A method and system for enhancing image characteristics based on multi-azimuth SAR observation

Abstract

The present invention discloses an image characteristic enhancement method based on multi-azimuth SAR observation, comprising the following steps: S1: acquiring large-angle / circular-track SAR echo data and performing sub-aperture segmentation on the data to obtain sub-aperture SAR echo data of different viewing angles, performing imaging processing based on the sub-aperture SAR echo data to obtain sub-aperture image sequence sets of different observation viewing angles; S2: performing geometric correction and amplitude correction based on the sub-aperture image sequence set to obtain a corrected sub-aperture image sequence set; S3: performing amplitude fusion processing based on the corrected sub-aperture image sequence set to obtain a SAR image with enhanced scattering characteristics. The present invention makes full use of the complementarity of multi-angle observation information, effectively suppresses target side lobes, thereby greatly enriching target scattering information in SAR images while obtaining high-definition images; the signal-to-noise ratio can be effectively improved; the tracking and detection of hidden targets can be performed; and the processing efficiency is greatly improved.

Description

Technical Field

[0001] The invention belongs to the field of synthetic aperture radar azimuth multi-angle observation image processing, and in particular relates to an image characteristic enhancement method and system based on multi-azimuth SAR observation. Background Art

[0002] Synthetic Aperture Radar (SAR) is an active remote sensing device that can work around the clock and in all weather conditions. It is widely used in military, civilian and other fields and has become one of the important means of high-resolution earth observation and global resource management. In order to more clearly detect important ground scenes and improve the interpretability of SAR images, high-quality images have always been an important direction for the development of SAR technology. In response to the above needs, SAR multi-azimuth observation is a new observation mode proposed in recent years. By repeatedly observing the same area at different azimuths, multi-view geometric information and scattering information of the objects in the area are obtained, thereby making up for the serious lack of information and poor image interpretability of the single-view SAR system. This mode can not only obtain richer target features in the scene, but also has the potential to enhance the target scattering characteristics, greatly improving the detection performance of the SAR system.

[0003] Since the scattering characteristics of radar targets depend on the measurement angle, feature information at different angles will be introduced in the multi-angle SAR imaging process. In view of this, domestic and foreign researchers have realized that the use of multi-view processing of SAR images can not only suppress noise, but also enrich target scattering information, thereby improving the processing performance of target detection and classification. Carin L et al. from the Department of Electrical and Computer Engineering at Duke University in the United States first obtained multiple sub-aperture images from a single-channel SAR image through a directional filter, and then used a hidden Markov model to establish the connection between the sub-images, and then implemented target detection. In addition, after fusing different sub-aperture images, the multi-directional scattering characteristics of the target can be more intuitively and comprehensively displayed, thereby greatly improving the image quality and the accuracy of target recognition.

[0004] In summary, the fusion of SAR images obtained from different observation angles can eliminate shadows and overlaps to a certain extent, effectively suppress side lobes, and thus obtain higher image clarity, which has a significant effect on improving the quality of SAR images and subsequent applications. To this end, this patent proposes a simple and efficient image feature enhancement method based on multi-azimuth SAR observations. Summary of the invention

[0005] The technical purpose of the present invention is to provide an image characteristic enhancement method and system based on multi-azimuth SAR observation to solve the problems of image overlap, top and bottom inversion and local shadow caused by observation angle in traditional mode.

[0006] To solve the above problems, the technical solution of the present invention is:

[0007] A method for enhancing image characteristics based on multi-azimuth SAR observations comprises the following steps:

[0008] S1: Acquire large-angle / circular SAR echo data and perform sub-aperture segmentation on it to obtain sub-aperture SAR echo data of different viewing angles, perform imaging processing based on the sub-aperture SAR echo data, and obtain sub-aperture image sequence sets of different observation viewing angles;

[0009] S2: performing geometric correction and amplitude correction based on the sub-aperture image sequence set to obtain a corrected sub-aperture image sequence set;

[0010] S3: Perform amplitude fusion processing on the corrected sub-aperture image sequence set to obtain a SAR image with enhanced scattering characteristics.

[0011] Wherein, step S1 specifically includes the following steps:

[0012] S11: performing sub-aperture segmentation on the large-angle / circular SAR echo data according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different viewing angles;

[0013] The calculation formula for the critical synthetic aperture time is:

[0014]

[0015] Among them, ρ a is the azimuth resolution, λ is the wavelength, R st V is the slant distance from the radar to the center of the scene, s is the satellite speed;

[0016] S12: Perform imaging processing on the sub-aperture SAR echo data through an imaging algorithm, thereby obtaining a sub-aperture image sequence set at different observation angles.

[0017] Wherein, step S2 specifically includes the following steps:

[0018] S21: Correct the difference in amplitude characteristics between images in the sub-aperture image sequence set by amplitude equalization, the correction factor δ i The calculation formula is

[0019]

[0020] Among them, S e,i is the amplitude information of the i-th sub-aperture image, S e,1 is the amplitude information of the first sub-aperture image, E{…} is the expectation operation, and ||…|| is the modulus operation;

[0021] S22: geocoding the sub-aperture image sequence set after amplitude equalization, and projecting the sub-aperture images in oblique moment coordinates into an international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

[0022] Among them, step S22 specifically includes the following steps:

[0023] S221: Based on the satellite position information, velocity information, slant range information and Doppler information corresponding to the four vertices on the sub-aperture image, positioning processing is performed to obtain its position in the WGS84 coordinate system. The positioning equation group is:

[0024]

[0025] Among them, (X s ,Y s ,Z s ) is the satellite position, (X t ,Y t ,Z t ) is the target point position, R is the slant moment vector of the radar reaching the target point. st is the modulus of the skew moment vector, is the velocity vector of the radar relative to the target point, f dc is the Doppler center frequency, R eq is the equatorial radius, R ep is the polar radius, and the target point positions corresponding to the four vertices can be obtained by solving the above equations using Newton iteration;

[0026] S222: According to the target point positions corresponding to the four vertices, the longitude and latitude range of the observation area in the longitude and latitude coordinate system is obtained through coordinate conversion;

[0027] S223: Determine the starting and ending points and sampling interval of the coordinate axes of the new image in the longitude and latitude coordinate system according to the longitude and latitude range;

[0028] S224: performing positioning processing on the satellite position information, velocity information, slant moment information and Doppler information corresponding to any pixel at a point on the sub-aperture image to obtain its position in the WGS84 coordinate system and converting the coordinates into the longitude and latitude coordinate system;

[0029] S225: Copy the pixel value of any pixel point to the grid corresponding to the new image according to the latitude and longitude coordinates of the pixel point.

[0030] An image characteristic enhancement system based on multi-azimuth SAR observation, comprising a multi-view image generation module, a multi-view image correction module and a multi-view scattering characteristic enhancement module;

[0031] The multi-view image generation module is used to acquire large-angle / circular SAR echo data and perform sub-aperture segmentation on it to obtain sub-aperture SAR echo data of different viewing angles, and perform imaging processing based on the sub-aperture SAR echo data to obtain a sub-aperture image sequence set of different observation viewing angles;

[0032] The multi-view image correction module is used to receive the sub-aperture image sequence set to perform geometric correction and amplitude correction to obtain a corrected sub-aperture image sequence set;

[0033] The multi-view scattering characteristic enhancement module is used to receive the corrected sub-aperture image sequence set for amplitude fusion processing, and then obtain the SAR image with enhanced scattering characteristics.

[0034] Among them, the multi-view image generation module is used to perform sub-aperture segmentation on the large-angle / circular SAR echo data according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different view angles;

[0035] The calculation formula for the critical synthetic aperture time is:

[0036]

[0037] Among them, ρ a is the azimuth resolution, λ is the wavelength, R st V is the slant distance from the radar to the center of the scene, s is the satellite speed;

[0038] The sub-aperture SAR echo data are imaged and processed by an imaging algorithm to obtain a sub-aperture image sequence set with different observation angles.

[0039] Among them, the multi-view image correction module is used to correct the amplitude characteristic differences between images in the sub-aperture image sequence set by amplitude equalization. The correction factor δ i The calculation formula is

[0040]

[0041] Among them, S e,i is the amplitude information of the i-th sub-aperture image, S e,1 is the amplitude information of the first sub-aperture image, E{…} is the expectation operation, and ||…|| is the modulus operation;

[0042] The sub-aperture image sequence set after amplitude equalization is then geocoded, and the sub-aperture images in oblique moment coordinates are projected into the international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

[0043] Specifically, the multi-view image correction module is used to perform geometric correction. According to the satellite position information, velocity information, slant range information and Doppler information corresponding to the four vertices on the sub-aperture image, positioning processing is performed to obtain its position in the WGS84 coordinate system. The positioning equation group is:

[0044]

[0045] Among them, (X s ,Y s ,Z s ) is the satellite position, (X t ,Y t ,Z t ) is the target point position, R is the slant moment vector of the radar reaching the target point. st is the modulus of the skew moment vector, is the velocity vector of the radar relative to the target point, f dc is the Doppler center frequency, R eq is the equatorial radius, R ep is the polar radius, and the target point positions corresponding to the four vertices can be obtained by solving the above equations using Newton iteration;

[0046] According to the target point positions corresponding to the four vertices, the longitude and latitude range of the observation area in the longitude and latitude coordinate system is obtained through coordinate conversion;

[0047] Determine the starting and ending points and sampling interval of the coordinate axes of the new image in the longitude and latitude coordinate system according to the longitude and latitude range;

[0048] The satellite position information, velocity information, slant moment information and Doppler information corresponding to any pixel on the sub-aperture image are positioned to obtain its position in the WGS84 coordinate system and the coordinates are converted to the longitude and latitude coordinate system, and the pixel value is copied to the grid corresponding to the new image.

[0049] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0050] 1) The present invention makes full use of the complementarity of multi-angle observation information, overcomes the image overlap, top and bottom inversion and local shadow phenomena existing in the traditional mode, and can effectively suppress the target side lobe, thereby greatly enriching the target scattering information in the SAR image while obtaining high-definition images;

[0051] 2) The present invention utilizes the non-correlation of noise, and the signal-to-noise ratio of the fused SAR image can be effectively improved.

[0052] 3) The present invention utilizes the difference in viewing angles of multiple observation angles and can be used to track and detect hidden targets.

[0053] 4) The present invention can utilize cubic spline interpolation to realize fast fusion processing of all scene pixels, thus greatly improving processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0055] Figure 1 This is a flow chart of an image characteristic enhancement method based on multi-azimuth SAR observation according to the present invention;

[0056] Figure 2 The observation scene graph corresponding to the Gotcha data used in the present invention;

[0057] Figure 3 This is the imaging result diagram of different viewing angles of Gotcha data used in the present invention;

[0058] Figure 4 This is a graph showing the result of multi-azimuth image characteristic enhancement using Gotcha data in the present invention. DETAILED DESCRIPTION

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0060] In order to simplify the drawings, only the parts related to the present invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0061] The following is a further detailed description of a management and debugging device for a 16RCPC motherboard proposed by the present invention in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and claims.

[0062] Example 1

[0063] A method for enhancing image characteristics based on multi-azimuth SAR observations comprises the following steps:

[0064] First, in step S1, large-angle / circular SAR echo data is acquired through a multi-view image generation module and sub-aperture segmented to obtain sub-aperture SAR echo data of different viewing angles. Imaging processing is performed based on the sub-aperture SAR echo data to obtain a sub-aperture image sequence set of different observation viewing angles.

[0065] Specifically, step S1 can be divided into the following two steps.

[0066] First, in step S11, the large-angle / circular SAR echo data is segmented into sub-apertures according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different viewing angles;

[0067] The calculation formula for the critical synthetic aperture time is:

[0068]

[0069] Among them, ρ a is the azimuth resolution, λ is the wavelength, R st V is the slant distance from the radar to the center of the scene, s is the satellite speed.

[0070] Then in step S12, the sub-aperture SAR echo data is imaged by an imaging algorithm to obtain a sub-aperture image sequence set at different observation angles.

[0071] Next, in step S2, a multi-view image correction module performs geometric correction and amplitude correction based on the sub-aperture image sequence set to obtain a corrected sub-aperture image sequence set;

[0072] First, in step S21, the amplitude characteristic difference between images in the sub-aperture image sequence set is corrected by amplitude equalization. The correction factor δ i The calculation formula is

[0073]

[0074] Among them, S e,i is the amplitude information of the i-th sub-aperture image, S e,1 is the amplitude information of the first sub-aperture image, E{…} is the expectation operation, and ||…|| is the modulus operation;

[0075] Then comes step S22, geocoding the amplitude-equalized sub-aperture image sequence set, projecting the sub-aperture images in oblique moment coordinates into an international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

[0076] Preferably, step S22 can be divided into the following steps.

[0077] First, in step S221, positioning is performed based on the satellite position information, velocity information, slant range information, and Doppler information corresponding to the four vertices on the sub-aperture image to obtain its position in the WGS84 coordinate system. The positioning equation group is:

[0078]

[0079] Among them, (X s ,Y s ,Z s ) is the satellite position, (X t ,Y t ,Z t ) is the target point position, R is the slant moment vector of the radar reaching the target point. st is the modulus of the skew moment vector, is the velocity vector of the radar relative to the target point, f dc is the Doppler center frequency, R eq is the equatorial radius, R ep is the polar radius. The target point positions corresponding to the four vertices can be obtained by solving the above equations using Newton iteration.

[0080] In step S222, the longitude and latitude range of the observation area in the longitude and latitude coordinate system can be obtained through coordinate conversion according to the target point positions corresponding to the four vertices.

[0081] Next, in step S223, the start and end points and the sampling interval of the coordinate axes of the new image in the longitude and latitude coordinate system are determined based on the longitude and latitude range.

[0082] Next, in step S224, the satellite position information, velocity information, skew moment information and Doppler information corresponding to any pixel on the sub-aperture image are positioned to obtain its position in the WGS84 coordinate system and the coordinates are converted to the longitude and latitude coordinate system;

[0083] Finally, the pixel value of any pixel point is copied to the grid corresponding to the new image according to the latitude and longitude coordinates of the point.

[0084] In step S3, a multi-view scattering characteristic enhancement module is used to perform amplitude fusion processing on the corrected sub-aperture image sequence set, thereby obtaining a SAR image with enhanced scattering characteristics, which is what this embodiment aims to obtain.

[0085] See also Figures 2 to 4 In order to verify the effectiveness of the method in this embodiment, the Gotcha Volumetric SAR Dataset version 1.0 measured data released by the US Air Force Laboratory was used in the experiment. Figure 2As shown in the figure, the observation scene is a parking lot, which includes vehicles of different models, calibrators, grass, asphalt roads, street lights, trees and other objects.

[0086] First, the SAR echo data is divided into 36 non-overlapping sub-apertures, and the azimuth resolution of each sub-aperture data is 0.1m. This experiment first obtains SAR images corresponding to 36 sub-apertures. The echo imaging results of some sub-apertures (interval 60°) are shown in Figure 2. Figure 3 As shown, Figure 3 (a) is the image with an observation angle of 0°. Figure 3 (b) is the image with an observation angle of 60°. Figure 3 (c) is the image with an observation angle of 120°. Figure 3 (d) is the image with an observation angle of 180°. Figure 3 (e) is the image with an observation angle of 240°. Figure 3 (f) is the image with an observation angle of 300°. It can be clearly seen from the above figure that there are certain differences in the scattering characteristics between SAR images with different viewing angles. Figure 3 It can also be seen that the boundaries of the road and lawn in a single SAR image are blurred and difficult to identify; Figure 3 (a) and Figure 3 (c) It can be found that some strong scattering points in the lower left corner of the image do not appear in the SAR image when the observation angle is 0°, but are very obvious when the observation angle is 120°, proving that multi-angle observation has the potential to discover hidden targets. In addition, the changes in the scattering strength and scattering characteristics of the same vehicle in SAR images of different viewing angles are also very obvious, which will affect the detection and recognition accuracy of the target, proving that the amount of information in a single SAR image is very limited, and further illustrating the necessity of multi-angle SAR image fusion.

[0087] The fusion results of different numbers of multi-view SAR images are shown in Figure 2. Figure 4 As shown, Figure 4 (a) is the fusion result of 6 images. Figure 4 (b) is the fusion result of 15 images. Figure 4 (c) is the fusion result of 30 images. Figure 4 (d) is the fusion result of 36 images. Figure 4 As can be seen from (a) to (d), as the number of SAR images involved in fusion increases, the effect of speckle suppression becomes more obvious, and the fused SAR image becomes more uniform and fuller in visual effect, which improves the comfort of human eye observation, leading to better image interpretation and target recognition effects. Figure 4As shown in (d), the TopHat target in the lower left corner appears as a circular bright spot in the SAR image, indicating that its scattering characteristics have been restored with high quality; the outlines of various vehicles in the image, as well as the boundary between the lawn and the road are also clearly visible, and the pixel fullness is very close to the effect of an optical grayscale image.

[0088] Experiments have confirmed that the present invention can effectively enhance image characteristics and improve quality.

[0089] Example 2

[0090] See also Figure 1 This embodiment provides an image characteristic enhancement system based on multi-azimuth SAR observation, which is used in conjunction with an image characteristic enhancement method based on multi-azimuth SAR observation provided in Application Example 1. Specifically, it includes a multi-view image generation module, a multi-view image correction module, and a multi-view scattering characteristic enhancement module.

[0091] The multi-view image generation module is signal-connected to the multi-view image correction module, and is used to acquire large-angle / circular-track SAR echo data and perform sub-aperture segmentation on it to obtain sub-aperture SAR echo data of different viewing angles, and perform imaging processing based on the sub-aperture SAR echo data to obtain sub-aperture image sequence sets of different observation viewing angles.

[0092] In this module, the large-angle / circular SAR echo data are segmented into sub-apertures according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different viewing angles.

[0093] The calculation formula for the critical synthetic aperture time is:

[0094]

[0095] Among them, ρ a is the azimuth resolution, λ is the wavelength, R st V is the slant distance from the radar to the center of the scene, s is the satellite speed. Then, the sub-aperture SAR echo data is imaged and processed by an imaging algorithm set therein, thereby obtaining a sub-aperture image sequence set at different observation angles, and the sub-aperture image sequence set is a sub-aperture image at different angles.

[0096] Next, the sub-aperture image sequence set is received by the multi-view image correction module, which first performs amplitude correction and then geometric correction on the received sub-aperture image sequence set to obtain a corrected sub-aperture image sequence set.

[0097] Specifically, amplitude equalization is used to correct the difference in amplitude characteristics between images in the sub-aperture image sequence set, and the correction factor δ i The calculation formula is

[0098]

[0099] Among them, S e,i is the amplitude information of the i-th sub-aperture image, S e,1 is the amplitude information of the first sub-aperture image, E{…} is the expectation operation, and ||…|| is the modulo operation.

[0100] The sub-aperture image sequence set after amplitude equalization is then geometrically corrected and geocoded first, and the sub-aperture images in oblique moment coordinates are projected into the international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

[0101] Specifically, the satellite position information, velocity information, slant range information and Doppler information corresponding to the four vertices on the sub-aperture image are used for positioning processing to obtain its position in the WGS84 coordinate system. The positioning equation group is:

[0102]

[0103] Among them, (X s ,Y s ,Z s ) is the satellite position, (X t ,Y t ,Z t ) is the target point position, R is the slant moment vector of the radar reaching the target point. st is the modulus of the skew moment vector, is the velocity vector of the radar relative to the target point, f dc is the Doppler center frequency, R eq is the equatorial radius, R ep is the polar radius. The target point positions corresponding to the four vertices can be obtained by solving the above equations using Newton iteration.

[0104] According to the target point positions corresponding to the four vertices, the latitude and longitude range of the observation area in the latitude and longitude coordinate system is obtained through coordinate transformation. The starting and ending points and sampling interval of the coordinate axis of the new image in the latitude and longitude coordinate system are determined according to the latitude and longitude range. The satellite position information, velocity information, skew moment information and Doppler information corresponding to any pixel on the sub-aperture image are positioned to obtain its position in the WGS84 coordinate system and the coordinates are transformed to the latitude and longitude coordinate system, and the pixel value is copied to the grid corresponding to the new image.

[0105] Finally, the multi-view scattering characteristic enhancement module is connected to the multi-view image correction module signal to receive the corrected sub-aperture image sequence set for amplitude fusion processing, thereby obtaining a SAR image with enhanced scattering characteristics.

[0106] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they still fall within the protection scope of the present invention.

Claims

1. A method for enhancing image characteristics based on multi-azimuth SAR observation, characterized in that: The steps include: S1: acquiring large-angle / circular-track SAR echo data and performing sub-aperture segmentation on the data to obtain sub-aperture SAR echo data of different viewing angles, and performing imaging processing based on the sub-aperture SAR echo data to obtain sub-aperture image sequence sets of different observation viewing angles; S2: performing geometric correction and amplitude correction based on the sub-aperture image sequence set to obtain the corrected sub-aperture image sequence set; S3: performing amplitude fusion processing according to the corrected sub-aperture image sequence set, thereby obtaining a SAR image with enhanced scattering characteristics; Wherein, the step S1 specifically includes the following steps: S11: performing sub-aperture segmentation on the large-angle / circular SAR echo data according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different viewing angles; The calculation formula of the critical synthetic aperture time is: in, is the azimuth resolution, is the wavelength, is the slant distance from the radar to the center of the scene, is the satellite speed; S12: performing imaging processing on the sub-aperture SAR echo data by using an imaging algorithm, thereby obtaining the sub-aperture image sequence set at different observation angles.

2. The image characteristic enhancement method based on multi-azimuth SAR observation according to claim 1, characterized in that: The step S2 specifically includes the following steps: S21: Correcting the amplitude characteristic differences between the images in the sub-aperture image sequence set by amplitude equalization, the correction factor The calculation formula is in, For the The amplitude information of each sub-aperture image, For the The amplitude information of each sub-aperture image, To perform the expectation operation, is the modulo operation; S22: geocoding the amplitude-equalized sub-aperture image sequence set, and projecting the sub-aperture images in oblique moment coordinates into an international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

3. The image characteristic enhancement method based on multi-azimuth SAR observation according to claim 2 is characterized in that: The step S22 specifically includes the following steps S221: performing positioning processing based on the satellite position information, velocity information, slant range information and Doppler information corresponding to the four vertices on the sub-aperture image to obtain its position in the WGS84 coordinate system; S222: According to the target point positions corresponding to the four vertices, the longitude and latitude range of the observation area in the longitude and latitude coordinate system is obtained through coordinate conversion; S223: Determine the starting and ending points and sampling interval of the coordinate axes of the new image in the longitude and latitude coordinate system according to the longitude and latitude range; S224: performing positioning processing on the satellite position information, velocity information, slant moment information and Doppler information corresponding to any pixel at a point on the sub-aperture image to obtain its position in the WGS84 coordinate system and converting the coordinates into the longitude and latitude coordinate system; S225: Copy the pixel value of any pixel point to the grid corresponding to the new image according to the latitude and longitude coordinates of the point.

4. An image characteristic enhancement system based on multi-azimuth SAR observation, characterized in that: It includes a multi-view image generation module, a multi-view image correction module and a multi-view scattering characteristic enhancement module; The multi-view image generation module is used to acquire large-angle / circular SAR echo data and perform sub-aperture segmentation on it to obtain sub-aperture SAR echo data of different viewing angles, and perform imaging processing based on the sub-aperture SAR echo data to obtain sub-aperture image sequence sets of different observation viewing angles; The multi-view image correction module is used to receive the sub-aperture image sequence set to perform geometric correction and amplitude correction to obtain the corrected sub-aperture image sequence set; The multi-view scattering characteristic enhancement module is used to receive the corrected sub-aperture image sequence set to perform amplitude fusion processing, thereby obtaining a SAR image with enhanced scattering characteristics; The multi-view image generation module is further used to perform sub-aperture segmentation on the large-angle / circular SAR echo data according to the critical synthetic aperture time to obtain sub-aperture SAR echo data of different viewing angles; The calculation formula of the critical synthetic aperture time is: in, is the azimuth resolution, is the wavelength, is the slant distance from the radar to the center of the scene, is the satellite speed; The sub-aperture SAR echo data is imaged and processed by an imaging algorithm to obtain a sub-aperture image sequence set at different observation angles.

5. The image characteristic enhancement system based on multi-azimuth SAR observation according to claim 4 is characterized in that: The multi-view image correction module is used to correct the amplitude characteristic differences between the images in the sub-aperture image sequence set by using amplitude equalization. The calculation formula is in, For the The amplitude information of each sub-aperture image, For the The amplitude information of each sub-aperture image, To perform the expectation operation, is the modulo operation; Then, the sub-aperture image sequence set after amplitude equalization is subjected to geocoding processing, and the sub-aperture images under oblique moment coordinates are projected into the international standard geographic reference system, so that the pixel coordinates of the same target in different sub-aperture images are the same.

6. The image characteristic enhancement system based on multi-azimuth SAR observation according to claim 4, characterized in that: The multi-view image correction module is used to perform geometric correction, and performs positioning processing according to the satellite position information, velocity information, slant range information and Doppler information corresponding to the four vertices on the sub-aperture image to obtain its position in the WGS84 coordinate system; According to the target point positions corresponding to the four vertices, the longitude and latitude range of the observation area in the longitude and latitude coordinate system is obtained through coordinate conversion; Determine the starting and ending points and sampling interval of the coordinate axes of the new image in the longitude and latitude coordinate system according to the longitude and latitude range; The satellite position information, velocity information, slant moment information and Doppler information corresponding to any pixel on the sub-aperture image are positioned to obtain its position in the WGS84 coordinate system and the coordinates are converted to the longitude and latitude coordinate system, and the pixel value is copied to the grid corresponding to the new image.

Images