CN115712118B - Pixel offset tracking monitoring and correcting method - Google Patents
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Abstract
The invention relates to a pixel offset tracking monitoring and correcting method, which comprises the steps of firstly determining an offset threshold value in a research area by using empirical knowledge, processing a cross-correlation coefficient matrix to ensure that the acquired offset cannot be greatly different from the actual situation, finding out a peak value which is larger than the average value of the cross-correlation coefficient in the cross-correlation coefficient matrix and the position thereof by using a peak function, optimizing the intensity of an image block for calculating the pixel offset if the peak value is only one, carrying out cross-correlation calculation again by using the optimized image block to obtain accurate deformation information, and finding out the peak value which is nearest to the deformation threshold value and the position thereof if the peak value is more than one.
Description
Technical Field
The invention belongs to the field of Synthetic Aperture Radar (SAR) pixel offset tracking (offset-tracking) deformation monitoring, and relates to a pixel offset tracking monitoring and correcting method.
Background
The deformation of the earth surface caused by earthquakes, landslides, mining, glaciers and the like can often reach several meters or even tens of meters, the large-magnitude deformation can cause complete incoherence of the interference phase of an interferometric InSAR technology for acquiring deformation information based on SAR image phase, and finally, the deformation value acquired by the InSAR technology is seriously smaller than the real deformation value, and even accurate deformation information cannot be acquired. In this case, the correlation scholars propose to acquire large-magnitude deformations using a pixel offset tracking technique based on SAR image intensity information. The pixel offset tracking method based on SAR image intensity information is derived from a normalized cross-correlation algorithm of image matching, and utilizes the intensity information of two images to calculate the similarity of the two images through a normalized correlation measurement formula, and utilizes the following formula to calculate the cross-correlation coefficient of the two imagesρ
Wherein the method comprises the steps ofρIs the cross-correlation coefficient,AandBis the intensity information of the main and auxiliary image blocks for calculating the cross-correlation coefficient,mandnis the length and width of the image block, also known as the size of the cross-correlation window. As can be seen from the formula, the cross-correlation coefficient is related to the intensity of the image and the cross-correlation window size.
The offset tracking technology is to calculate the cross-correlation coefficient matrix of two images by using the intensity information of the two registered SAR images, find out the position of the peak value of the cross-correlation coefficient matrix (usually the maximum value of the cross-correlation coefficient matrix), and the distance between the maximum value of the cross-correlation coefficient matrix and the center of the images in the azimuth direction and the distance direction is the offset of the two images in the azimuth direction and the distance direction, and finally convert the offset into deformation. The cross-correlation coefficient matrix directly relates to the offset of two images, and the cross-correlation coefficient matrix is directly obtained by utilizing the intensity information of the images through mathematical calculation, so that the monitoring precision of the method is directly influenced by the intensity information of the images and the size of a cross-correlation window.
The authors found during the course of the study that there may be multiple peaks in the cross-correlation coefficient matrix, and that the offset calculated using the largest peak is much more distant from the actual deformation. The reason may be that the intensity information of the two images is changed severely due to seasonal changes or human activities, so that the homonymous points of the two images are mismatched, a plurality of peaks appear in the cross-correlation coefficient matrix, and finally the distance between the maximum peak position of the cross-correlation coefficient matrix and the center of the images is larger, so that the monitoring result is abnormal.
Disclosure of Invention
The invention aims to solve the problems of the offset tracking technology based on SAR image intensity information, and provides a pixel offset tracking monitoring and correcting method which can remove the abnormality of an offset tracking result caused by severe change of the earth surface intensity, effectively solve the problem of inaccurate monitoring result caused by a plurality of peaks of a cross-correlation coefficient matrix and enable the offset tracking technology to acquire accurate earth surface deformation information.
A pixel offset tracking monitoring and correcting method comprises the following steps:
s1, registering two SAR images shot by the same SAR satellite in a research area, generating a main image and an auxiliary image, and predicting the maximum distance offset r and azimuth offset a in the research area by using an empirical method;
s2, carrying out offset tracking on the main image and the auxiliary image based on a cross-correlation algorithm, obtaining a cross-correlation coefficient matrix C1 of a pixel p (i, j) based on the constraint of a distance offset r and a direction offset a, and calculating to obtain the offset of the p (i, j) in the distance direction;
s3, repeating the step S2 until the offset tracking of the main image and the auxiliary image is completed, and acquiring a distance offset matrix RA of the main image and the auxiliary image;
s4, calculating a distance offset threshold Tr of the pixel p (i, j) based on the following formula:
if it isIf the pixel distance is not less than the preset threshold value, the pixel distance at the position (i, j) is not accurate to the offset, and the pixel distance is processed as follows:
acquiring a matrix C1 corresponding to the pixel p (i, j), finding out all peaks in the matrix C1, reserving peaks larger than a preset upper limit value for calculating a new distance direction offset, if the absolute value of the difference value of the new distance direction offset Tr is still larger than or equal to the preset threshold value, extracting the matrix C1 again after replacing the maximum value of the pixel intensity in each image block corresponding to the pixel p (i, j) by using other statistical values, and calculating the distance direction offset of the pixel (i, j);
and repeating S4 until the absolute value of the difference value between the new distance offset and the distance offset threshold Tr is smaller than the preset threshold.
In a preferred embodiment, in S2, the cross correlation coefficient matrix of a pixel p (i, j) is obtained based on the constraint of the distance offset r and the azimuth offset a, and the manner of obtaining the offset of p (i, j) in the distance direction is calculated as follows:
firstly, a cross correlation coefficient matrix C of a certain pixel p (i, j) is obtained based on a cross correlation algorithm, and then a new matrix is extracted from the matrix C:
；
wherein N is the column number of the cross correlation coefficient matrix, M is the line number of the cross correlation coefficient matrix, and N is the oversampling coefficient;
the distance between the position of the peak of the matrix C1 and the center of the matrix C1 in the distance direction is taken as the offset of the pixel p (i, j) in the distance direction.
As a preferred embodiment, the magnitude of the preset threshold is determined according to the accuracy of the offset tracking method.
As a preferred embodiment, the magnitude of the preset threshold is 3 times the accuracy of the offset tracking method.
As a preferred embodiment, the preset upper limit value is the average value of the matrix C1.
As a preferred embodiment, the maximum value of the pixel intensities in the image blocks is replaced by the average value of the pixel intensities in each image block.
As a preferred embodiment, in the step S4, if there are only 1 peaks reserved, the peak is directly used to calculate a new distance offset, and step S4 is repeated until the absolute value of the difference between the new distance offset and the distance offset threshold Tr is smaller than the preset threshold; if the number of repetitions exceeds the preset repetition upper limit value, the absolute value of the difference between the new distance offset and the distance offset threshold Tr is still greater than or equal to the preset threshold, and the distance offset of the pixel point is made to be 0.
In a preferred embodiment, in S4, if the number of the reserved peaks exceeds 1, the distance offset between each peak and the center of the C1 matrix in the distance direction is calculated n To make |Tr-offset n Minimum offset n As a new distance vector offset.
The cross-correlation coefficient matrix tracked by the offset may have a plurality of peaks, so that the monitoring result is greatly different from the actual situation due to the peak identification error, and the monitoring result is abnormal due to the severe change of the surface intensity. According to the invention, the offset threshold value in the research area is determined by using experience knowledge, the obtained offset is ensured not to have a larger difference from the actual situation by processing the cross-correlation coefficient matrix, then the peak value and the position thereof which are larger than the average value of the cross-correlation coefficient in the cross-correlation coefficient matrix are found out by using the peak value function, if the peak value is only one, the intensity of the image block used for calculating the pixel offset is optimized, the accurate deformation information is obtained by carrying out cross-correlation calculation again by using the optimized image block, if the peak value is more than one, the peak value closest to the deformation threshold value and the position thereof are found out, and under the condition, the cross-correlation calculation is not needed again, a large amount of calculation time can be saved under the condition of finding out the accurate offset, the calculation efficiency is greatly improved, the abnormal result of offset tracking caused by the severe change of the intensity can be accurately identified, the calculation efficiency is improved while the accuracy of the offset tracking monitoring method is improved, and the technical support is provided for the offset tracking monitoring method.
Drawings
Fig. 1 is two SAR image intensity maps covering an investigation region.
Fig. 2 is a graph of primary and secondary tile intensities for calculating the pixel p (620,320) offset.
Fig. 3 (a) is a cross correlation coefficient matrix C of the pixel p (620,320); (b) Is a cross correlation coefficient matrix C1 of the pixel p (620,320) after the offset threshold is set.
Fig. 4 (a) is a graph of distance deformation obtained by offset tracking using two intensity maps; (b) The distance direction deformation graph is obtained after the offset threshold is set.
Fig. 5 (a) shows the main tile intensity for calculating the pixel p (640,530) offset; (b) The auxiliary image block intensity is used for calculating the offset of the pixel p (640,530); (C) is the cross correlation coefficient matrix C of the pixel p (640,530); (d) is the cross correlation coefficient matrix C1 of the pixel p (640,530).
Fig. 6 is a distance offset obtained using the method of the present invention.
Detailed Description
The following detailed description of specific embodiments of the invention refers to the accompanying drawings.
In the embodiment, the vertical deformation monitoring of the 52304 working face of the large Liu Da mining area in Ulmin, shaanxi province is selected as an experimental object, the coal seam mining thickness of the 52304 working face is 6.45m, and the mining time is 11 months 1 in 2011 to 3 months 25 in 2013.
1) Selecting a 2-scene terrsar-X image covering a research area as an experimental image, wherein the image distance is 0.91m towards the pixel, the image incidence angle is 42.43 degrees, the image acquisition time is 26 days of 1 month in 2013 and 6 days of 2 months in 2013, and matlab R2020b is the image processing software; and cutting and registering the 2-scene terra SAR-X image, wherein the two registered image intensity images are shown in fig. 1, the image size is 900X 600 pixels, m=900, and n=600. As can be seen from fig. 1, the intensity within the black box changes drastically.
2) Maximum sedimentation value during 10 days 2012, 11, 4, and 10 in 2013 in study area obtained by GPS monitoringS𝑢𝑏For 4.3m, if no other monitoring means is used to obtain the maximum sedimentation value in the research area, the empirical knowledge can be used: the maximum subsidence of the earth surface does not exceed the coal seam mining thickness, and the maximum subsidence value in the research area is predictedS𝑢𝑏For a coal seam mining thickness of 6.45m, the maximum distance offset r in the investigation region was calculated using the following formula:
in the method, in the process of the invention,𝑆izeis the SAR image distance to pixel size,𝜃is the angle of incidence of the SAR image. By means ofS𝑢𝑏R=3.49 calculated according to the above formula, r is set to 4 for ease of calculation; according to empirical knowledge, horizontal movement caused by coal exploitation does not exceed a maximum sedimentation value, and the azimuth offset threshold value a is set to be 4 for the convenience of calculation.
3) Setting the cross correlation window size of the offset tracking technique to 64 pel x 64 pel, for pel p (i, j), image block a1=a (i-32:i+32, j-32:j+32) in a for calculating the offset of pel p (i, j), image block b1=b (i-32:i+32, j-32:j+32) in B for calculating the offset of pel p (i, j), when i=620, j=320, the position of the point in the study area is shown as a black dot in fig. 1, the point is at the edge of the deformation area, the deformation should belong to small magnitude deformation, and fig. 2 is the image block intensity for calculating the offset of pel p (620,320); setting the oversampling coefficient as 8, performing cross-correlation calculation on A1 and B1 to obtain a cross-correlation coefficient matrix C (the size is 520 x 520, as shown in (a) of fig. 3) of A1 and B1, finding out the position of a matrix C peak (as shown by a black square box in (a)), wherein the distance between the position of the matrix C peak and the center of the matrix C in the distance direction and the azimuth direction is 1.6875 pixel and 15.4375 pixel respectively, and calculating the distance direction and the azimuth direction offset of a pixel p (620,320) by using an original method by using the matrix C to obtain 1.6875 pixel and 15.4375 pixel, which obviously are different from the actual situation. Extracting a new cross-correlation coefficient matrix C1=C (260-a×8:260+a×8, 260-r×8:260+r×8), as shown in (b) of fig. 3, finding out the position of the peak of the matrix C1, wherein the distance between the position of the peak of the matrix C1 and the center of the matrix C1 in the distance direction and the azimuth direction is 0.0625 pixel and 0.3125 pixel, that is, the offset of the pixel p (620,320) in the distance direction and the azimuth direction is 0.0625 pixel and 0.3125 pixel, so that the calculated distance direction and azimuth direction offset cannot exceed two thresholds r and a of the distance direction and the azimuth direction offset in the research area. If i.ltoreq.32 or i > m-32 or j.ltoreq.32 or j > n-32, the offset of the picture element p (i, j) in both the distance direction and the azimuth direction is directly made zero. And carrying out offset tracking processing on each pixel of the A and the B according to the method to obtain a distance offset matrix RA (the size is m x n) of the intensity diagrams A and B. In fig. 4, (a) is a graph of the distance direction deformation obtained by using the original offset tracking method, and (b) is a graph of the distance direction deformation obtained by using the set offset threshold, it can be seen from the graph that a large number of outliers exist in the distance direction deformation obtained by using the original offset tracking method, and the outliers in the distance direction deformation can be greatly reduced by using the offset tracking method of the set offset threshold.
4) Let i=33, j=33, where i is a positive integer ranging from 33 to m-32, j is a positive integer ranging from 33 to n-32, and for pixel p (i, j), the distance-to-offset threshold Tr of the pixel is calculated according to the following formula:
if |Tr-RA (i, j) | <0.3, indicating that the distance-wise offset of pixel p (i, j) is accurate, no correction is required, tr for the next pixel p (i, j+1) is calculated, and if j > n-32, tr for pixel p (i+1, 33) is calculated; if Tr-RA (i, j) is larger than or equal to 0.3, which indicates that the distance-direction offset of the pixel p (i, j) is inaccurate and needs correction, the following steps are executed:
(1) extracting an image block A1=A (i-32:i+32, j-32:j+32) of the pixel p (i, j) in the A for offset tracking, and extracting an image block B1=B (i-32:i+32, j-32:j+32) of the pixel p (i, j) in the B for offset tracking;
(2) calculating a cross correlation coefficient matrix C of A1 and B1, extracting a new cross correlation coefficient matrix C1=C (260-a.8: 260+a.8, 260-r.8: 260+r.8), and calculating a mean_c of C1;
(3) let temp=0, find out all peak values and positions of all peak values of C1 by using imregionalmax function, and remove peak values with peak values smaller than or equal to mean_c, only keep peak value P and its position that peak value is greater than mean_c, calculate distance offset1 in distance direction of the position of peak value and C1 matrix center that remain, if there is only one peak value that remains, go through step (4) - (5); if more than one peak is retained, executing step (6);
(4) if |tr-offset1| <0.3, let RA (i, j) =offset 1, execute step 5); if |tr-offset1| > =0.3, step (5) is performed;
(5) if temp >5, let RA (i, j) =0, execute step 5); if temp < = 5, let temp = temp +1, calculate mean_a and mean_b of A1 and B1, find out max_a and max_b of A1 and B1, let the intensity value of the pixel where max_a is located be equal to mean_a, let the intensity value of the pixel where max_b is located be equal to mean_b, form new A1 and B1; calculating a new cross-correlation coefficient matrix C of A1 and B1, extracting a new cross-correlation coefficient matrix C1=C (260-a×8: 260+a×8, 260-r×8: 260+r×8), calculating a distance offset1 between a peak value of the matrix C1 and the center of the matrix C1 in a distance direction, and executing the step (4);
(6) calculating the difference value Diff between each offset in the offset1 and Tr, diff= |Tr-offset1|, finding out the minimum value Rmin of Diff and the offset2 corresponding to Rmin, if Rmin <0.3, making RA (i, j) =offset 2, and executing step 5); if Rmin is more than or equal to 0.3, executing the steps (4) - (5);
step 4) will be further described with reference to pixel p (640,530): RA (639,529) =0.0625, RA (639,530) =0.0625, RA (639,531) =0.0625, RA (640,529) =0.0625, RA (640,530) = 3.1875, and calculating the distance-to-offset threshold tr=0.0625 for pixel p (640,530), where |tr-RA (640, 530) |is greater than or equal to 0.3, indicates that the distance-to-offset of pixel p (640,530) is inaccurate, and requires correction.
Step (1) is performed to extract image blocks a1=a (608:672, 498:562) for offset tracking from image element p (640,530) in a, and extract image blocks b1=b (608:672, 498:562) for offset tracking from image element p (640,530) in B, the intensities of image blocks A1, B1 are as shown in fig. 5 (a) and (B).
Step (2) is performed, and a cross correlation coefficient matrix C (520×520) of A1 and B1 is calculated (as shown in (C) of fig. 5), where it can be seen from (C) of fig. 5 that the position of the maximum peak value in the matrix C is far from the center of the matrix, which indicates that the offset of the pixel obtained by using the matrix C is seriously large, and does not conform to the actual situation; extracting a new cross-correlation coefficient matrix c1=c (228:292 ) (as shown in fig. 5 d), it can be seen from fig. 5d that there are multiple peaks in C1, and calculating the mean_c=0.0033 of C1.
Step (3) is executed, the temp=0 is made, the imregionalmax function is used to find all peaks and positions of all peaks of the C1, the peak value with the peak value smaller than or equal to mean_c is removed, only the peak value P with the peak value larger than mean_c and the positions thereof are reserved, the distance in the distance direction between the positions of the reserved 9 peaks and the center of the C1 matrix is calculated to be equal to the distance of the offset 1= [3.1875 ], 0.1875, 0.9375, -4.0625, 3.9375, 2.0625, -0.8125, 3.8125 and-2.3125 ].
Step (6) is performed, the difference diff= [3.125; 0.125; 0.875; 4.125; 3.875; 2; 0.875; 3.75; 2.375] between each offset in offset1 and Tr is calculated, the minimum value of Diff rmin=0.125, the offset corresponding to Rmin is found out, offset 2=0.1875, rmin <0.3, RA (640,530) =offset 2, and then the next pixel p (640,531) is processed as described above.
5) And step 4) is executed on the pixel p (i, j+1) until the offset anomaly correction of the pixel p (m-32, n-32) is completed, so that the anomaly of the offset tracking result caused by a plurality of peaks of the cross correlation coefficient matrix or severe change of the ground surface intensity of the monitoring results of the intensity graphs A and B obtained by using the offset tracking technology can be removed. The corrected deformation diagram of the distance direction is shown in fig. 6.
Claims (7)
1. The pixel offset tracking, monitoring and correcting method is characterized by comprising the following steps:
s1, registering two SAR images shot by the same SAR satellite in a research area, generating a main image and an auxiliary image, and predicting the maximum distance offset r and the maximum azimuth offset a in the research area by using an empirical method;
s2, carrying out offset tracking on the main image and the auxiliary image based on a cross-correlation algorithm, obtaining a cross-correlation coefficient matrix C1 of a pixel p (i, j) based on the constraint of a distance offset r and a direction offset a, and calculating to obtain the offset of the p (i, j) in the distance direction, wherein the offset is as follows:
firstly, a cross-correlation coefficient matrix C of a certain pixel p (i, j) is obtained based on a cross-correlation algorithm, and then a new cross-correlation coefficient matrix C1 is extracted from the C:
；
wherein N is the column number of the cross correlation coefficient matrix C, M is the line number of the cross correlation coefficient matrix C, and N is the oversampling coefficient;
taking the distance between the position of the peak value of the matrix C1 and the center of the matrix C1 in the distance direction as the offset of the pixels p (i, j) in the distance direction;
s3, repeating the step S2 until the offset tracking of the main image and the auxiliary image is completed, and acquiring a distance offset matrix RA of the main image and the auxiliary image;
s4, calculating a distance offset threshold Tr of the pixel p (i, j) based on the following formula:
；
if it isIf the pixel distance is not less than the preset threshold value, the pixel distance at the position (i, j) is not accurate to the offset, and the pixel distance is processed as follows:
acquiring a matrix C1 corresponding to the pixel p (i, j), finding out all peaks in the matrix C1, reserving peaks larger than a preset upper limit value for calculating a new distance offset, and if the absolute value of the difference value between the new distance offset and a distance offset threshold Tr is still larger than or equal to the preset threshold value, extracting the matrix C1 again after replacing the maximum value of the pixel intensity in each image block corresponding to the pixel p (i, j) by using other statistical values, and calculating the distance offset of the pixel p (i, j);
and repeating S4 until the absolute value of the difference value between the new distance offset and the distance offset threshold Tr is smaller than the preset threshold.
2. The method of claim 1, wherein the magnitude of the predetermined threshold is determined based on the accuracy of an offset tracking method.
3. The method of claim 1, wherein the magnitude of the predetermined threshold is a value 3 times the accuracy of the offset tracking method.
4. The method according to claim 1, wherein the preset upper limit value is a mean value of the matrix C1.
5. The method of claim 1, wherein the maximum value of pixel intensities in the image blocks is replaced with a mean value of pixel intensities in each image block.
6. The method according to claim 1, wherein in S4, if there are only 1 peaks remaining, then calculating a new distance offset directly using the peaks, repeating S4 until the absolute value of the difference between the new distance offset and the distance offset threshold Tr is smaller than the preset threshold; if the number of repetitions exceeds the preset repetition upper limit value, the absolute value of the difference between the new distance offset and the distance offset threshold Tr is still greater than or equal to the preset threshold, and the distance offset of the pixel point is made to be 0.
7. The method according to claim 1, wherein in S4, if the number of the reserved peaks exceeds 1, the distance offset between each peak and the center of the C1 matrix in the distance direction is calculated n To make |Tr-offset n Minimum offset n As a new distance vector offset.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101815227A (en) * | 2009-02-19 | 2010-08-25 | 索尼株式会社 | Image processing equipment and method |
| JP2016134908A (en) * | 2015-01-22 | 2016-07-25 | キヤノン株式会社 | Imaging apparatus and control method thereof |
| CN106066478A (en) * | 2016-05-27 | 2016-11-02 | 中国矿业大学 | Merge pixel skew to follow the tracks of and the mining area surface deformation calculation method of short baseline set |
| CN109829874A (en) * | 2019-01-30 | 2019-05-31 | 西安电子科技大学 | SAR image fusion method based on Frame Theory |
| CN112068136A (en) * | 2020-09-14 | 2020-12-11 | 广东省核工业地质局测绘院 | An Azimuth Deformation Monitoring Method Based on Amplitude Offset |
| CN113160288A (en) * | 2021-03-22 | 2021-07-23 | 广东电网有限责任公司广州供电局 | SAR image registration method based on feature points |
| CN115097450A (en) * | 2022-06-27 | 2022-09-23 | 长安大学 | Cross-track high-resolution three-SAR (synthetic aperture radar) offset large-gradient landslide deformation estimation method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8369579B2 (en) * | 2006-12-21 | 2013-02-05 | Massachusetts Institute Of Technology | Methods and apparatus for 3D surface imaging using active wave-front sampling |
| JP2010071922A (en) * | 2008-09-22 | 2010-04-02 | Yamatake Corp | Apparatus for measuring three-dimensional position |
| JP5792662B2 (en) * | 2011-03-23 | 2015-10-14 | シャープ株式会社 | Parallax calculation device, distance calculation device, and parallax calculation method |
| JP6234087B2 (en) * | 2013-07-05 | 2017-11-22 | キヤノン株式会社 | Distance detection device and distance detection method |
-
2022
- 2022-11-07 CN CN202211383151.0A patent/CN115712118B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101815227A (en) * | 2009-02-19 | 2010-08-25 | 索尼株式会社 | Image processing equipment and method |
| JP2016134908A (en) * | 2015-01-22 | 2016-07-25 | キヤノン株式会社 | Imaging apparatus and control method thereof |
| CN106066478A (en) * | 2016-05-27 | 2016-11-02 | 中国矿业大学 | Merge pixel skew to follow the tracks of and the mining area surface deformation calculation method of short baseline set |
| CN109829874A (en) * | 2019-01-30 | 2019-05-31 | 西安电子科技大学 | SAR image fusion method based on Frame Theory |
| CN112068136A (en) * | 2020-09-14 | 2020-12-11 | 广东省核工业地质局测绘院 | An Azimuth Deformation Monitoring Method Based on Amplitude Offset |
| CN113160288A (en) * | 2021-03-22 | 2021-07-23 | 广东电网有限责任公司广州供电局 | SAR image registration method based on feature points |
| CN115097450A (en) * | 2022-06-27 | 2022-09-23 | 长安大学 | Cross-track high-resolution three-SAR (synthetic aperture radar) offset large-gradient landslide deformation estimation method |
Non-Patent Citations (1)
| Title |
|---|
| 利用Landsat 8和TerraSAR-X影像研究老虎沟12号冰川运动特征;张晓博;赵学胜;葛大庆;刘斌;张玲;李曼;王艳;;遥感学报(第01期);全文 * |
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