Each processing step is described below with a specific example.

[0045] In a specific example, when electronic device 110 acquires SAR imagery, it first performs data import. Data import involves importing the raw binary data files (typically in CEOS, SAFE, raw, etc.) directly recorded by the satellite into the ENVI software environment. Since different satellites have different data formats and parameters, configuration is required based on the satellite type to convert the raw binary data files into image files. As an example, when configuring based on the satellite type, polarization mode and / or imaging mode can be configured. Polarization mode specifies the polarization state of radar wave transmission and reception (e.g., HH - horizontal transmit, horizontal receive; HV - horizontal transmit, vertical receive; VV - vertical transmit, vertical receive; VH - vertical transmit, horizontal receive). Imaging mode specifies the SAR imaging method (e.g., Stripmap mode, Potlight mode, ScanSAR mode, etc.).

[0046] Subsequently, electronic device 110 performs multi-view processing on the SAR image. Multi-view processing involves averaging multiple adjacent pixels in both the azimuth (along the flight direction) and range (perpendicular to the flight direction, i.e., the radar wave emission direction) directions of the imported image file. It is understood that SAR images contain speckle noise, which is caused by the coherence characteristics of radar waves and appears as randomly distributed bright and dark spots on the image. Multi-view processing utilizes the randomness of speckle noise; by averaging multiple pixels, some random noise can be canceled, thereby improving image quality and facilitating subsequent interpretation. As an example, this step can be implemented in ENVI software.

[0047] Furthermore, the electronic device 110 performs filtering processing on the SAR image. Filtering processing involves applying specific image filtering algorithms to the image after multi-view processing. For example, Lee filtering can be used. Lee filtering is primarily based on the statistical characteristics (mainly mean and variance) of local regions of the image (usually a small window, such as 5x5 or 7x7). Specifically, if the value of the pixel at the center of the window is considered a "uniform" region locally (small variance), the filtering tends to smooth it (take the mean) to suppress noise. If the pixel value is considered an "edge" or "strong target" locally (large variance), the filtering will retain the original value of the pixel or make minor modifications. That is, Lee filtering can effectively reduce noise while preserving ground boundaries, linear features (roads, rivers), and important texture information (such as forest canopies and the texture of urban building complexes), thereby obtaining SAR images with higher visual quality and easier information extraction for subsequent interpretation.

[0048] Finally, electronic device 110 geocodes the SAR image. Geocoding converts the SAR image, which is located in a slant-range coordinate system, into an image in a standard geographic coordinate system (such as WGS84). Specifically, since the original SAR image coordinates are based on the slant-range distance from the radar antenna to the target, geocoding first converts them to horizontal distances on the ground, and then converts the horizontal distance coordinate system to latitude and longitude coordinates (such as WGS84) or projected coordinates (such as UTM). Simultaneously, by combining it with DEM data, terrain distortion can be eliminated to form an accurate SAR image for subsequent interpretation. As an example, this processing step can be implemented using ENVI software.

[0049] However, it's worth noting that when SAR imagery includes mountains, interference caused by terrain slope and radar incidence angle is common. This primarily applies to SAR satellite images captured during geological disasters such as landslides or debris flows. This interference increases the difficulty of interpreting SAR images; therefore, SAR imagery correction is necessary to obtain corrected images.

[0050] In some embodiments, the electronic device 110 processes SAR images using image processing software to obtain a corrected SAR image, which can be achieved through the following process:

[0051] First, in response to a disaster type of landslide or debris flow, electronic device 110 determines imaging features contained in the SAR image. In some embodiments, the imaging features indicate interference caused by terrain slope and radar incident angle. As examples, imaging features may include perspective contraction, overlay inversion, and shadows. Perspective contraction means that the slope facing the radar is compressed; overlay inversion means that the mountain peak or high ground appears closer to the radar in the image than its actual location; shadows mean that the slope facing away from the radar has no signal at all.

[0052] In some embodiments, since different imaging features are corrected in different ways, the electronic device 110 can identify the imaging features contained in the SAR image by recognizing the SAR image processed by the image processing software, so as to correct the SAR image.

[0053] Then, electronic equipment 110 constructs a calibration model corresponding to the imaging features. It is understood that different imaging features require different correction methods, therefore different calibration models must be constructed for different imaging features. Of course, the calibration model applicable to each disaster-stricken area is also different. The following section uses different imaging features as examples to explain in detail the construction process of the corresponding calibration models.

[0054] In some embodiments, the specific process by which the electronic device 110 constructs a calibration model corresponding to the imaging features may include: in response to the imaging features being perspective-contracted, establishing a first calibration model, the first calibration model indicating the relationship between a first slant range and a first ground distance in the contracted area, the first calibration model including the radar platform height and a first elevation of the ground target relative to a reference surface; determining a first reference ground distance based on a first preset value of the first elevation according to the first calibration model; and correcting the first preset value based on the difference between the first reference ground distance and the first ground distance, so that the relationship between the first slant range and the first ground distance satisfies the error condition, thereby obtaining the calibration model.

[0055] like Figure 4 As shown, it can be understood that the principle of perspective contraction is that when the radar beam reaches the top, middle, and bottom of the slope at slopes R, respectively... T R M and R N Because of R T >R M >R NIn SAR imagery, the radar beam first illuminates the bottom of the slope and then the top, resulting in an image of the bottom before the top. The slope range is displayed as a distance ΔR1 on the image. ΔR1 is clearly less than the slope length L, thus significantly shortening the perceived length of the slope. In other words, in geological disaster scenarios such as landslides or debris flows, the landslide area in SAR images will be smaller than the actual landslide area. Furthermore, the degree of perspective contraction is directly related to the terrain slope (θ) and the radar incident angle (α) (α is the angle between the radar beam and the ground normal). For the uphill slope (slope facing the radar), the smaller θ (gentler slope) the weaker the contraction; the larger θ (steeper slope) the more significant the contraction (the contraction reaches its maximum when θ approaches α).

[0056] Based on this, a first calibration model can be established, which indicates the relationship between the first slope distance R and the first ground distance G in the contraction area. In some embodiments, the relationship between the first slope distance R and the first ground distance G can be expressed by the following formula:

[0057]

[0058] Where H is the radar platform height, α is the radar incident angle, and h is the first elevation of the ground target relative to the reference surface.

[0059] It is understandable that the first elevation h in the above formula is not a specific numerical value. Therefore, when determining the relationship between the first slope distance R and the first ground distance G, a first preset value for the first elevation can be determined first, and then this preset value can be substituted into the above formula to determine the first reference ground distance. To determine the true value of the first elevation, the first reference ground distance and the first ground distance G need to be infinitely close. Therefore, when there is a difference between the first reference ground distance and the first ground distance G, the first preset value can be adjusted based on this difference. The steps of substituting the first preset value into the relationship between the first slope distance R and the first ground distance G, and adjusting the first preset value based on the difference between the first reference ground distance and the first ground distance G, can be repeated multiple times to derive a definite relationship between the first slope distance R and the first ground distance G.

[0060] In some embodiments, the number of times a step is repeated can be configured, or it can be determined by pre-setting a condition for stopping iteration. As an example, the condition for stopping iteration can be an error condition, such as the mapping error between the first slope distance R and the first ground distance G being less than 0.5 pixels. When the mapping error between the first slope distance R and the first ground distance G is less than 0.5 pixels, the error condition is met, and no further iteration is needed; the first calibration model at this point is the required calibration model. Conversely, when the mapping error between the first slope distance R and the first ground distance G is not less than 0.5 pixels, the error condition is not met, and iteration needs to continue to reduce the mapping error between the first slope distance R and the first ground distance G.

[0061] In some embodiments, the specific process of the electronic device 110 constructing a calibration model corresponding to the imaging features may include: in response to the imaging features being overlayed, establishing a second calibration model, the second calibration model indicating the relationship between a second slant distance and a second ground distance in the overlay region, the second calibration model including a second elevation of the ground target relative to a reference surface; determining a second reference ground distance based on a second preset value of the second elevation according to the second calibration model; and correcting the second preset value based on the difference between the second reference ground distance and the second ground distance to obtain the calibration model.

[0062] like Figure 5 As shown, it can be understood that the principle of overlay inversion is that SAR images will exhibit overlay when depicting steep slopes with varying local terrain angles. The overlay at the top of a mountain has a closer slant distance than at the base, resulting in severe image distortion. When R... T <R M <R N In this scenario, the image of the mountaintop appears before the image of the base. The radar beam illuminates the ground at a certain angle of depression (α). When the terrain slope (θ) is greater than the radar depression angle (θ>α), nearby high points will be illuminated by the radar beam before distant low points, causing the echo signal of the high point to overlay the echo signal of the low point in the image, resulting in overlay. The slope range of this slope is shown as distance ΔR2 in the image. ΔR2 is obviously greater than the slope length L, therefore, the echo of the top of the slope appears before the bottom. This phenomenon makes the terrain features in the image appear to bend upwards. In other words, in geological disaster scenarios such as landslides or debris flows, the landslide area in the SAR image will be larger than the actual landslide area. Moreover, the degree of overlay is affected by many factors, including the radar incident angle, the slope and direction of the terrain, and the radar wavelength. The larger the incident angle or the steeper the terrain, the more severe the overlay phenomenon.

[0063] Based on this, a second calibration model can be established, which indicates the relationship between the second slope distance R and the second ground distance R0 of the overlapping area. In some embodiments, the relationship between the second slope distance R and the second ground distance R0 can be expressed by the following formula:

[0064]

[0065] Where α is the radar depression angle, R0 is the slant range of the radar to the ground reference point, and H is the second elevation of the ground target relative to the reference surface.

[0066] It is understandable that the second elevation H is not a specific numerical value in the above formula. Therefore, when determining the relationship between the second slope distance R and the second ground distance R0, a second preset value for the second elevation can be determined first, and then substituted into the above formula to determine the second reference ground distance. To determine the true value of the second elevation, the second reference ground distance needs to be infinitely close to the second ground distance R0. Therefore, when there is a difference between the second reference ground distance and the second ground distance R0, the second preset value can be adjusted based on this difference. The steps of substituting the second preset value into the relationship between the second slope distance R and the second ground distance R0, and adjusting the second preset value based on the difference between the second reference ground distance and the second ground distance R0, can be repeated multiple times to derive a definite relationship between the second slope distance R and the second ground distance R0.

[0067] In some embodiments, the number of times a step is repeated can be configured, or it can be determined by pre-setting a condition for stopping iteration. As an example, the condition for stopping iteration can be an error condition, such as the mapping error between the second slope distance R and the second ground distance R0 being less than 0.5 pixels. When the mapping error between the second slope distance R and the second ground distance R0 is less than 0.5 pixels, the error condition is met, and no further iteration is needed; the second calibration model at this point is the required calibration model. Conversely, when the mapping error between the second slope distance R and the second ground distance R0 is not less than 0.5 pixels, the error condition is not met, and iteration needs to continue to reduce the mapping error between the second slope distance R and the second ground distance R0.

[0068] It is understood that since geological disasters involving mountains, such as landslides or mudslides, typically only affect one side of a mountain, the presence of shadows in SAR images indicates that the SAR satellite is capturing images of the other side of the mountain. In some embodiments, to avoid shadows, the SAR satellite can be moved to a position where radar waves can reach the affected area when acquiring SAR images, thus reducing the difficulty of interpretation.

[0069] like Figure 1 and Figure 2 As shown, finally, the electronic device 110 uses the calibration model to correct the image processed by the image processing software to obtain a corrected image.

[0070] In box 330, electronic device 110 interprets the corrected image based on image features associated with image reference features in the corrected image, according to imaging information, to obtain the interpretation result. In some embodiments, the image reference features indicate commonly used image features in the SAR image that are easy to interpret. These commonly used image features are some features summarized based on the SAR imaging principle. The SAR imaging principle, that is, using a side-looking imaging mode (non-vertical ground observation), relies on the transmission, reflection, and reception of radio waves and signal processing technology. It mainly utilizes the principle of synthetic aperture in space, through multiple focal reflections at the corresponding spatial location, to finally form a radar image with resolution.

[0071] In some embodiments, the image reference features include at least a first reference tone feature and a reference shadow feature indicating a building, and a second reference tone feature indicating a road. In some other embodiments, the image reference features may also include features indicating the size, shape, tone, location, activity, shadow, etc., of other facilities. The following detailed explanation uses buildings and roads as examples, combined with the principles of SAR imaging.

[0072] For buildings, the tonal characteristics of buildings in SAR imagery will exhibit obvious straight or L-shaped bright lines because building roofs are usually relatively smooth. When radar waves hit the roof, specular reflection occurs, so the roof generally appears dark. However, the walls and ground of a building form a dihedral structure, which has a focusing effect on electromagnetic signals, resulting in secondary scattering. This appears as a highly bright linear structure in SAR imagery, generally a straight line or L-shaped broken line. This straight or L-shaped broken line bright line structure is the first reference tonal characteristic of the building. The shadow characteristics of buildings are also prominent in SAR imagery. Radar electromagnetic waves propagate in a straight line. Due to the blockage of building walls and roofs, the side facing away from the radar cannot be illuminated by electromagnetic waves, and therefore cannot receive the echo at the corresponding location, forming a shadow area. For buildings damaged after geological disasters, due to collapsed roofs, collapsed walls, and some even being razed to the ground, becoming ruins, damaged buildings will exhibit irregular and noisy diffuse bright areas compared to normal buildings. For some areas where buildings were razed to the ground, the scattered signals from the entire collapsed area were unobstructed, so the signal strength received by satellites in that area tended to be uniform. Furthermore, the rubble piled up on the ground after the buildings collapsed had a very rough and irregular surface, generating relatively strong reflected signals, which appeared bright on the radar. These shadow characteristics are the reference shadow characteristics of the buildings.

[0073] For roads, the tonal characteristics of roads in SAR imagery appear distinctly dark. This is because the road surface is relatively smooth, similar to a specular reflection; most electromagnetic waves are reflected parallel to the ground, with only a small amount of energy returning to the SAR sensor, resulting in low echo intensity. Therefore, roads typically appear as darker areas in SAR imagery. The shape characteristics of roads in SAR imagery also exhibit clear linear features, as the length of the road is much greater than its width, making its linearity obvious. For roads damaged by geological disasters, SAR imagery shows a disruption of linear continuity, with obvious gaps or interruptions. Furthermore, the damaged road surface becomes rough, resulting in significant changes in tonal characteristics in the SAR imagery. These tonal characteristics constitute the second reference tonal characteristics of roads.

[0074] In some embodiments, the interpretation of SAR impacts can utilize not only the features described above but also imaging information. For example, imaging time can be specified down to the minute level to accurately determine the extent of damage at the specific imaging moment; data resolution needs to be specified as 1 meter, 0.5 meters, or other resolutions to facilitate the measurement of the length of damaged facilities in the image.

[0075] In some embodiments, the interpretation results may include at least the damaged length and the damaged area. By accurately calculating the damaged length and area of ​​buildings, roads, or other facilities, it is easier for people to further understand and assess the disaster situation in the affected area.

[0076] In some embodiments, for buildings, the specific process by which the electronic device 110 interprets the corrected image based on imaging information according to the image features associated with image reference features in the corrected image to obtain the interpretation result may include:

[0077] First, the electronic device 110 identifies strong line feature pixels in the image features and determines the damaged length of the building based on the strong line feature pixels and the image resolution in the imaging information. Specifically, the image features in the corrected image that are associated with the image reference features are those that are close to the image reference features. These features can be used to determine the damaged area of ​​the building, road, or other equipment.

[0078] In some embodiments, when the SAR satellite's flight trajectory aligns with that of a building, the two are prone to angular reflection, resulting in strong line features. In this case, the damaged length of the building can be determined based on these strong line features. For example, the damaged length of the building = strong line feature pixels × image resolution.

[0079] Then, electronic device 110 uses radar cross section measurement technology to identify the number of first pixels in the damaged area of ​​the building, and determines the damaged area of ​​the building based on the number of first pixels and the image resolution. As an example, the damaged area of ​​the building = number of first pixels × image resolution × image resolution.

[0080] In some embodiments, for a road, the electronic device 110 interprets the corrected image based on image features associated with image reference features in the corrected image, and the specific process of obtaining the interpretation result may include:

[0081] First, the electronic device 110 identifies linear target feature pixels in the image features and determines the damaged length of the road based on the linear target feature pixels and the image resolution in the imaging information.

[0082] In some embodiments, linear target feature pixels are linear features with continuous gaps or breaks, which can reflect damaged road sections. The damaged length of the road can be determined based on these linear target features. As an example, the damaged length of the road = linear target feature pixels × image resolution.

[0083] Then, the electronic device 110 uses radar cross section measurement technology to identify the number of second pixels in the damaged area of ​​the road, and determines the damaged area of ​​the road based on the number of second pixels and the image resolution. For example, the damaged area of ​​the road = number of second pixels × image resolution × image resolution.

[0084] In some embodiments, for cases involving rising and falling rails and side views, the SAR image can be interpreted by first rotating its angle. For example, during interpretation, the light source (radar beam) can be positioned from the front (upper), meaning the target shadows in the image are facing the viewer (shadows facing downwards). This provides the optimal interpretation perspective and makes it easier to extract information from the disaster-affected images.

[0085] In some embodiments, multiple data source images can be used to assist in interpretation. For example, based on high-resolution Google images of the geological disaster-stricken area, features can be identified, and the number, outline, and spatial distribution of buildings, road lengths, directions, and spatial distribution, as well as information on rivers and mountains in the disaster area can be obtained. This allows for a true reconstruction of the pre-disaster features in the disaster-stricken area and assists in post-disaster SAR image recognition.

[0086] In box 340, electronic device 110 uses the interpretation results to label the SAR image to create a plotted image. In some embodiments, the plotted image indicates the disaster situation in the affected area, which includes at least the specific location of the affected area, the extent of the disaster, the location and size of the affected facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads. As an example, professional plotting software (such as Photoshop) can be used to create thematic maps on satellite remote sensing images of geological disaster-affected areas by labeling points, lines, areas, and other symbols, as well as text information, based on the interpretation results. The plotted image can fully demonstrate the overall situation of the affected area. Specifically, it can be as follows: Figure 6 As shown.

[0087] In some embodiments, once the mapping image is completed, the disaster area and spatial distribution can be statistically analyzed using software such as ArcGIS and ENVI based on the interpretation results and the mapping image. Specifically, this includes the number and extent of damaged buildings, the length, accessibility, and repair difficulty of damaged roads, and the overall disaster area range and area, in order to generate a report.

[0088] Figure 7 A schematic diagram illustrating the interpretation results according to some embodiments of this disclosure is shown. The following is in conjunction with... Figure 7 To describe a specific interpretation process.

[0089] First, using network information technology, we obtained the specific disaster location information of the landslide area in Yibin, Sichuan. We then used Google Earth software to generate vector data. Since no available satellite data was available when the geological disaster occurred, we used the AS-01 SAR satellite for emergency programming imaging. We used the vector geographic coordinate information to query the AS-01 satellite's transit time and imaging parameters. We then used an appropriate incident angle and the side-view direction of the disaster area towards the radar wave to conduct observation and imaging, and obtained the best post-disaster high-resolution SAR image.

[0090] Then, the acquired post-disaster SAR images were preprocessed using ENVI software. The steps included data import, multi-view processing, filtering, and geocoding. The raw SAR data was imported into ENVI, and parameters (such as polarization and imaging mode) were set according to the sensor type, resulting in an output image file. The imported and processed file underwent multi-view processing. Azimuth and range averaging was used to reduce image speckle noise and improve the signal-to-noise ratio. The Lee filtering method was then applied to filter the multi-view processed image, suppressing speckle noise and preserving texture information. Finally, the filtered image was converted from slant range coordinates to geographic coordinates in ENVI, and topographic distortion was eliminated by combining it with DEM data to generate an orthorectified image.

[0091] For SAR image data of the landslide area in Yibin, Sichuan, the imaging mode was 0.5-meter spotting mode, using a right-side-view imaging method with a descending orbit and an incident angle of 39.7 degrees. During interpretation, the image needed to be rotated clockwise by approximately 45 degrees (radar wave direction upwards, shadow direction downwards). The interpretation confirmed that the affected area was the area of ​​building and road damage caused by the landslide. In the SAR remote sensing image, the surface features of the landslide area showed significant changes: the surface tended to be smooth after the landslide, appearing as a dark tone in the image; the debris deposits formed by the landslide made the surface texture tend to be uniform, appearing as smooth areas in the image, and the boundaries of the landslide body were clearly distinguishable, while the boundaries of undamaged roads and the outlines of houses remained intact. Regarding the interpretation characteristics of buildings, normal buildings in SAR images typically exhibit strong reflective characteristics: the roof produces specular reflection due to its smooth surface, and the dihedral structure formed by the walls and the ground produces a focusing effect on electromagnetic signals and forms secondary scattering, thus appearing as a high-brightness linear structure (a straight line or L-shaped broken line) in the image. Damaged buildings, due to collapsed roofs and walls (some even leveled to the ground and becoming ruins), exhibit reflective characteristics that, compared to normal buildings, transform into irregular, diffuse bright areas (resembling noise characteristics). The collapsed areas of leveled buildings possess dual reflective characteristics: on the one hand, the scattered signals from the collapsed areas are unobstructed, resulting in a more uniform signal intensity received by satellites; on the other hand, the accumulated rubble significantly increases surface roughness and its irregular distribution, generating strong reflective signals that appear bright in radar images. The interpretation characteristics of roads also show significant differences: normal roads appear as long, linear features in SAR images, with relatively flat surfaces, uniform echo intensity, and no obvious textural variations or speckled distribution; while the boundary between damaged and normal road sections is clearly discernible, and due to the disruption of the road's linear continuity, its radar wave reflection exhibits chaotic and irregular characteristics.

[0092] Because the disaster-stricken area is located in a mountainous region with dramatic terrain undulations and complex slope variations, perspective contraction and overlay inversion phenomena are particularly pronounced in SAR images. The steep slopes exhibit significant perspective contraction due to the slant range compression effect of radar side-view imaging, causing spatial scale distortion of the terrain in the images. Meanwhile, some steep slopes near the radar side experience overlay inversion due to the leading superposition of echo signals, resulting in a reversed mapping of terrain elevation in the images. These geometric distortions directly affect the accurate identification of damaged targets and the measurement of geometric parameters. Before conducting quantitative analysis, the images are preprocessed based on a DEM correction model. An elevation remapping algorithm for overlay areas is used to correct the inverted terrain relationships, and an RD transformation model is combined to eliminate scale bias caused by perspective contraction, ensuring the geometric accuracy of subsequent measurement data.

[0093] Finally, based on the interpretation results of the post-disaster SAR images and analysis using ArcGIS software, it was shown that the landslide in Yibin, Sichuan, generally slid in a northwest-southeast direction. Influenced by the topography, the landslide body turned northeast after sliding into the valley, ultimately forming a "V"-shaped landslide area. Based on the RCS reflection characteristics of the damaged targets and the SAR image resolution parameters, it was precisely calculated that the landslide caused damage to approximately 280 meters of road, destroyed a total of 10 houses (with a total damaged area of ​​approximately 3000 square meters), and formed a debris accumulation approximately 1.2 kilometers long and 100 meters wide. Combining the above interpretation and analysis results, relevant text annotations and symbol plotting were completed on the images, ultimately creating a post-disaster thematic map, providing intuitive spatial information support for disaster assessment and subsequent response.

[0094] In summary, the embodiments of this disclosure interpret the corrected image by identifying image features that reflect facilities such as buildings and roads, and combining these features with imaging information. This method transforms SAR images, which are difficult to interpret, into easily quantifiable features, thus reducing the difficulty of SAR image interpretation.

[0095] Example devices and equipment

[0096] Embodiments of this disclosure also provide corresponding apparatus for implementing the above methods or processes. Figure 8 A schematic structural block diagram of an example apparatus 800 for SAR image interpretation according to certain embodiments of the present disclosure is shown. Apparatus 800 may be implemented as or included in electronic device 110. Various modules / components in apparatus 800 may be implemented by hardware, software, firmware, or any combination thereof.

[0097] like Figure 8 As shown, the device 800 includes: an acquisition module 810 configured to acquire SAR images of the disaster-stricken area and imaging information of the SAR images, wherein the imaging information indicates at least the imaging time, data resolution, ascent and descent rail, side-view direction, and incident angle; a processing module 820 configured to process the SAR images using image processing software to obtain a corrected image of the SAR images; an interpretation module 830 configured to interpret the corrected image based on image features associated with image reference features in the corrected image, according to the imaging information, to obtain an interpretation result, wherein the image reference features indicate commonly used image features in the SAR images that are easy to interpret; and a drawing module 840 configured to mark the SAR images using the interpretation result to draw a plotted image, wherein the plotted image indicates the disaster situation of the disaster-stricken area, and the disaster situation includes at least the specific location of the disaster-stricken area, the disaster range, the location and size of the disaster-stricken facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads.

[0098] In some embodiments, the image reference features include at least a first reference tone feature and a reference shadow feature indicating a building, and a second reference tone feature indicating a road.

[0099] In some embodiments, the interpretation result includes at least the damaged length and the damaged area. The corrected image includes a building. Based on image features associated with image reference features in the corrected image, the corrected image is interpreted according to imaging information to obtain the interpretation result, which includes: identifying strong line feature pixels in the image features; determining the damaged length of the building based on the strong line feature pixels and the image resolution in the imaging information; identifying the first number of pixels of the damaged area of ​​the building using radar cross section measurement technology; and determining the damaged area of ​​the building based on the first number of pixels and the image resolution.

[0100] In some embodiments, the corrected image includes a road. Based on image features associated with image reference features in the corrected image, the corrected image is interpreted according to imaging information to obtain the interpretation result, which includes: identifying linear target feature pixels in the image features; determining the damaged length of the road based on the linear target feature pixels and the image resolution in the imaging information; identifying the number of second pixels of the damaged area of ​​the road using radar cross section measurement technology; and determining the damaged area of ​​the road based on the number of second pixels and the image resolution.

[0101] In some embodiments, processing SAR images with image processing software to obtain a corrected SAR image includes: in response to a disaster type of landslide or debris flow, determining imaging features contained in the SAR image, the imaging features indicating interference caused by terrain slope and incident angle; constructing a calibration model corresponding to the imaging features; and using the calibration model to correct the image processed by the image processing software to obtain a corrected image.

[0102] In some embodiments, constructing a calibration model corresponding to the imaging features includes: in response to the imaging features being perspective shrinkage, establishing a first calibration model, the first calibration model indicating the relationship between a first slant range and a first ground distance in the shrinkage region, the first calibration model including the radar platform height and a first elevation of the ground target relative to a reference surface; determining a first reference ground distance based on a first preset value of the first elevation according to the first calibration model; and correcting the first preset value based on the difference between the first reference ground distance and the first ground distance, such that the relationship between the first slant range and the first ground distance satisfies an error condition, thereby obtaining the calibration model.

[0103] In some embodiments, constructing a calibration model corresponding to an imaging feature includes: in response to an imaging feature being an overlay inversion, establishing a second calibration model, the second calibration model indicating the relationship between a second slant distance and a second ground distance in the overlay region, the second calibration model including a second elevation of the ground target relative to a reference surface; determining a second reference ground distance based on a second preset value of the second elevation according to the second calibration model; and correcting the second preset value based on the difference between the second reference ground distance and the second ground distance to obtain the calibration model.

[0104] like Figure 9 As shown, the computing device 900 is in the form of a general-purpose electronic device. Components of the computing device 900 may include, but are not limited to, one or more processors or processing units 910, memory 920, storage devices 930, one or more communication units 940, one or more input devices 950, and one or more output devices 960. The processing unit 910 may be a physical or virtual processor and is capable of performing various processes according to programs stored in the memory 920. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of the computing device 900.

[0105] Computing device 900 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to computing device 900, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 920 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 930 can be removable or non-removable media and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data and can be accessed within computing device 900.

[0106] The computing device 900 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 9 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 920 may include computer program product 925 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0107] The communication unit 940 enables communication with other electronic devices via a communication medium. Additionally, the components of the computing device 900 can function as a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, the computing device 900 can operate in a networked environment using logical connections to one or more other servers, networked personal computers (PCs), or another network node.

[0108] Input device 950 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 960 can be one or more output devices, such as a monitor, speaker, printer, etc. Computing device 900 can also communicate as needed with one or more external devices (not shown) via communication unit 940. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with computing device 900, or with any device that enables computing device 900 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interfaces (not shown).

[0109] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0110] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0111] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0112] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0114] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

[0115] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A SAR image interpretation method, characterized in that, The method includes: Acquire SAR images of the disaster-stricken area and imaging information of the SAR images, wherein the imaging information indicates at least the imaging time, data resolution, ascent and descent orbit, side-view direction, and incident angle; The SAR image is processed using image processing software to obtain a corrected image of the SAR image; Based on image features associated with image reference features in the corrected image, the corrected image is interpreted according to the imaging information to obtain an interpretation result. The image reference features indicate commonly used image features in the SAR image that are easy to interpret. The SAR image is labeled using the interpretation results to create a plotted image. The plotted image indicates the disaster situation in the disaster-stricken area. The disaster situation includes at least the specific location of the disaster-stricken area, the scope of the disaster, the location and size of the disaster-stricken facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads. The process of processing the SAR image using image processing software to obtain a corrected image of the SAR image includes: In response to a disaster type of landslide or debris flow, imaging features contained in the SAR image are determined, the imaging features indicating interference caused by terrain slope and incident angle; Construct a calibration model corresponding to the imaging features; and The image processed by the image processing software is corrected using the calibration model to obtain the corrected image; The construction of the calibration model corresponding to the imaging features includes: In response to the imaging feature being perspective shrinkage, a first calibration model is established. The first calibration model indicates the relationship between a first slant range and a first ground distance in the shrinkage region. The first calibration model includes the radar platform height and the first elevation of the ground target relative to the reference plane. Based on a first preset value of the first elevation, a first reference ground distance is determined according to the first calibration model; and Based on the difference between the first reference distance and the first distance, the first preset value is corrected so that the relationship between the first slope distance and the first distance satisfies the error condition, so as to obtain the calibration model; And / or, constructing a calibration model corresponding to the imaging features includes: In response to the imaging feature being overlayed, a second calibration model is established, the second calibration model indicating the relationship between the second slant range and the second ground distance of the overlay region, the second calibration model including the second elevation of the ground target relative to the reference surface; Based on the second preset value of the second elevation, the second reference ground distance is determined according to the second calibration model; and Based on the difference between the second reference distance and the second distance, the second preset value is corrected to obtain the calibration model.

2. The method according to claim 1, characterized in that, The image reference features include at least a first reference tone feature and a reference shadow feature indicating a building, and a second reference tone feature indicating a road.

3. The method according to claim 2, characterized in that, The interpretation result includes at least the damaged length and damaged area. The corrected image includes buildings. The interpretation result obtained by interpreting the corrected image based on the image features associated with image reference features in the corrected image and according to the imaging information includes: Identify strong line feature pixels in the image features; The damaged length of the building is determined based on the strong line feature pixels and the image resolution in the imaging information; The number of first pixels used to identify the damaged area of ​​the building using radar cross section measurement technology; and The damaged area of ​​the building is determined based on the first number of pixels and the image resolution.

4. The method according to claim 2, characterized in that, The corrected image includes roads, and the interpretation of the corrected image based on image features associated with image reference features in the corrected image, according to the imaging information, yields the following interpretation results: Identify linear target feature pixels in the image features; The damaged length of the road is determined based on the linear target feature pixels and the image resolution in the imaging information; The number of second pixels used to identify the damaged area of ​​the road using radar cross section measurement technology; and The damaged area of ​​the road is determined based on the second number of pixels and the image resolution.

5. An apparatus for interpreting SAR images, used to perform the method according to any one of claims 1 to 4, characterized in that, include: The acquisition module is configured to acquire SAR images of the disaster-stricken area and imaging information of the SAR images, wherein the imaging information indicates at least the imaging time, data resolution, ascent and descent rail, side-view direction, and incident angle. The processing module is configured to process the SAR image using image processing software to obtain a corrected image of the SAR image; The interpretation module is configured to interpret the correction image based on the imaging information according to the image features associated with the image reference features in the correction image, and obtain the interpretation result, wherein the image reference features indicate commonly used image features in the SAR image that are easy to interpret; as well as The drawing module is configured to use the interpretation results to identify the SAR image to draw a plotted image, which indicates the disaster situation of the disaster area. The disaster situation includes at least the specific location of the disaster area, the scope of the disaster, the location and size of the disaster facilities, and multiple other information such as the location of surrounding mountains, rivers, and roads.

6. A computing device, comprising: At least one processing unit; as well as At least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the computing device to perform the method according to any one of claims 1 to 4 when executed by the at least one processing unit.

7. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 4.

Images