Vehicle trace characterization method and device using zero baseline airborne heavy rail interferometric SAR

Abstract

The application relates to a zero baseline airborne heavy rail interference SAR vehicle trace characterization method and device, two complex images are obtained by adopting a BP imaging method and imaging two groups of original echo data of the ground before and after a vehicle driven by a zero baseline airborne heavy rail detection vehicle, the two complex images are aligned based on a sub-pixel offset, interference processing is performed on the two aligned complex images to obtain an interference image, an interference phase containing vehicle trace information is extracted from the interference image, a wrapped flat ground phase is estimated according to the interference phase under unknown baseline error, after unwrapping the flat ground phase, the interference phase is indirectly unwrapped according to the phase ambiguity number of the wrapped flat ground phase and the unwrapped flat ground phase, the flat ground phase in the unwrapped interference phase is eliminated, and a deformation phase generated only by the vehicle trace is obtained, the deformation phase is filtered to obtain a SAR image of vehicle trace characterization. The method can effectively reduce false alarms and improve the clarity of vehicle trace characterization.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of radar signal processing, in particular to a vehicle trace characterization method and device of zero baseline airborne heavy rail interferometric SAR. BACKGROUND

[0002] Synthetic Aperture Radar (SAR) is the core representative of active microwave remote sensing technology. In recent years, it has become a research hotspot in the field of radar due to its three core advantages of all-weather, all-day and high resolution. Unlike optical remote sensing which relies on natural light, SAR can image by actively emitting microwave signals and receiving ground echo. Even in severe environments such as heavy rain, thick fog and night, it can still stably obtain ground information. This feature makes it play a key role in many fields: in topographic mapping, it can generate digital elevation models with precision up to meters or even sub-meters, providing accurate data for highway alignment and water conservancy planning; in post-disaster rescue, it can quickly identify collapsed areas and flood inundation areas after an earthquake, helping rescue teams to deploy forces efficiently.

[0003] Airborne heavy rail SAR is an important application form of SAR technology. Its core design is to let the radar-carrying aircraft fly along a fixed route (i.e., "heavy rail") to observe the key area twice in a short time. The interval between the two observations is usually several days to several weeks. It can characterize the changes in the ground surface by comparing the scattering intensity of the ground object obtained from the two observations. For example, if there is a significant change in the area, such as new building construction or large-scale vegetation withering, the microwave scattering characteristics of the ground object will change significantly, and the scattering intensity difference will be highlighted, so it can be accurately captured.

[0004] However, when monitoring vehicle driving traces, traditional methods encounter bottlenecks. The traces left by vehicles driving are mostly subtle changes in the ground surface, such as tire pressure marks on the road surface and driving track marks in open areas. Such changes do not change the core scattering properties of the ground object, such as material and structure, so they almost do not cause significant differences in scattering intensity, resulting in the complete failure of traditional characterization methods based on scattering intensity. SUMMARY

[0005] Therefore, it is necessary to provide a vehicle trace characterization method and device of zero baseline airborne heavy rail interferometric SAR that can reduce false alarms.

[0006] A vehicle trace characterization method of zero baseline airborne heavy rail interferometric SAR, the method comprising:

[0007] Obtaining two groups of original echo data of the ground surface before and after the vehicle driving, the original echo data being detected by zero baseline airborne heavy rail SAR;

[0008] The two sets of original echo data are imaged by using a BP imaging method to obtain two SAR complex images of the ground before and after the vehicle travels, and the two SAR complex images are aligned based on a sub-pixel offset therebetween;

[0009] The two aligned SAR complex images are interferometrically processed to obtain an interference image, and an interference phase containing vehicle trace information is extracted from the interference image;

[0010] A wrapped flat ground phase under unknown baseline error is estimated according to the interference phase, the flat ground phase is unwrapped, and the interference phase is indirectly unwrapped according to a phase ambiguity number of the wrapped flat ground phase and the unwrapped flat ground phase, the flat ground phase in the unwrapped interference phase is eliminated, and a deformation phase generated only by the vehicle trace is obtained;

[0011] The deformation phase is filtered to obtain a SAR image representing the vehicle trace.

[0012] In one embodiment, the two sets of original echo data are obtained by a zero-baseline airborne repeat-pass SAR system performing first and second SAR detections on an observation area before and after a vehicle travels, and during the two SAR detections, an aircraft of the SAR system flies along completely coincident tracks, and radar beam pointing of the SAR system remains consistent.

[0013] In one embodiment, the alignment based on the sub-pixel offset between the two SAR complex images comprises:

[0014] Based on amplitude information, a Lucas optical flow registration is used to estimate the sub-pixel offset between the two SAR complex images generated by unknown baseline deviation.

[0015] Based on the sub-pixel offset, the two SAR complex images are aligned by using bidirectional interpolation.

[0016] In one embodiment, the wrapped flat ground phase under unknown baseline error is estimated according to the interference phase by using Goldstein adaptive filtering.

[0017] In one embodiment, the indirect unwrapping of the interference phase according to the phase ambiguity number of the wrapped flat ground phase and the unwrapped flat ground phase is represented as:

[0018]

[0019] In the above formula, represents a phase ambiguity number obtained according to the unwrapped flat ground phase, represents the wrapped flat ground phase.

[0020] In one embodiment, the deformation phase generated only by the vehicle trace is obtained by eliminating the flat phase in the unwrapped interference phase, and is expressed as:

[0021]

[0022] In the above formula, and respectively represent the indirectly unwrapped interference phase and the flat phase, represents the deformation phase.

[0023] In one embodiment, the SAR image representing the vehicle trace is obtained by performing salt and pepper filtering on the deformation phase.

[0024] The application further provides a device for vehicle trace representation of zero-baseline airborne heavy-rail interferometric SAR, and the device comprises:

[0025] an original echo data acquisition module, configured to acquire two groups of original echo data of the ground before and after the vehicle driving, wherein the original echo data is detected by zero-baseline airborne heavy-rail SAR;

[0026] an image registration module, configured to perform imaging on the two groups of original echo data by using a BP imaging method, to obtain two SAR complex images of the ground before and after the vehicle driving, and to align the two SAR complex images based on a sub-pixel offset therebetween;

[0027] an interference phase extraction module, configured to perform interference processing on the two aligned SAR complex images to obtain an interference image, and to extract an interference phase containing vehicle trace information from the interference image;

[0028] a deformation phase acquisition module, configured to estimate a wrapped flat phase under unknown baseline error based on the interference phase, to perform unwrapping on the flat phase, to indirectly unwrap the interference phase based on a phase ambiguity number of the wrapped flat phase and the unwrapped flat phase, and to obtain a deformation phase generated only by the vehicle trace by eliminating the flat phase in the unwrapped interference phase;

[0029] a vehicle trace representation module, configured to perform filtering on the deformation phase to obtain a SAR image representing the vehicle trace.

[0030] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the specific steps of the vehicle trace representation method of zero-baseline airborne heavy-rail interferometric SAR when executing the computer program.

[0031] A computer readable storage medium, having stored thereon a computer program, which, when executed by a processor, implements the specific steps of the vehicle trace characterization method of zero baseline airborne heavy-rail interferometric SAR described above.

[0032] The vehicle trace characterization method and device of zero baseline airborne heavy-rail interferometric SAR described above, by adopting the BP imaging method, images two groups of original echo data of the ground before and after the vehicle driving detected by the zero baseline airborne heavy-rail SAR to obtain two SAR complex images of the ground before and after the vehicle driving, aligns based on the sub-pixel offset between the two SAR complex images, performs interference processing on the two aligned SAR complex images to obtain an interference image, extracts the interference phase containing the vehicle trace information from the interference image, estimates the unwrapped flat ground phase under the unknown baseline error according to the interference phase, after unwrapping the flat ground phase, indirectly unwraps the interference phase according to the phase ambiguity number of the wrapped flat ground phase and the unwrapped flat ground phase, obtains the deformation phase generated only by the vehicle trace by eliminating the flat ground phase in the unwrapped interference phase, and filters the deformation phase to obtain the SAR image of the vehicle trace characterization. The method can effectively reduce false alarms and improve the clarity of vehicle trace characterization. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 It is a flowchart of the vehicle trace characterization method of zero baseline airborne heavy-rail interferometric SAR in one embodiment;

[0034] Figure 2 It is a geometric model diagram of the vehicle trace characterization based on zero baseline airborne heavy-rail interferometric SAR in one embodiment;

[0035] Figure 3 It is an optical image of the experimental scene in one experiment;

[0036] Figure 4 It is the primary and secondary SAR amplitude images before registration in one experiment, wherein Figure 4 (a) represents the primary image, Figure 4 (b) represents the secondary image;

[0037] Figure 5 It is the primary and secondary SAR amplitude images after registration in one experiment, wherein Figure 5 (a) represents the primary image, Figure 5 (b) represents the secondary image;

[0038] Figure 6 It is a diagram of the interference phase and the estimated flat ground phase in one experiment, wherein Figure 6 (a) represents the interference phase image, Figure 6 (b) represents the flat ground phase image;

[0039] Figure 7 is a figure of the estimation of deformation phase in an experiment, wherein Figure 7 (a) represents the unwrapped interference phase map, Figure 7 (b) represents the unwrapped flat phase map, Figure 7 (c) represents the deformation phase map with noise;

[0040] Figure 8 is a figure of the representation of vehicle trace obtained by the method in an experiment, wherein Figure 8 (a) represents the vehicle trace representation image obtained by phase filtering, Figure 8 (b) represents the vehicle trace representation image obtained by traditional coherence coefficient method;

[0041] Figure 9 is a structure block diagram of vehicle trace representation device of zero baseline airborne repeat-pass interferometric SAR in an embodiment;

[0042] Figure 10 is an internal structure diagram of computer device in an embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0044] In view of the existing vehicle trace representation method, the false alarm problem caused by the weak view angle difference generated by the zero baseline error, as shown in Figure 1 , a vehicle trace representation method of zero baseline airborne repeat-pass interferometric SAR capable of reducing false alarm is proposed, which specifically includes the following steps:

[0045] Step S100, two groups of original echo data of the ground before and after the vehicle driving are obtained, and the original echo data are obtained by zero baseline airborne repeat-pass SAR detection.

[0046] Step S110, a BP imaging method is used to image the two groups of original echo data, to obtain two SAR complex images of the ground before and after the vehicle driving, and the two SAR complex images are aligned based on the sub-pixel offset therebetween.

[0047] Step S120, interference processing is performed on the two aligned SAR complex images to obtain an interference image, and an interference phase containing vehicle trace information is extracted from the interference image.

[0048] Step S130: Based on the unknown baseline error of the interference phase estimation, the entangled flat-ground phase is unentangled. Then, the interference phase is indirectly unentangled based on the phase ambiguity number of the entangled flat-ground phase and the unentangled flat-ground phase. By eliminating the flat-ground phase in the unentangled interference phase, the deformation phase generated only by vehicle traces is obtained.

[0049] Step S140: Filter the deformation phase to obtain a SAR image representing vehicle traces.

[0050] In this application, to obtain richer rut trace features, the SAR deformation phase is obtained by utilizing the interferometry of zero-baseline airborne heavy-rail SAR based on the slight deformation of the ground after vehicle movement, thereby accurately representing the trace area. SAR trace features based on deformation phase supplement existing coherence-based SAR trace features, improving the robustness of SAR vehicle trace detection.

[0051] In step S100, in the synthetic aperture radar (SAR) observation scenario of vehicle travel tracks, a first SAR detection is performed on the observation area before the vehicle travels, using zero-baseline airborne heavy-track SAR to obtain the first set of raw echo data of the ground in that area. A second SAR detection is performed on the same observation area after the vehicle travels, to obtain the second set of raw echo data of the ground in that area. During these two SAR detections, the aircraft carrying the zero-baseline airborne heavy-track SAR system flies along completely overlapping tracks, and the radar beam pointing of the SAR system remains consistent.

[0052] Specifically, when acquiring the two sets of raw echo data, a zero-baseline heavy-orbit interferometry flight was implemented by a human-machine interface. Simultaneously, a high-sensitivity servo controller was used to correct beam pointing offsets caused by aircraft attitude in real time, ensuring consistent radar beam pointing.

[0053] In step S110, a high-phase-preserving BP imaging algorithm is used to perform raw echo imaging. In BP imaging, the echo after quadrature demodulation is first subjected to range compression to obtain the following pulse-compressed echo signal:

[0054] (1)

[0055] In formula (1), Represents the target's position vector and pixel points correspond, This represents the antenna's position vector. This indicates the instantaneous slant range between the antenna and the target.

[0056] After obtaining all the echoes corresponding to the pixel points in the grid in the two-dimensional time domain after the pulse compression in the range direction, the echoes are compensated for the residual phase, and the antenna position vector coherent superposition is obtained, to obtain the final imaging result, which can be represented as:

[0057] (2)

[0058] From formula (2), it can be seen that the imaging geometry of the BP algorithm is wide, and is suitable for imaging of non-linear trajectories.

[0059] Specifically, the BP imaging algorithm is used to image the original echo data, and the complex value of the pixel point in the SAR complex image can be represented as:

[0060] (3)

[0061] In formula (3), represents the scattering intensity of the imaging grid point at two times, represents the signal bandwidth, represents the signal wavelength, represents the speed of light, represents the fast time, represents the slow time, represents the azimuth envelope of the imaging result, represents the slant range of the imaging grid point.

[0062] In this embodiment, the alignment is performed based on the sub-pixel offset between two SAR complex images, including: based on the amplitude information, the Lucas optical flow registration is used to estimate the sub-pixel offset between the two SAR complex images caused by the unknown baseline deviation, and then based on the sub-pixel offset, the two SAR complex images are aligned by using bidirectional interpolation.

[0063] Specifically, the offset is calculated according to the registration method, and the main and auxiliary SAR complex images are aligned by using the interpolation method. First, the Lucas optical flow registration is used to estimate the offset, and then the bidirectional interpolation is used to align the main and auxiliary SAR complex images (wherein the SAR complex image corresponding to the vehicle driving before is the main SAR complex image, and the SAR complex image corresponding to the vehicle driving after is the auxiliary SAR complex image). Wherein, the Lucas optical flow registration obtains the image constraint equation based on the brightness constancy and time continuity assumption, which is represented as:

[0064] (4)

[0065] In formula (4), represents the brightness of the image at time. Using Taylor formula, it can be approximately represented as:​

[0066] (5)

[0067] i.e.

[0068] (6)

[0069] Then, according to the spatial consistency, it is assumed that the optical flow is a constant value within a window, and the time increment , the following equation set can be obtained:

[0070] (7)

[0071] Specifically, the sub-pixel registration offset between the primary and secondary imaging results is obtained by using the SAR amplitude image registration method described above , and the relationship can be expressed as:

[0072] (8)

[0073] In formula (8), represents the primary amplitude image, represents the secondary amplitude image, represents the modulo operation, represents the different slant ranges of the imaging grid points due to the baseline deviation.

[0074] In this embodiment, when aligning the primary and secondary SAR complex images, the least square method is used to obtain the registration offset of the primary and secondary SAR complex images . Since sub-pixel registration is performed, i.e. , the secondary SAR complex image can obtain the final estimation result through the bilinear interpolation of the four points in the neighborhood, which is expressed as:

[0075] (9)

[0076] As can be seen from formula (9), this method is suitable for aligning the primary and secondary SAR complex images under non-stationary conditions.

[0077] Further, according to the registration offset , the two SAR complex images are aligned by using the interpolation method, and the relationship can be expressed as:

[0078] (10)

[0079] wherein, (11)

[0080] ​In step S120, the two registered SAR complex images are interfered to obtain the interfered phase of the two registered SAR complex images The process of the interference is shown in the following formula (12) :

[0081] (12)

[0082] Further, the interference phase containing the vehicle trace information is extracted from the formula (12), which is shown in the following formula (13) :

[0083] (13)

[0084] In the formula (13), represents the weak slant range change caused by the vehicle trace.

[0085] In step S130, the Goldstein adaptive filter is used to smooth the interference phase to obtain the wrapped flat phase . Specifically, the interference image is divided into sliding windows which overlap each other, and the phase power spectrum of each window is smoothed, that is,

[0086] (14)

[0087] In the formula (14), represents the filter parameter, is the smoothing operator; is the spatial frequency; and are the frequency spectrums of the image blocks before and after the filtering. When the filter parameter , there is no filtering effect, and when the filter parameter , the filtering effect is the largest. The interference phase contains the flat phase, the deformation phase and the noise phase. Since the deformation phase and the noise phase are non-uniformly distributed, it is difficult to directly determine the filter parameter . The commonly used method is to indirectly represent the filter parameter by using the coherence coefficient, that is,

[0088] (15)

[0089] As can be seen from the formula (14), the smoothing process can be regarded as using a larger smoothing strength for the low-coherence trace area and the noise area, and using a smaller smoothing strength for the high-coherence low-noise area, so as to reduce the loss of the flat phase when smoothing the noise phase and the deformation phase.

[0090] Further, the wrapped flat phase is estimated under the condition that the baseline error is unknown. For the flat terrain, the process can be shown in the following formula (16) :

[0091] (16)

[0092] In formula (16), Represents a smoothing function. This indicates the estimated flatland phase.

[0093] Next, the entangled flat phase is untangled, and the process is represented as follows:

[0094] (17)

[0095] In formula (17), Represents the untangling function. This indicates the flat-ground phase after untangling.

[0096] In this embodiment, the flat-ground phase in the interference phase is eliminated to obtain the deformed phase. This process first involves smoothing the obtained flat-ground phase. Perform phase unwrapping to obtain the unwrapped phase. Then, based on the untangling phase The ambiguity number for each phase is obtained, i.e.:

[0097] (18)

[0098] Next, based on the fuzzy number Unwrapping the unsmoothed interference phase, i.e.:

[0099] (19)

[0100] Finally, eliminate flat-ground phase component Obtain the deformation phase ,Right now:

[0101] (20)

[0102] In step S140, a vehicle trace feature representation image is obtained using salt-and-pepper filtering. This process, based on ordination statistics theory, replaces the value of the neighborhood center point with the median value of its neighborhood, allowing the surrounding values ​​to be represented. pixel The value is close to the true value, thus eliminating isolated noise points.

[0103] This paper also demonstrates the effectiveness of the proposed method through experiments. To verify the effectiveness of the proposed algorithm, our project team conducted a vehicle trace characterization experiment based on zero-baseline airborne heavy-track interferometric SAR at a certain location. The geometric model of the experiment is as follows: Figure 2 As shown, where, and Indicates the positions of the two routes. This indicates the baseline deviation generated by zero-baseline heavy orbit interferometric SAR. Indicates baseline the horizontal angle between the the flight path the height of the the target position of the area where the ground surface does not produce deformation, the target position before the ground surface deforms, the target position after the ground surface deforms, and the and the downward angle relative to the target slant range, the deformation amount of the before and after the ground surface deforms. The experimental scene is a farmland with a block without crop coverage, and the optical image of the scene is shown in FIG. 3. Two corner reflectors are placed in the farmland, and the left corner reflector does not change position during the heavy rail flight, while the right corner reflector moves out of the scene before the second flight.

[0104] The echo data in step S100 is collected by a radar system carried under a manned aircraft.

[0105] The two-time BP imaging results before image alignment in step S110 are shown in FIG. 2, wherein Figure 4 (a) represents the main SAR image; and Figure 4 (b) represents the auxiliary SAR image. Figure 4 The two-time BP imaging results after image alignment in step S110 are shown in FIG. 3, wherein Figure 5 (a) represents the main SAR image; and Figure 5 (b) represents the auxiliary SAR image. Figure 5 The imaging results shown in FIGS. 2 and 3 show that the vehicle traces cannot be represented by the change of the ground surface scattering intensity. Affected by the flight path deviation, the left corner reflector that does not change position produces a small change in scattering intensity, and the right corner reflector that changes position produces a large change in scattering intensity. Figure 4 Figure 5 The interference phase and the estimated flat phase in steps S120 and S130 are shown in FIG. 4, wherein (a) represents the interference phase, which contains the wrapped flat phase, the deformation phase and the noise phase; and

[0107] (b) represents the approximately flat phase obtained by estimation, wherein the noise phase is completely suppressed, most of the deformation phase is smoothed, and only a small amount of residual deformation phase exists. Figure 6 Figure 6 The estimation of the deformation phase in step S130 is shown in FIG. 5, wherein Figure 6 (a) represents the deformation phase, which contains the deformation phase and the noise phase; and (b) represents the deformation phase obtained by estimation, wherein the noise phase is completely suppressed, and only a small amount of residual deformation phase exists.

[0108] Figure 7 The estimation of the deformation phase in step S130 is shown in FIG. 5, wherein Figure 7 (a) represents the deformation phase, which contains the deformation phase and the noise phase; and Figure 6 (b) represents the deformation phase obtained by estimation, wherein the noise phase is completely suppressed, and only a small amount of residual deformation phase exists.​Figure 7 Figure 3 shows the unwrapping results of the interferometric phase, where Figure 7 (a) represents the unwrapping results of the interferometric phase, which contains the deformation phase, the noise phase and the flat phase; Figure 6 (a) represents the unwrapping results of the interferometric phase, which contains the deformation phase, the noise phase and the flat phase; Figure 7 (b) represents the unwrapping results of the approximate flat phase, which mainly contains the flat phase; Figure 6 (b) represents the unwrapping results of the approximate flat phase, which mainly contains the flat phase; Figure 7 (c) represents the filtered deformation phase of the change phase shown in (a) and Figure 7 (c) represents the filtered deformation phase of the change phase shown in (a) and Figure 7 (b) represents the filtered deformation phase of the change phase shown in (a) and (b). By comparing the two vehicle trace characterization methods, it can be seen that the trace characterization results obtained by the traditional coherent coefficient method cannot suppress the low coherence false alarm formed by the two corner reflection targets, and the proposed interference method better suppresses the low coherence false alarm in the trace characterization results.

[0109] The vehicle trace characterization image in step S140 is shown in Figure 4, where Figure 8 (a) represents the vehicle trace characterization image obtained by filtering the change phase shown in (c); and Figure 8 (a) represents the vehicle trace characterization image obtained by filtering the change phase shown in (c); and Figure 7 (b) represents the vehicle trace characterization image obtained by the traditional coherent coefficient method. By comparing the two vehicle trace characterization methods, it can be seen that the trace characterization results obtained by the traditional coherent coefficient method cannot suppress the low coherence false alarm formed by the two corner reflection targets, and the proposed interference method better suppresses the low coherence false alarm in the trace characterization results. Figure 8 The above-mentioned zero baseline airborne track interferometric SAR vehicle trace characterization method, by adopting the zero baseline airborne track with constant radar beam pointing, obtains the original echo data; adopts the BP imaging algorithm to image the original echo data, obtains the SAR complex image at two time points; adopts the image registration method to calculate the sub-pixel offset of the main and auxiliary SAR complex images; according to the registration offset, adopts the interpolation method to obtain the aligned auxiliary SAR complex; according to the aligned main and auxiliary SAR complex images, adopts the interference processing to obtain the interferometric phase; according to the interferometric phase, adopts the image smoothing method to blur the deformation phase in the interferometric phase, to obtain the wrapped flat phase; according to the wrapped flat phase, unwraps the flat phase to obtain the ambiguity number of the interferometric phase; according to the ambiguity number of the interferometric phase, obtains the unwrapped interferometric phase; according to the unwrapped interferometric phase and the unwrapped flat phase, adopts the difference method to obtain the deformation phase containing the noise phase; using the filtering method, filters the noise in the deformation phase to obtain the clear vehicle trace characterization image. The deformation phase based SAR trace feature provides a supplement to the existing coherent based SAR trace feature, and improves the robustness of the SAR vehicle trace detection.

[0110] It should be understood that, although

[0111] Figure 1 ​The steps in the flowcharts are shown in sequence according to the arrows, but the steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, the steps are not strictly limited in sequence, and the steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the flowcharts can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or sub-steps or stages of other steps.

[0112] In one embodiment, as shown in Figure 9 A zero-baseline airborne heavy-rail interferometric SAR vehicle trace characterization device is provided, comprising: an original echo data acquisition module 200, an image registration module 210, an interferometric phase extraction module 220, a deformation phase acquisition module 230, and a vehicle trace characterization module 240, wherein:

[0113] The original echo data acquisition module 200 is configured to acquire two sets of original echo data of the ground before and after the vehicle travels, the original echo data being detected by a zero-baseline airborne heavy-rail SAR;

[0114] The image registration module 210 is configured to image the two sets of original echo data using a BP imaging method to obtain two SAR complex images of the ground before and after the vehicle travels, and align the two SAR complex images based on a sub-pixel offset between the two SAR complex images;

[0115] The interferometric phase extraction module 220 is configured to perform interferometric processing on the two aligned SAR complex images to obtain an interferometric image, and extract an interferometric phase containing vehicle trace information from the interferometric image;

[0116] The deformation phase acquisition module 230 is configured to estimate a wrapped flat ground phase under unknown baseline error from the interferometric phase, unwrap the flat ground phase after unwrapping the flat ground phase, indirectly unwrap the interferometric phase based on a phase ambiguity number of the wrapped flat ground phase and the unwrapped flat ground phase, eliminate the flat ground phase in the unwrapped interferometric phase, and obtain a deformation phase generated only by the vehicle trace;

[0117] The vehicle trace characterization module 240 is configured to filter the deformation phase to obtain a SAR image of vehicle trace characterization.

[0118] The specific definition of the vehicle trace characterization device based on zero baseline airborne heavy-rail interferometric SAR can refer to the definition of the vehicle trace characterization method based on zero baseline airborne heavy-rail interferometric SAR, which will not be repeated here. Each module in the vehicle trace characterization device based on zero baseline airborne heavy-rail interferometric SAR can be realized by software, hardware, and a combination thereof, in whole or in part. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so that the processor calls and executes the operations corresponding to each of the above modules.

[0119] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in Figure 10 The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a vehicle trace characterization method based on zero baseline airborne heavy-rail interferometric SAR. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or can be a key, trackball, or touchpad arranged on the shell of the computer device, or can be an external keyboard, touchpad, or mouse, etc.

[0120] Those skilled in the art can understand that Figure 10 The structure shown in the above

[0121] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, the following steps are implemented:

[0122] Obtaining two groups of original echo data of the ground before and after the vehicle driving, the original echo data being detected by zero baseline airborne heavy-rail SAR;

[0123] Using a BP imaging method, imaging the two groups of original echo data to obtain two SAR complex images of the ground before and after the vehicle driving, and aligning based on a sub-pixel offset between the two SAR complex images.

[0124] interferometrically processing the two SAR complex images to obtain an interferometric image, and extracting an interferometric phase containing vehicle trace information from the interferometric image;

[0125] estimating a wrapped flat earth phase under unknown baseline error according to the interferometric phase, unwrapping the flat earth phase, indirectly unwrapping the interferometric phase according to a phase ambiguity number of the wrapped flat earth phase and the unwrapped flat earth phase, and obtaining a deformation phase generated only by the vehicle trace by eliminating the flat earth phase in the unwrapped interferometric phase;

[0126] filtering the deformation phase to obtain a SAR image representing the vehicle trace.

[0127] In one embodiment, a computer readable storage medium is provided, and a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the following steps:

[0128] obtaining two groups of original echo data of the ground before and after the vehicle driving, the original echo data being obtained by zero baseline airborne stripmap SAR;

[0129] imaging the two groups of original echo data by using a BP imaging method to obtain two SAR complex images of the ground before and after the vehicle driving, and aligning the two SAR complex images based on a sub-pixel offset between the two SAR complex images;

[0130] interferometrically processing the two SAR complex images to obtain an interferometric image, and extracting an interferometric phase containing vehicle trace information from the interferometric image;

[0131] estimating a wrapped flat earth phase under unknown baseline error according to the interferometric phase, unwrapping the flat earth phase, indirectly unwrapping the interferometric phase according to a phase ambiguity number of the wrapped flat earth phase and the unwrapped flat earth phase, and obtaining a deformation phase generated only by the vehicle trace by eliminating the flat earth phase in the unwrapped interferometric phase;

[0132] filtering the deformation phase to obtain a SAR image representing the vehicle trace.

[0133] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0134] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0135] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A zero baseline airborne heavy track interferometric SAR vehicle track characterization method, characterized by, The method comprises: obtaining two groups of original echo data of the ground before and after the vehicle driving, the original echo data being obtained by zero baseline airborne heavy track SAR detection; using a BP imaging method to image the two groups of original echo data to obtain two SAR complex images of the ground before and after the vehicle driving, and aligning the two SAR complex images based on a sub-pixel offset therebetween; performing interference processing on the two aligned SAR complex images to obtain an interference image, and extracting an interference phase containing vehicle trace information from the interference image; estimating a wrapped flat phase under unknown baseline error according to the interference phase, performing unwrapping on the flat phase, indirectly unwrapping the interference phase according to a phase ambiguity number of the wrapped flat phase and the unwrapped flat phase, and obtaining a deformation phase generated only by the vehicle trace by eliminating the flat phase in the unwrapped interference phase; filtering the deformation phase to obtain a SAR image representing the vehicle trace.

2. The zero baseline airborne rail track interference SAR vehicle track characterization method of claim 1, wherein, The two groups of original echo data are obtained by first and second SAR detection of an observation area by a zero baseline airborne heavy track SAR system before and after the vehicle driving, and in the two SAR detection processes, an aircraft of the zero baseline airborne heavy track SAR system flies along completely coinciding tracks, and a radar beam direction of the SAR system remains consistent.

3. The zero baseline airborne rail interference SAR vehicle track characterization method of claim 2, wherein, The aligning based on the sub-pixel offset between the two SAR complex images comprises: based on amplitude information, estimating a sub-pixel offset between the two SAR complex images generated by unknown baseline deviation by using Lucas optical flow registration; based on the sub-pixel offset, aligning the two SAR complex images by using bidirectional interpolation.

4. The zero baseline airborne rail interference SAR vehicle track characterization method of claim 3, wherein, The wrapped flat phase under unknown baseline error is estimated according to the interference phase by using Goldstein adaptive filtering.

5. The zero baseline airborne rail interference SAR vehicle track characterization method of claim 4, wherein, The indirectly unwrapping the interference phase according to the phase ambiguity number of the wrapped flat phase and the unwrapped flat phase is represented as: In the above formulae, denotes the phase ambiguity number obtained from the unwrapped flat earth phase, denotes the wrapped flat earth phase.

6. The zero baseline airborne rail interference SAR vehicle track characterization method of Claim 5, wherein, The deformation phase generated only by the vehicle trace is obtained by eliminating the flat phase in the unwrapped interference phase, which is represented as: In the above formulae, and respectively represent the indirectly unwrapped interferometric phase and the flat phase, denotes the deformation phase.

7. The zero baseline airborne rail interference SAR vehicle track characterization method of Claim 6, wherein, The SAR image representing the vehicle trace is obtained by performing salt and pepper filtering on the deformation phase.

8. A zero baseline airborne heavy track interferometric SAR vehicle track characterization apparatus, characterized by, The device comprises: an original echo data obtaining module configured to obtain two groups of original echo data of the ground before and after the vehicle driving, the original echo data being obtained by zero baseline airborne heavy track SAR detection; an image registration module configured to use a BP imaging method to image the two groups of original echo data to obtain two SAR complex images of the ground before and after the vehicle driving, and align the two SAR complex images based on a sub-pixel offset therebetween; an interference phase extracting module configured to perform interference processing on the two aligned SAR complex images to obtain an interference image, and extract an interference phase containing vehicle trace information from the interference image; a deformation phase acquisition module, configured to: acquire a wrapped flat phase according to the baseline error unknown interferometric phase; perform unwrapping on the wrapped flat phase to obtain an unwrapped flat phase; perform indirect unwrapping on the interferometric phase according to a phase ambiguity number of the wrapped flat phase and the unwrapped flat phase; and obtain a deformation phase generated only by the vehicle trace by eliminating the flat phase in the unwrapped interferometric phase; a vehicle trace representation module, configured to: filter the deformation phase to obtain a SAR image representing the vehicle trace.

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