A scene DEM extraction method based on CSAR sub-aperture correlation

Abstract

This invention discloses a scene DEM extraction method based on CSAR sub-aperture correlation, belonging to the field of radar imaging technology. The method includes: dividing sub-aperture echo data into different viewpoints; image processing to obtain complex numerical sub-aperture images; binarization processing to transform the sub-aperture images into images containing only the target and background, and extracting the centroid position of the target region; registering the centroid points of the sub-aperture images from different viewpoints; calculating the target position offset and target height based on position offset geometry; calculating the DEM information of all sub-aperture images based on the target height; and integrating the DEM information of sub-aperture images from different viewpoints to obtain omnidirectional scene DEM information. This invention calculates the offset of different sub-aperture images by utilizing the centroid of the point target imaging, resulting in a simple calculation process, low complexity, and improved computational efficiency. By obtaining the target position offset through viewpoint differences and calculating omnidirectional scene DEM information, it achieves higher accuracy and noise resistance.

Description

Technical Field

[0001] This invention relates to the field of radar imaging technology, and more specifically, to a method for scene DEM extraction based on CSAR sub-aperture correlation. Background Technology

[0002] Synthetic Aperture Radar (SAR) achieves high range resolution by transmitting wide-bandwidth signals, while the radar platform observes targets at a wide angle, enabling high azimuth resolution. SAR imaging can obtain more electromagnetic scattering information about the observed target by reconstructing the target scattering function, which helps in the analysis, classification, and identification of target features. SAR provides all-weather, all-time reconnaissance capabilities, offering significant advantages in remote sensing observation and having a wide range of applications.

[0003] Circular SAR (CSAR) refers to a SAR platform that moves in a circular trajectory around the observation scene, with its antenna beam continuously pointing towards the target scene. CSAR mode enables omnidirectional observation, resulting in higher resolution images, multi-angle observation and imaging of the target, thus obtaining more complete target information and improving image resolution.

[0004] In existing airborne CSAR imaging experiments, lidar is typically used to acquire Digital Elevation Model (DEM) information of the reference scene as reference data for the imaging plane. However, because CSAR requires acquiring DEM information over a large area, lidar still faces challenges and limitations in acquiring DEM information. For example, lidar systems typically require expensive equipment and maintenance; lidar bands have difficulty penetrating clouds and dense vegetation; they are easily affected by weather and flight altitude limitations; and they cannot effectively extract DEM information from the observed scene, hindering the further application and development of CSAR technology. Methods for extracting DEM information using interferometric SAR have complex processing procedures and high algorithm complexity, requiring high accuracy in registration and phase unwrapping algorithms. If the high resolution and all-around observation capabilities of the CSAR system can be effectively utilized to directly extract scene elevation information from the CSAR data itself, more accurate and detailed terrain height information can be provided. Furthermore, because the CSAR system can operate under various weather conditions, it has significant advantages over optical and infrared remote sensing technologies, and it can achieve integrated imaging and processing, enabling real-time imaging in a short time from data acquisition to image processing and result output.

[0005] To address the aforementioned issues, this invention fully leverages the advantages of CSAR in extracting DEM information and proposes a scene DEM extraction method based on CSAR sub-aperture correlation. Summary of the Invention

[0006] The purpose of this invention is to overcome the problems of low accuracy and complex processing procedures in existing technologies for extracting DEM information of target scenes, and to make full use of the multi-angle observation characteristics of CSAR to extract DEM information of target scenes from sub-aperture imaging, providing a scene DEM extraction method based on CSAR sub-aperture correlation.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A method for scene DEM extraction based on CSAR sub-aperture correlation includes the following steps:

[0009] The full aperture echo data acquired by CSAR is divided into multiple sub-aperture echo data with different perspectives along the azimuth direction.

[0010] The CSAR imaging algorithm is used to process the echo data of each sub-aperture to obtain the corresponding complex numerical sub-aperture image.

[0011] Each of the complex numerical quantum aperture images is binarized to convert it into an image with only two values: target and background. The centroid position of the target region in each image is then extracted.

[0012] Based on the centroid position, centroid registration is performed between sub-aperture images from different viewpoints;

[0013] Based on the geometric relationship of the focus position offset, the target position offset of the sub-aperture images under different viewpoints after registration with the centroid point is calculated, and the target height is calculated based on the target position offset.

[0014] Based on known information, including the target height, the DEM information of each sub-aperture image is calculated, and the DEM information of sub-aperture images from different perspectives is integrated to obtain omnidirectional scene DEM information.

[0015] As a preferred embodiment, the sub-aperture echo data from multiple different perspectives are of the same size, and each sub-aperture echo data contains a phase history for subsequent sub-aperture imaging. When dividing the data, the sub-aperture length and overlap must be determined, and the resolution must be balanced.

[0016] As a preferred embodiment, the CSAR imaging algorithm includes a back projection (BP) algorithm, and GPU acceleration is used for parallel processing during the imaging process.

[0017] As a preferred option, the binarization process is performed using an adaptive binarization algorithm based on Otsu's method.

[0018] As a preferred embodiment, the step of extracting the centroid position includes: calculating the connected region, setting a connected region area threshold, excluding isolated noise points with an area smaller than the connected region threshold, and extracting the centroid position of the connected region.

[0019] As a preferred embodiment, the centroid registration includes: based on the centroid positions extracted from each sub-aperture image, finding the corresponding centroids for registration using the nearest neighbor matching metric, and using chain-like association calculation to traverse all sub-aperture images to complete the centroid registration of all sub-aperture images.

[0020] As a preferred embodiment, the chain-linked calculation specifically involves: selecting a certain sub-aperture image as the first sub-aperture image and using the centroid of the first sub-aperture image as a reference. The first sub-aperture image contains centroids in the sequence 1, 2, 3, ..., n. The centroids in the first sub-aperture image with the sequence 1, 2, 3, ..., n are traversed. In the second sub-aperture image, the centroid closest to the centroid with the sequence 1 in the first sub-aperture image is found. The sequence number of this centroid is also marked as 1 in the second sub-aperture image. Other centroids are processed similarly. All centroids in all sub-aperture images are traversed to complete the one-to-one correspondence of centroids.

[0021] As a preferred embodiment, the step of calculating the target position offset of all sub-aperture images under different viewing angles after centroid registration based on the geometric relationship of the focus position offset, and calculating the target height based on the target position offset, specifically involves:

[0022] Ignoring the variation of the CSAR beam elevation angle θ within the target scene region, the imaging reference plane is set as H. ref =z p -Δh, the imaging projection positions of point target P in the two sub-aperture images are Pi and Pj respectively. A and P B Based on geometric relationships, their coordinates are expressed as follows:

[0023]

[0024] From the above equation, it can be seen that when there is an imaging position offset between two sub-aperture images of CSAR, the height deviation Δh and the imaging position offset Δr = |P A -P B The relationship between | is:

[0025]

[0026] in, The interval is the azimuth angle of the center of the sub-aperture.

[0027] As a preferred embodiment, the height deviation |Δh| should satisfy:

[0028]

[0029] Among them, k max This is the maximum wavenumber of the signal transmitted by the CSAR system.

[0030] As a preferred embodiment, after integrating the DEM information of sub-aperture images from different perspectives, the average value of the integrated DEM information is calculated, and the average value is used as the all-around scene DEM information.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] This invention calculates the offset of different sub-aperture imaging by using the centroid of point target imaging. It does not require the point target position coordinates as prior information, the calculation process is simple and the complexity is low, which improves the calculation efficiency. Furthermore, by using the multi-view information of sub-aperture imaging, the target position offset is obtained through the difference in viewpoints, and then the all-round scene DEM information is calculated. Compared with single-view imaging, it has higher accuracy and noise resistance. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the steps of a scene DEM extraction method based on CSAR sub-aperture correlation, as described in Embodiment 1 of this application.

[0034] Figure 2 This is a schematic diagram of CSAR imaging when the target has a height deviation, as shown in Embodiment 2 of this application.

[0035] Figure 3 This is a three-dimensional spatial geometry diagram of the CSAR mode in Embodiment 2 of this application;

[0036] Figure 4 The image shows the CSAR sub-aperture imaging results of the target at 1°~60°, 61°~120°, 121°~180°, 181°~240°, 241°~300°, and 301°~360° in Embodiment 2 of this application.

[0037] Figure 5 This is an illustration of the processing steps of a scene DEM extraction method based on CSAR sub-aperture correlation, as described in Embodiment 2 of this application.

[0038] Figure 6 This is a simulation imaging result diagram of 9 point targets in Embodiment 3 of this application;

[0039] Figure 7 The image shows the CSAR sub-aperture imaging results of the target at 1° to 60° in Embodiment 3 of this application;

[0040] Figure 8 The image shows the CSAR sub-aperture imaging results of the target at 61° to 120° for Embodiment 3 of this application. Detailed Implementation

[0041] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0042] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0043] Example 1

[0044] Please see Figure 1 Embodiment 1 of this application provides a method for scene DEM extraction based on CSAR sub-aperture correlation, including:

[0045] S1: Divide the full aperture echo data acquired by CSAR into multiple sub-aperture echo data with different perspectives along the azimuth direction.

[0046] The full-aperture echo data is divided into multiple sub-aperture echo data of the same size along the azimuth direction. Each sub-aperture echo data contains phase history for subsequent sub-aperture imaging. When dividing, the sub-aperture length and overlap must be determined, and factors such as resolution must be weighed.

[0047] S2: The CSAR imaging algorithm is used to process the echo data of each sub-aperture to obtain the corresponding complex numerical sub-aperture image.

[0048] The CSAR imaging algorithm is used to process the echo data of each sub-aperture to obtain the corresponding complex numerical sub-aperture image, thus obtaining a sequence of multiple consecutive sub-aperture images. Given the excellent imaging performance of the BP algorithm, this embodiment 1 uses the BP algorithm for sub-aperture imaging processing. Furthermore, to improve imaging processing efficiency, a GPU-accelerated BP algorithm is used for parallel processing.

[0049] like Figure 4 The image shown represents six sub-aperture images obtained in Example 1, corresponding to sub-aperture imaging results for 1°–60°, 61°–120°, 121°–180°, 181°–240°, 241°–300°, and 301°–360°, respectively. The color bar unit is dB. Figure 4It can be observed that due to the height deviation between the target and the imaging plane, the focus position of the target will be different in different sub-aperture images, and the side lobe direction of sub-aperture imaging will also be different.

[0050] S3: Binarize each complex numerical quantum aperture image to convert it into an image with only two values: target and background, and extract the centroid position of the target region in each image.

[0051] Since the ultimate goal is to extract the contour of the target image, and the binary image only has two values, 0 and 1, resulting in low computational cost, each complex sub-aperture image needs to be binarized to convert it into only two values: target and background. Given that the Otsu method-based binarization algorithm can adaptively analyze the pixel distribution values ​​of each image and select the binarization threshold that maximizes the inter-class variance between the foreground and background images, thus achieving good image binarization results, this embodiment 1 uses an adaptive binarization algorithm based on the Otsu method to binarize the sub-aperture images.

[0052] Since sub-aperture imaging results in large target imaging regions, calculating the offset of point targets under different sub-aperture imaging using a sliding window method would be computationally too expensive. To reduce the computational cost of sub-aperture imaging offset, this invention calculates the centroid position of the point target region in the sub-aperture image as a reference point for subsequent sub-aperture image registration. Therefore, this step first calculates the connected regions, sets a connected region area threshold, then excludes isolated noise points with areas smaller than the connected region threshold, and finally extracts the centroid of the connected regions. Subsequent calculations only use the centroid of the point target imaging to calculate the offset of different sub-aperture imaging, which can improve computational efficiency.

[0053] S4: Based on the centroid position, perform centroid registration between sub-aperture images from different viewpoints;

[0054] To establish the correspondence between target scattering points from different viewpoints, centroid registration is required between images of different sub-apertures. In this embodiment 1, based on the centroid positions extracted from each sub-aperture image, the corresponding feature points are found and registered using the nearest neighbor matching metric.

[0055] This embodiment 1 uses chain-based correlation calculation, traversing all sub-aperture images. For example... Figure 5Step 4 illustrates a calculation example using chain association in this invention. Specifically, taking the centroid of the first sub-aperture image as a reference, the first sub-aperture image contains centroids in the sequence 1, 2, 3, ..., n. Traversing the centroids in the first sub-aperture image in the sequence 1, 2, 3, ..., n, in the second sub-aperture image, find the centroid closest to centroid sequence 1 in the first sub-aperture image, and mark the sequence number of this centroid in the second sub-aperture image as 1. Similar steps are taken for other centroids, traversing all sub-aperture images to complete the one-to-one correspondence of centroids.

[0056] S5: Based on the geometric relationship of the focus position offset, calculate the target position offset of all sub-aperture images under different viewing angles after registration with the centroid point, and calculate the target height based on the target position offset;

[0057] By utilizing the geometric relationship of the target's focused position offset in imaging with different sub-apertures, and solving for the viewing angle differences between sub-aperture images and known viewing angle parameters, the line-of-sight direction offset (target position offset) of the corresponding scattering point under different viewing angles is calculated, and then the target height is calculated. For example... Figure 5 As shown in step 5, the deviation between sub-aperture image 2 and sub-aperture image 1 is calculated, the deviation between sub-aperture image 3 and sub-aperture image 2 is calculated, and so on, traversing all sub-aperture images to obtain the target's focus position offset (target position offset) in different sub-aperture images.

[0058] S6: Based on known information including the target height, calculate the DEM information of each sub-aperture image, integrate the DEM information of sub-aperture images from different perspectives, and obtain all-around scene DEM information.

[0059] To reduce the impact of noise, DEM information obtained from associated sub-aperture images is integrated and its average value is calculated to obtain a more accurate and comprehensive scene DEM extraction result. The core of this invention lies in utilizing multi-view information from sub-aperture imaging to determine the target position offset through viewpoint differences, thereby calculating omnidirectional scene DEM information. Compared to single-view imaging, this invention offers higher accuracy and noise resistance.

[0060] Example 2

[0061] This embodiment 2 further explains (1) the impact of target height deviation on the CSAR full aperture imaging quality and (2) the principle of target scene DEM information extraction based on CSAR sub-aperture imaging in the scene DEM extraction method based on CSAR sub-aperture association in embodiment 1, including:

[0062] (1) The impact of target height deviation on the quality of CSAR full aperture imaging:

[0063] CSAR imaging focusing is easily affected by target height. Ideally, when the target is at the preset imaging plane height, the phase change of the echo signal can be fully compensated, resulting in a high-quality CSAR image that accurately reflects the target's geometric features. Conversely, if there is a deviation in the imaging plane height—that is, the actual height of the target does not match the imaging plane height—the resulting phase error will cause geometric deformation of the target imaging result, altering its shape, size, and position, thereby reducing imaging quality and the accuracy of target identification.

[0064] CSAR determines the distance between the target and the radar by measuring the arrival time of the echo signal. The measured distance is the slant range of the target along the radar's line of sight, not the target's actual ground projection distance. Therefore, during imaging, the target is projected onto an inclined plane with an angle to the ground, which is the slant range projection mode. For targets with a certain height, slant range projection causes the target to be projected onto the imaging plane along the line of sight, resulting in a shift in its position in the CSAR image compared to its actual ground projection position. This shift increases with the target's height and the line-of-sight angle.

[0065] Figure 2 This is a schematic diagram of CSAR imaging under conditions of target height deviation. Assuming the imaging plane height is 0m, the airborne CSAR performs circular observations with radius R on a plane at a height H above the imaging plane, with the beam center always pointing towards the target observation scene. Its circular observation flight trajectory is shown by the blue curve. A represents the CSAR's azimuth angle... The position at time, coordinates are A' is the projection of CSAR position A onto the imaging plane, and its coordinates are... Let I be a target at a distance Δh from the imaging plane. Due to the slant range projection mechanism of CSAR, target I will be projected onto the imaging plane at a distance Δh during imaging. A Place.

[0066] When performing observation and imaging, CSAR assumes its position coordinate vector is... Let the position coordinates of point target P within the observation area be r. p =(x p ,y p ,z p If the instantaneous distance from CSAR to point target P is:

[0067]

[0068] When the imaging plane height z0 = 0m, and the BP algorithm is used to image the echo signal of the point target P, the point spread function of the point target on the imaging plane can be expressed as:

[0069]

[0070] Where k is the wavenumber of the signal transmitted by the CSAR system, and R xy Let be the distance from CSAR to each grid point (x, y, 0) on the imaging plane grid, and its expression is:

[0071]

[0072] Performing a two-dimensional Fourier transform on equation (2), we obtain:

[0073]

[0074] Substituting the point extension function g(x,y) into equation (4), we get:

[0075]

[0076] Let Φ = -2kR lp +2kR xy -k x xk y If y, then equation (5) can be transformed into:

[0077]

[0078] Calculate the first-order partial derivatives of phase Φ with respect to variables x and y in equation (6) respectively, and the instantaneous frequency is:

[0079]

[0080] Setting the first-order partial derivative to zero, then k x ,k y The relationship between x and y is:

[0081]

[0082] Therefore, equation (6) can be expressed as:

[0083] G(k x ,k y )=exp{jΦ(x(k x ),y(k y ))} (9)

[0084] Using k in equation (8) x ,k y The relationship between x and y can be obtained as follows:

[0085]

[0086] Equation (10) is the relationship between the wavenumber and distance of any grid point (x,y,0) on the CSAR imaging plane. For a point target P, we have:

[0087]

[0088] Substituting equations (10) and (11) into equation (9), we get:

[0089]

[0090] in, Substituting into equation (12), we get:

[0091]

[0092] Performing a two-dimensional inverse Fourier transform on equation (13), we have:

[0093]

[0094] make:

[0095]

[0096] Find Ψ(k) for equation (15) x ,k y Regarding (k) x ,k y The first-order partial derivative of ) is then:

[0097]

[0098] Setting the first-order partial derivative to zero, we have:

[0099] (xx p ) 2 +(yy p ) 2 =(z p ·tanθ) 2 (17)

[0100] By analyzing equation (17), it can be seen that if there is a height difference between the target's location and the set imaging plane, the CSAR imaging quality will decrease, producing a specific geometric deformation on the imaging plane, which is a shape with (x p ,y p ( ) is the center, |z p The circular blur region with radius tanθ| results in ineffective energy concentration and poor image quality.

[0101] To ensure high-precision focusing of point target P during CSAR full-aperture imaging, it is necessary to guarantee the height difference Δh = |z| between the height of the point target and the imaging plane. p-z0| should satisfy: Δh ≤ Δr·cotθ / 2, where Δr is the resolution of the imaging plane grid. In this case, the imaging result can accurately reflect the actual position of the point target.

[0102] The impact of target height deviation on CSAR sub-aperture imaging quality:

[0103] From equation (15), let the phase error be Ψ. e Then we have:

[0104]

[0105] Ψ e (k x ,k y Convert to polar coordinate format Ψ e (ρ,φ), then we have:

[0106]

[0107] Where ρ=2ksinθ, then equation (19) can be rewritten as:

[0108]

[0109] After transformation, the phase error Ψ e It can be expressed as:

[0110] Ψ e =ρtanθ·Δh (21)

[0111] Phase error Ψ e It is a key factor affecting image quality, and it can be decomposed into a first-order phase error component Ψ. e1 Error components Ψ of quadratic and higher-order terms e2 .

[0112] Ψ e =Ψ e1 +Ψ e2 (twenty two)

[0113] Due to the positional error of the CSAR system, the motion of the target, or the instability of the CSAR platform, a first-order phase error component Ψ will be generated. e1 The error component Ψ directly affects the positional shift of the target image on the imaging plane and is a major factor determining imaging accuracy. e1 The expression is:

[0114] Ψ e1 =k x ·Δx+k y ·Δy (23)

[0115] The full circumference aperture is divided into multiple sub-apertures, with the center of each sub-aperture being... Azimuth angle is Then we have:

[0116]

[0117] Therefore, equation (23) can be rewritten as:

[0118]

[0119] in, The range of values ​​is make Equation (25) can be rewritten as:

[0120]

[0121] The error components Ψ of the quadratic and higher-order terms e2 The error typically originates from nonlinear effects during signal propagation, inhomogeneities in the propagation medium, or non-ideal characteristics of the imaging system itself, thus affecting image quality. Combining equation (22), the error components Ψ of the quadratic and higher-order terms... e2 for:

[0122]

[0123] Phase error Ψ e2 Should satisfy |Ψ e2 |<π / 4, thus enabling the imaging results to have a better focusing effect, taking You can get |Ψ e2 | max for:

[0124]

[0125] At this point, the height deviation |Δh| should satisfy:

[0126]

[0127] According to equation (29), the sub-aperture azimuth accumulation angle can be determined. The size of the sub-aperture directly affects the sensitivity of the imaging results to target height deviations. When the sub-aperture is more finely divided (smaller...), the sensitivity increases. When the sub-aperture division is coarser (larger), the data volume and computational load are larger, and the imaging results are less sensitive to height deviations, meaning the imaging quality is relatively better and the target focusing is more effective. However, when the sub-aperture division is coarser (larger), the data volume and computational load are larger, but the imaging results are less sensitive to height deviations, meaning the imaging quality is relatively better and the target focusing is more effective. When the data volume and computational load are relatively small, the imaging results are highly sensitive to height deviations, meaning poor image quality, and the target image may appear blurry or out of focus. Therefore, selecting an appropriate sub-aperture azimuth accumulation angle is crucial. To achieve high-quality imaging, it is crucial to select the appropriate method based on specific application requirements and the target's altitude range. The value is used to balance imaging quality and altitude measurement accuracy.

[0128] The sub-aperture division determines the grouping method of CSAR data, which in turn affects the resolution and accuracy of extracting elevation information from this data. A reasonable sub-aperture division can maximize the utilization of the correlation between CSAR data while reducing the impact of poor CSAR imaging quality caused by height deviations.

[0129] The above analysis shows that when there is an error between the actual height of the point target and the imaging plane, a circular blurred area will appear in CSAR full-aperture imaging, affecting the image quality. The size of this circular blurred area is closely related to the imaging height deviation, reflecting the importance of measuring the target's elevation information.

[0130] (2) Principle of target scene DEM information extraction based on CSAR sub-aperture imaging:

[0131] The above analysis shows that the focusing quality of target imaging is closely related to the imaging height deviation. When there is a deviation between the imaging reference plane and the true height of the target in the scene, the CSAR slant range projection cannot be projected onto the accurate image point, resulting in a positional offset. This affects the positional accuracy of the target imaging and the overall usability of the imaging results. Furthermore, this offset may vary in different sub-aperture imagings. By comparing the offset differences among these images, the target height can be estimated. The following section analyzes the method of extracting elevation information of the observation area using sub-aperture imaging in CSAR mode. Figure 3 The diagram illustrates the three-dimensional spatial geometry of the CSAR model, establishing a Cartesian coordinate system OXYZ. The CSAR platform observes the target by performing circular motion around the OZ axis in a plane of height H, with a velocity of V.

[0132] Figure 3 (a) in the image is a stereo view in CSAR mode. Figure 3 (b) is a top view of the CSAR mode, where the blue curve is the circular flight trajectory of the CSAR platform, and A is the CSAR azimuth angle. The position at point B is the CSAR position in azimuth angle. The location of point P. P is a point target with a height difference of Δh from the imaging plane, and its coordinates are P(x...). p ,y p ,z p Due to the slant range projection observation mechanism of CSAR, CSAR has limitations in azimuth angle... When observed, P will be projected onto the imaging plane. A Location; CSAR at azimuth angle When observed, P will be projected onto the imaging plane. B At each location, the projection of point target P onto the imaging plane deviates, causing geometric distortion in the CSAR image and resulting in poor image quality. Under CSAR full-aperture imaging, the point target P will project as a circular region onto the reference imaging plane due to the height difference, such as... Figure 3 As shown in the green areas in (a) and (b), their radii are:

[0133]

[0134] Where θ is the beam elevation angle of CSAR. As can be seen from equation (30), the geometric deformation of the sub-aperture image increases with the increase of the height difference. When the target is exactly on the imaging plane, that is, when the height deviation is zero, the defocusing ring radius is zero, which means that the point target is well focused and the imaging quality is the highest under this condition.

[0135] To further illustrate the method for extracting target height, a top view of the geometric model of a target with height errors in CSAR 2D imaging is analyzed, such as... Figure 3 (b) In this context, assuming the beam elevation angle θ varies negligibly within the target scene area, the imaging reference plane is set as H. ref =z p -Δh, the imaging projection positions of point target P in the two sub-aperture images are Pi and Pj respectively. A and P B Based on geometric relationships, their coordinates are expressed as follows:

[0136]

[0137] From equation (31), it can be seen that when the imaging position of point target P shifts between two CSAR sub-aperture images, the height difference Δh and the imaging position shift Δr = |P A -P B The relationship between | is:

[0138]

[0139] in, The interval is the azimuth angle of the center of the sub-aperture.

[0140] As can be seen from equation (32), the height of the target can be calculated by analyzing the imaging position offset between two sub-aperture images, and the DEM information of the entire scene area can be obtained by traversing all the observation angles of the sub-apertures.

[0141] Example 3

[0142] To demonstrate the effectiveness of the scene DEM extraction method based on CSAR sub-aperture correlation proposed in Example 1, a point target simulation experiment of the observed scene was conducted for verification. This Example 3 is based on Example 1 and Example 2.

[0143] The parameters of the point target experimental simulation system are shown in Table 1. It is easy to calculate that the CSAR beam top-down observation angle is 45°.

[0144] Table 1 Parameters of the Point Target Simulation System

[0145]

[0146]

[0147] The observation scene is set up with 9 point targets with the same scattering coefficient but different heights, and the imaging plane height is 0m. Their two-dimensional positions and heights are shown in Table 2.

[0148] Table 2 shows the coordinates of the target points.

[0149]

[0150] Imaging results of 9 point targets in the observation scene are as follows Figure 6 As shown. It can be found that when the imaging plane height is 0m, the imaging quality of a point target with a height of 2m is better than that of a point target with a height greater than 2m. As the height of the point target increases, the energy distribution becomes more dispersed, the resolution decreases, and the defocus ring radius increases, indicating that the intensity of the reflected signal weakens with the increase of the height deviation, which is consistent with the theoretical analysis. At the same time, the defocus ring shows a consistent trend, indicating that CSAR imaging has good consistency and predictability throughout the observation range, which is very important for calibrating and optimizing the imaging process. Among them, the actual height of point target C is 18m. Substituting the system parameters set in the simulation into equation (30), the theoretical defocus ring radius is 18m, and the imaging results show that the defocus ring radius is also 18m, which is consistent with the theoretical analysis.

[0151] To verify the effectiveness of the method for extracting scene DEM using sub-aperture imaging, sub-aperture imaging results for 1°–60° and 61°–120° are presented, such as... Figure 7 , Figure 8 As shown. By Figure 7 , Figure 8 It can be observed that due to the height difference between the target and the imaging plane, the same target exhibits significant positional shifts in imaging at different sub-apertures, and the sidelobe directions of the target imaging also differ. The sidelobe orientation remains consistent with the center angle of the sub-aperture, which is in line with theoretical analysis.

[0152] It can be observed that the greater the height difference between the actual height of the target and the imaging plane, the greater the positional offset. The height difference between target C and the imaging plane is 18m, and the imaging positional offset of target C under sub-apertures of 1°–60° and 61°–120° is 32.79m. The height difference between target H and the imaging plane is 4m, and the imaging positional offset of target H under sub-apertures of 1°–60° and 61°–120° is 7.31m, which is less than the offset of target C. Therefore, based on the relationship between the target's offset and the sub-aperture angle under different sub-aperture imaging conditions, the true height of the target can be extracted, and thus the DEM information of the scene can be extracted.

[0153] The scene DEM extraction method based on CSAR sub-aperture correlation of this invention was used to extract DEM information for nine point targets in the scene. The actual height of the point targets and the height extraction results are shown in Table 3. It can be found that for several point targets with height deviation from the imaging plane, this method can extract relatively accurate actual heights of the point targets with an error within 0.1m. For some point targets, the height extraction error is within 0.05m, demonstrating the effectiveness of the method proposed in this invention.

[0154] Table 3. Actual height and extraction results of point targets

[0155]

[0156] The other technical details and steps of this embodiment 3 are the same as those of embodiments 1 and 2, and will not be repeated here.

[0157] In summary, this invention proposes a scene DEM extraction method based on CSAR sub-aperture correlation. The invention details the approach, explaining the impact of target height deviation on CSAR full-aperture and sub-aperture imaging, and the DEM extraction principle. This method extracts DEM information with higher accuracy and better adaptability. Furthermore, it eliminates the need for point target coordinates as prior information, simplifying the calculation process and reducing complexity. Utilizing multi-view information from sub-aperture imaging, it calculates target position offsets based on viewpoint differences, thereby calculating omnidirectional scene DEM information. Compared to single-view imaging, this method offers higher accuracy and noise resistance. The target scene DEM information extracted by this invention can provide important source data for topographic mapping and topographic relief error compensation.

[0158] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for scene DEM extraction based on CSAR sub-aperture correlation, characterized in that, Includes the following steps: The full aperture echo data acquired by CSAR is divided into multiple sub-aperture echo data with different perspectives along the azimuth direction. The CSAR imaging algorithm is used to process the echo data of each sub-aperture to obtain the corresponding complex numerical sub-aperture image. Each of the complex numerical quantum aperture images is binarized to convert it into an image with only two values: target and background. The centroid position of the target region in each image is then extracted. Based on the centroid position, centroid registration is performed between sub-aperture images from different viewpoints; Based on the geometric relationship of the focus position offset, the target position offset of the sub-aperture images under different viewpoints after registration with the centroid point is calculated, and the target height is calculated based on the target position offset. Based on known information including the target height, the DEM information of each sub-aperture image is calculated, and the DEM information of sub-aperture images from different perspectives is integrated to obtain all-around scene DEM information. The steps for extracting the centroid position include: calculating the connected region, setting a connected region area threshold, excluding isolated noise points with an area smaller than the connected region threshold, and extracting the centroid position of the connected region. The centroid registration includes: based on the centroid positions extracted from each sub-aperture image, finding the corresponding centroids for registration using the nearest neighbor matching metric, and using chain-like association calculation to traverse all sub-aperture images to complete the centroid registration of all sub-aperture images.

2. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, The sub-aperture echo data from multiple different perspectives are of the same size. Each sub-aperture echo data contains a phase history for subsequent sub-aperture imaging. When dividing the data, the sub-aperture length and overlap must be determined, and the resolution must be balanced.

3. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, The CSAR imaging algorithm includes the back projection (BP) algorithm, and GPU acceleration is used for parallel processing during the imaging process.

4. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, The binarization process is performed using an adaptive binarization algorithm based on Otsu's method.

5. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, The chain-linked calculation is specifically as follows: Select a certain sub-aperture image as the first sub-aperture image and take the centroid of the first sub-aperture image as the reference. The first sub-aperture image has centroids in the sequence 1, 2, 3, ..., n. Traverse the centroids in the first sub-aperture image in the sequence 1, 2, 3, ..., n. In the second sub-aperture image, find the centroid closest to the centroid in the first sub-aperture image in the sequence 1. Mark the sequence number of this centroid in the second sub-aperture image as 1. Similar to other centroids, traverse all centroids in all sub-aperture images to complete the one-to-one correspondence of centroids.

6. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, The target position offset is calculated based on the geometric relationship of the focus position offset, and the target height is calculated based on the target position offset. Specifically: Ignore the beam elevation angle of CSAR Changes within the target scene area, with the imaging reference plane set as... Target The imaging projection positions in the two sub-aperture images are respectively and Based on geometric relationships, their coordinates are expressed as follows: As shown in the above formula, when there is an imaging position shift of point target P between two sub-aperture images of CSAR, the height deviation can be obtained. With imaging position offset The relationship is as follows: in, The interval is the azimuth angle of the center of the sub-aperture.

7. A scene DEM extraction method based on CSAR sub-aperture correlation according to claim 6, characterized in that, The height deviation It should meet the following requirements: in, This is the maximum wavenumber of the signal transmitted by the CSAR system.

8. The scene DEM extraction method based on CSAR sub-aperture correlation according to claim 1, characterized in that, After integrating the DEM information of sub-aperture images from different perspectives, the average value of the integrated DEM information is calculated, and the average value is used as the all-around scene DEM information.

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