Urban and rural land dynamic evaluation system based on satellite remote sensing monitoring

Abstract

The invention discloses an urban and rural land dynamic evaluation system based on satellite remote sensing monitoring, relates to the technical field of land monitoring, solves the problem of low accuracy recognition when a change area is recognized during dynamic evaluation of an original remote sensing image, and provides a dynamic evaluation method for the urban and rural land based on satellite remote sensing monitoring through feature vector comparison (standard vector and abnormal vector distinguishing). The contour partition corresponding to the abnormal vector is marked as a'change partition ', the partition disappearance interference caused by natural factors (such as cloud layer coverage) can be eliminated in combination with the limitation of an image acquisition period within three days, and a real land use change area is accurately positioned. A standard partition is introduced as a reference, and quantitative analysis of spatial position and morphological difference of a change area is realized through vector set translation coincidence verification of front and back images.

Description

Technical Field

[0001] The present invention relates to the technical field of land use monitoring, and in particular to a dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring. Background Art

[0002] As the core carrier of urban space expansion and rural development, the dynamic changes of urban and rural land directly reflect the regional economic and social development, ecological protection and planning implementation. With the acceleration of urbanization, the scarcity of land resources and the complexity of development and utilization are becoming increasingly prominent. Traditional monitoring methods such as manual inspections and low-altitude aerial photography are difficult to meet the needs of refined management of national land space due to their limited coverage, lack of timeliness and high labor costs.

[0003] Traditional remote sensing image processing relies on manual threshold setting or a single algorithm (such as simple grayscale conversion), which is difficult to adapt to the spectral characteristics of different objects. This leads to rough contour zoning and center point positioning errors, affecting the accuracy of subsequent dynamic comparison. For example, existing methods often misjudge the boundaries of mixed urban and rural land (such as urban villages and industrial parks) due to spectral confusion, requiring repeated manual corrections, which is time-consuming and labor-intensive.

[0004] Existing systems are mostly based on pixel-level difference analysis or simple geometric feature comparison, which are easily affected by noise such as cloud cover and lighting changes, resulting in a high false alarm rate. For example, geometric deformation caused by differences in shooting angles in images taken during adjacent time periods may be mistaken for changes in land use type. However, the detection sensitivity of small-scale changes (such as new illegal construction and abandoned farmland) is insufficient, making it difficult to meet the regulatory requirements of "early detection and early disposal";

[0005] In summary, the existing remote sensing monitoring technology for urban and rural land use has significant shortcomings in terms of automated processing, high-precision identification, refined positioning, and comprehensive decision-making support. There is an urgent need to develop a dynamic assessment system that integrates efficient image processing, robust change detection, sub-pixel level anomaly positioning, and multi-dimensional data fusion to improve the scientificity, timeliness, and application value of urban and rural land use monitoring. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring, which solves the problem of low accuracy in identifying changed areas when performing dynamic assessment using original remote sensing images.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring, comprising:

[0008] The image acquisition terminal acquires high-definition remote sensing images associated with designated urban and rural land areas;

[0009] On the feature processing side, the remote sensing high-definition image is gray-scaled, and the gray-scale image is confirmed. Then, based on the grayscale values ​​associated with different grayscale points in the gray-scale image, the gradient pixels in the gray-scale image are confirmed. Then, based on the confirmed groups of gradient pixels, the gray-scale image is divided into multiple contour partitions, and based on the overall edge contour of each contour partition, the partition center point of the corresponding contour partition is confirmed. The specific method is as follows:

[0010] No processing is performed on the outermost pixel points in the grayscale image. The vertical gradient and vertical gradient of the pixel points in the grayscale image are confirmed using the Sobel algorithm. Confirm the integrated gradient associated with the corresponding pixel point, and calibrate the pixel point that satisfies the following conditions: integrated gradient ≥ Y1 as a gradient pixel point, and its Y1 is the preset value; otherwise, no calibration is performed;

[0011] Based on the multiple groups of gradient pixel points confirmed in the grayscale image, adjacent gradient pixel points are identified from the multiple groups of gradient pixel points, and it is determined whether the corresponding gradient pixel points form a closed loop contour. If so, the area included in the corresponding closed loop contour is recorded as a group of contour partitions. If not, no marking is performed.

[0012] Confirm the center point associated with each contour partition, combine the closed-loop contour outside the corresponding contour partition with the two-dimensional coordinate system, confirm the two-dimensional coordinates associated with different contour points within the corresponding closed-loop contour, then average several groups of two-dimensional coordinates to confirm the mean coordinates, and based on the confirmed mean coordinates, confirm the mean point position in the two-dimensional coordinate system and mark it in the corresponding contour partition as the center point associated with the corresponding contour partition. After completing the feature processing, the remote sensing high-definition image is transmitted to the database for storage;

[0013] The dynamic monitoring processing end verifies the remote sensing high-definition images after feature processing to identify whether there are dynamic changes in the features corresponding to the remote sensing high-definition images associated with the adjacent time periods. The specific method is as follows:

[0014] Confirm the timestamps of the stored remote sensing high-definition images, identify two groups of remote sensing high-definition images with adjacent timestamps, and record them as images to be compared;

[0015] Based on the multiple contour partitions determined in the corresponding image to be compared, the center points marked in the contour partitions are identified, several center points are connected, and the polygon with the largest area formed by the several center points is determined. The edge contour of the polygon is combined with the two-dimensional coordinate system to determine the two-dimensional coordinates of different contour points. Then, the several two-dimensional coordinates are averaged and the average coordinates are determined. In this way, the center point of the polygon is locked and recorded as the feature point of the corresponding image to be compared;

[0016] Identify whether the feature points associated with the two sets of images to be compared are located at the same position. If not, generate a dynamic change signal and transmit the generated dynamic change signal to the associated partition verification end. If so, it means that the two sets of images to be compared associated with the previous and next timestamps have not changed;

[0017] The associated partition verification terminal dynamically verifies the two sets of remote sensing high-definition images based on the generated dynamic change signal, identifies and confirms the changed partition, and then verifies the changed partition forward and backward to lock the abnormal change area within the changed partition. The specific method is as follows:

[0018] Based on the generated dynamic change signal, the first image in the two sets of images to be compared is recorded as the front image, and the second image is recorded as the back image. Based on the feature points marked in the front image and the center points marked by the internal contour partitions, starting from the feature points, the feature vectors associated with the corresponding feature points and different center points are determined;

[0019] Move the feature points associated with the front image to the back image as the feature points of the back image, and use the same feature vector determination method to verify and compare the feature vectors associated with the front and back images. The feature vectors that match the comparison are recorded as standard vectors, and the feature vectors that do not match the comparison are recorded as abnormal vectors.

[0020] The center point associated with the abnormal vector is recorded as the abnormal point, and the contour partition where the abnormal point is located is recorded as the changed partition. Based on the comparison process, the contour partition where the corresponding abnormal point is located in the previous image is confirmed, and the corresponding contour partition is recorded as the standard partition.

[0021] Preferably, the specific method of confirming the abnormal region by the associated partition verification terminal is:

[0022] Based on the determined change partition and standard partition, the center points existing in the change partition and the standard partition are recorded as built-in points;

[0023] Identify the pixels associated with the built-in point and record the pixels at the four corners of the built-in point as feature pixels. Construct a line connecting the feature pixel and the built-in point and extend it by five pixels. Mark different lines based on the specific extension direction associated with the corresponding line.

[0024] Confirm and record the pixel difference values ​​associated with the pixels at the same sorted position on the adjacent lines. If the pixel difference value is ≥ 0, move the built-in point in the change partition and rotate it during the movement process to identify whether the line features associated with the corresponding built-in point are consistent with the line features confirmed in the standard partition. If there is a consistent related process, the position of the corresponding built-in point is recorded as the standard position. Otherwise, continue to confirm until the position of the built-in point is confirmed;

[0025] The built-in points at the standard position are overlapped with the built-in points of the standard partition, and the angle calibration is performed according to the confirmed connection direction. The area that does not overlap between the standard partition and the change partition is recorded as the mutation area.

[0026] Preferably, it also includes:

[0027] The marking display terminal marks and displays the abnormal area for review by external relevant personnel.

[0028] The present invention provides a dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring. Compared with the existing technology, it has the following advantages:

[0029] This method uses feature vector comparison (distinguishing between standard vectors and abnormal vectors) to mark the contour partitions corresponding to abnormal vectors as "changed partitions." Combined with the three-day image acquisition cycle, this method can eliminate the interference caused by natural factors (such as cloud cover) that can cause partition disappearance and accurately locate areas of actual land use changes. By introducing "standard partitions" as a reference benchmark and performing a translation and overlap check of the vector sets of the previous and next images, the spatial position and morphological differences of the changed areas can be quantitatively analyzed.

[0030] Based on the eight-directional characteristic pixel point line extension algorithm of built-in points (extending five pixel points and recording the difference between adjacent line pixels), combined with built-in point movement and rotation verification, it can identify millimeter-level pixel differences within the changed partition, and the positioning accuracy reaches the sub-pixel level (≤1 pixel); through angle calibration and coincidence analysis, the non-overlapping areas between the standard partition and the changed partition are marked as "abnormal areas", which intuitively display details such as land use increase and decrease, shape changes, etc., providing centimeter-level precision decision-making basis for land resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] First embodiment

[0034] See also Figure 1 The present application provides a dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring, comprising an image acquisition end, a feature processing end, a database, a dynamic monitoring processing end, an associated partition verification end, and a mark display end, wherein the image acquisition end is electrically connected to an input node of the feature processing end, and the feature processing end is electrically connected to the database or the dynamic monitoring processing end input node, and the database, the dynamic monitoring processing end, the associated partition verification end, and the mark display end are electrically connected in sequence from the output node to the input node;

[0035] The image acquisition end acquires high-definition remote sensing images associated with designated urban and rural land areas (at the time of acquisition, there is an acquisition authority, and the designated urban and rural land areas are determined in advance by relevant personnel, and the image is confirmed through inherent authority. This part of the acquisition process is operated and selected by relevant operators), and transmits the acquired high-definition remote sensing images to the feature processing end;

[0036] Among them, the feature processing end performs grayscale processing on the remote sensing high-definition image, confirms the grayscale image, and then confirms the gradient pixel points in the grayscale image based on the grayscale values ​​associated with different grayscale points in the grayscale image. Then, based on the confirmed groups of gradient pixel points, the grayscale image is divided into multiple contour partitions, and based on the overall edge contour of each contour partition, the partition center point of the corresponding contour partition is confirmed. Specifically, different image points in each remote sensing high-definition image need to be grayscale processed to confirm the corresponding grayscale point. The RGB value associated with each point is different. When the corresponding RGB value of the corresponding point is confirmed, different RGB values ​​are associated with different weight factors to confirm the grayscale value associated with the corresponding point. Therefore, the remote sensing high-definition image can be converted into the corresponding grayscale image based on the different grayscale values ​​associated with the corresponding grayscale point, and its grayscale value is proposed to be R×0.299+G×0.557+B×0.144;

[0037] The specific method for confirming the groups of gradient pixel points in the grayscale image is as follows:

[0038] The outermost pixel points in the grayscale image are not processed in any way. The Sobel algorithm is used to confirm the vertical gradient and vertical gradient of the pixel points in the grayscale image (combining the preset weight factor and the pixel value associated with the corresponding pixel point, the convolution operation is performed to confirm the corresponding horizontal gradient and vertical gradient). Confirm the integrated gradient associated with the corresponding pixel point, and calibrate the pixel point that satisfies the following conditions: integrated gradient ≥ Y1 as a gradient pixel point, where Y1 is a preset value, and its specific value is determined by the operator based on experience. Otherwise, no calibration is performed;

[0039] Based on the multiple groups of gradient pixel points confirmed in the grayscale image, adjacent gradient pixel points are identified from the multiple groups of gradient pixel points, and it is determined whether the corresponding gradient pixel points form a closed loop contour. If so, the area included in the corresponding closed loop contour is recorded as a group of contour partitions. If not, no marking is performed.

[0040] The center point associated with each contour partition is confirmed, the closed-loop contour outside the corresponding contour partition is combined with the two-dimensional coordinate system, and the two-dimensional coordinates associated with different contour points in the corresponding closed-loop contour are confirmed. Then, several groups of two-dimensional coordinates are averaged to confirm the mean coordinates. Based on the confirmed mean coordinates, the mean point position is confirmed in the two-dimensional coordinate system and calibrated in the corresponding contour partition as the center point associated with the corresponding contour partition. The high-definition remote sensing image after feature processing is transmitted to the database for storage.

[0041] Specifically, each different contour partition has a set of edge contours. Then, combined with the corresponding two-dimensional coordinate system, the two-dimensional coordinates associated with the corresponding contour points can be confirmed. Then, based on several contour points associated with the corresponding contour, the two-dimensional coordinates associated with several contour points are averaged, and the corresponding mean coordinates can be locked. The corresponding mean coordinates are the center point of the corresponding contour, that is, the center point inside the corresponding contour partition.

[0042] The dynamic monitoring processing end verifies the remote sensing high-definition images after feature processing to identify whether there are dynamic changes in the features corresponding to the remote sensing high-definition images associated with the adjacent time periods. The specific processing method for identification is as follows:

[0043] Confirm the timestamps of the stored remote sensing high-definition images, identify two groups of remote sensing high-definition images with adjacent timestamps, and record them as images to be compared;

[0044] Based on the multiple contour partitions determined in the corresponding image to be compared, the center points marked in the contour partitions are identified, several center points are connected, and the polygon with the largest area formed by the several center points is determined. The edge contour of the polygon is combined with the two-dimensional coordinate system to determine the two-dimensional coordinates of different contour points. Then, the several two-dimensional coordinates are averaged and the average coordinates are determined. In this way, the center point of the polygon is locked and recorded as the feature point of the corresponding image to be compared;

[0045] Identify whether the feature points associated with the two sets of images to be compared are located in the same position. If so, it means that the two sets of images to be compared associated with the previous and next timestamps have not changed. If not, a dynamic change signal is generated and the generated dynamic change signal is transmitted to the associated partition verification end. Specifically, when identifying whether they are located in the same position, the two images to be compared can be overlapped. When shooting, there are corresponding feature points. The overlap is performed based on the positions of the corresponding feature points to identify whether the two feature points are located in the same position. Alternatively, from the corresponding polygon, the vector feature between the corresponding center point and the inflection point of the polygon is confirmed, and then an overlap check is performed to identify whether all the vector features are consistent. If they are consistent, it means that they are located in the same position. Otherwise, it means that they are not in the same position.

[0046] Specifically, when the corresponding feature points change, it means that there is a confirmation difference in the confirmation of the entire contour partition of the corresponding image. Then, there is a dynamic change in the two sets of images associated before and after. Then, it is necessary to generate a corresponding dynamic change signal, and perform partition verification through the subsequent associated partition verification end to identify the partition with specific changes, so as to perform dynamic evaluation and display for relevant personnel to view.

[0047] The associated partition verification terminal dynamically verifies the two sets of remote sensing high-definition images based on the generated dynamic change signal, identifies and confirms the changed partition, and then verifies the changed partition forward and backward to lock the abnormal change area within the changed partition;

[0048] The specific method for identifying and confirming the changed partition is as follows:

[0049] Based on the generated dynamic change signal, the first image in the two sets of images to be compared is recorded as the front image, and the second image is recorded as the back image. Based on the feature points marked in the front image and the center points marked by the internal contour partitions, starting from the feature points, the feature vectors associated with the corresponding feature points and different center points are determined;

[0050] Move the feature points associated with the front image to the back image as the feature points of the back image, and use the same feature vector determination method to verify and compare the feature vectors associated with the front and back images (translate the two confirmed vector sets so that the same feature points overlap, so as to assess completely consistent consistency vectors). The feature vectors that are consistent with the comparison are recorded as standard vectors, and the feature vectors that are inconsistent with the comparison are recorded as abnormal vectors;

[0051] The center point associated with the abnormal vector is recorded as the abnormal point, and the contour partition where the abnormal point is located is recorded as the changed partition. Based on the comparison process, the contour partition where the corresponding abnormal point is located in the previous image is determined, and the corresponding contour partition is recorded as the standard partition.

[0052] Specifically, the acquisition cycle associated with its remote sensing imagery generally does not exceed 3 days. That is, within the corresponding 3 days, the corresponding single contour partition will not disappear because it occupies a large area. Therefore, the above-mentioned specific processing method can be used to confirm the relevant contour partitions with regional changes, thereby simultaneously confirming the corresponding change partitions and standard partitions, and comparing and verifying the confirmed change partitions and standard partitions. From the corresponding comparison and verification process, the specific missing areas can be confirmed, and dynamic verification can be performed to confirm the dynamic missing areas and make relevant displays.

[0053] The specific method for confirming the abnormal area within the change zone is as follows:

[0054] Based on the determined change partition and standard partition, the center points in the change partition and the standard partition are recorded as built-in points (different partitions have corresponding center points during the processing process, and the specific calibration of the corresponding center points can be completed through the processing process of the feature processing end);

[0055] Confirm the pixels associated with the built-in point (eight groups of pixels, each located at a different position of the built-in point), and record the pixels at the four corners of the built-in point as feature pixels (that is, eight groups of pixels are located at the four upper, lower, left, and right positions of the built-in point, and there are four corner positions, so the pixels associated with the four corner positions are the feature pixels that need to be found), construct a line connecting the feature pixel and the built-in point, and extend it so that the corresponding line extends by five pixels, that is, the end pixel of the corresponding line is five pixels away from the built-in point, and mark different lines according to the specific extension direction associated with the corresponding line (upper left, upper right, lower left, lower right. When confirming the feature pixel, the corresponding extension direction can be confirmed simultaneously).

[0056] The pixel difference values ​​associated with the pixel points at the same sorting position on the adjacent lines are confirmed and recorded, and the pixel difference value is ≥0. Taking the feature pixel points as an example, on the two adjacent groups of lines, the point position sorted in the first sorting position of the outer circle of the built-in points is the pixel point at the same sorting position, and so on. The corresponding sorting positions can be gradually determined outward, so that the built-in points are moved in the change partition and rotated during the movement process. It is identified whether the line features associated with the corresponding built-in points are consistent with the line features confirmed in the standard partition. If there is a consistent related process, the position of the corresponding built-in point is recorded as the standard position. Otherwise, the confirmation is continued until the position of the built-in point is confirmed;

[0057] Overlap the built-in points at the standard position with the built-in points of the standard partition, and perform angle calibration based on the confirmed connection line orientation. The area that does not overlap between the standard partition and the change partition is recorded as the mutation area, and the mutation area is marked and displayed through the marking display terminal for review by external relevant personnel.

[0058] Specifically, compared with the standard partition, when the corresponding change partition is changed, it is unclear which part of the area is missing. Therefore, in order to make a specific confirmation, it is necessary to confirm that the two partitions have the same part. Therefore, by confirming the built-in points and adjacent lines, the specific difference associated with the corresponding pixel points on the corresponding line is identified. Therefore, according to the specific difference between the corresponding differences and the specific rotation process, it is possible to quickly confirm that the two partitions have the same area and perform synchronous verification to perform coincidence verification, achieve a better coincidence verification process, and perform specific calibration of the abnormal area.

[0059] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0060] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A dynamic assessment system for urban and rural land use based on satellite remote sensing monitoring, characterized by: include: The image acquisition terminal acquires high-definition remote sensing images associated with designated urban and rural land areas; The feature processing end grayscales the remote sensing high-definition image, confirms the grayscale image, and then confirms the gradient pixels in the grayscale image based on the grayscale values ​​associated with different grayscale points in the grayscale image. Then, based on the confirmed groups of gradient pixels, the grayscale image is divided into multiple contour partitions, and based on the overall edge contour of each contour partition, the partition center point of the corresponding contour partition is confirmed; The dynamic monitoring processing end verifies the remote sensing high-definition images after feature processing to identify whether there are dynamic changes in the features corresponding to the remote sensing high-definition images associated with the adjacent time periods; The associated partition verification end dynamically verifies the two sets of remote sensing high-definition images based on the generated dynamic change signal, identifies and confirms the changed partition, and then verifies the changed partition front and back to lock the abnormal area within the changed partition.

2. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 1 is characterized in that: The feature processing end confirms the groups of gradient pixel points in the grayscale image in the following specific manner: No processing is performed on the outermost pixel points in the grayscale image. The vertical gradient and vertical gradient of the pixel points in the grayscale image are confirmed using the Sobel algorithm. Confirm the integrated gradient associated with the corresponding pixel point, and calibrate the pixel point that satisfies the following conditions: integrated gradient ≥ Y1 as a gradient pixel point, and its Y1 is the preset value; otherwise, no calibration is performed; Based on the multiple groups of gradient pixel points confirmed in the grayscale image, adjacent gradient pixel points are identified from the multiple groups of gradient pixel points, and it is determined whether the corresponding gradient pixel points form a closed loop contour. If so, the area included in the corresponding closed loop contour is recorded as a group of contour partitions. If not, no marking is performed. The center point associated with each contour partition is confirmed, the closed-loop contour outside the corresponding contour partition is combined with the two-dimensional coordinate system, and the two-dimensional coordinates associated with different contour points in the corresponding closed-loop contour are confirmed. Then, several groups of two-dimensional coordinates are averaged to confirm the mean coordinates. Based on the confirmed mean coordinates, the mean point position is confirmed in the two-dimensional coordinate system and calibrated in the corresponding contour partition as the center point associated with the corresponding contour partition. The high-definition remote sensing image after feature processing is transmitted to the database for storage.

3. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 1 is characterized in that: The dynamic monitoring processing end identifies whether there is a dynamic change in the remote sensing high-definition images associated with the adjacent time periods in the following specific manner: Confirm the timestamps of the stored remote sensing high-definition images, identify two groups of remote sensing high-definition images with adjacent timestamps, and record them as images to be compared; Based on the multiple contour partitions determined in the corresponding image to be compared, the center points marked in the contour partitions are identified, several center points are connected, and the polygon with the largest area formed by the several center points is determined. The edge contour of the polygon is combined with the two-dimensional coordinate system to determine the two-dimensional coordinates of different contour points. Then, the several two-dimensional coordinates are averaged and the average coordinates are determined. In this way, the center point of the polygon is locked and recorded as the feature point of the corresponding image to be compared; It is determined whether the feature points associated with the two sets of images to be compared are located at the same position. If not, a dynamic change signal is generated and the generated dynamic change signal is transmitted to the associated partition verification end.

4. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 3 is characterized in that: If the feature points associated with the two sets of images to be compared are located at the same position, it means that the two sets of images to be compared associated with the previous and next timestamps have not changed.

5. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 1 is characterized in that: The specific method for the associated partition verification terminal to identify and confirm the changed partition is: Based on the generated dynamic change signal, the first image in the two sets of images to be compared is recorded as the front image, and the second image is recorded as the back image. Based on the feature points marked in the front image and the center points marked by the internal contour partitions, starting from the feature points, the feature vectors associated with the corresponding feature points and different center points are determined; Move the feature points associated with the front image to the back image as the feature points of the back image, and use the same feature vector determination method to verify and compare the feature vectors associated with the front and back images. The feature vectors that match the comparison are recorded as standard vectors, and the feature vectors that do not match the comparison are recorded as abnormal vectors. The center point associated with the abnormal vector is recorded as the abnormal point, and the contour partition where the abnormal point is located is recorded as the changed partition. Based on the comparison process, the contour partition where the corresponding abnormal point is located in the previous image is confirmed, and the corresponding contour partition is recorded as the standard partition.

6. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 5 is characterized in that: The specific method for the associated partition verification terminal to confirm the abnormal area is: Based on the determined change partition and standard partition, the center points existing in the change partition and the standard partition are recorded as built-in points; Identify the pixels associated with the built-in point and record the pixels at the four corners of the built-in point as feature pixels. Construct a line connecting the feature pixel and the built-in point and extend it by five pixels. Mark different lines based on the specific extension direction associated with the corresponding line. Confirm and record the pixel difference values ​​associated with the pixels at the same sorted position on the adjacent lines. If the pixel difference value is ≥ 0, move the built-in point in the change partition and rotate it during the movement process to identify whether the line features associated with the corresponding built-in point are consistent with the line features confirmed in the standard partition. If there is a consistent related process, the position of the corresponding built-in point is recorded as the standard position. Otherwise, continue to confirm until the position of the built-in point is confirmed; The built-in points at the standard position are overlapped with the built-in points of the standard partition, and the angle calibration is performed according to the confirmed connection direction. The area that does not overlap between the standard partition and the change partition is recorded as the mutation area.

7. The urban and rural land use dynamic assessment system based on satellite remote sensing monitoring according to claim 1 is characterized in that: Also includes: The marking display terminal marks and displays the abnormal area for review by external relevant personnel.

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