InSAR settlement result image processing method and device, readable medium and electronic equipment

Abstract

The invention belongs to the technical field of image processing, and discloses an InSAR settlement result image processing method and device, a readable medium and electronic equipment. According to the method, the settlement monitoring precision can be effectively improved, more detail information is reserved, the data continuity is ensured, the processing efficiency and applicability are improved, and more reliable technical support is provided for geological disaster monitoring and urban sustainable development.

Description

Technical Field

[0001] This application relates to the field of image processing, and particularly to an InSAR settlement result image processing method, apparatus, readable medium, and electronic device. Background Art

[0002] With the development of technology, InSAR (Interferometric Synthetic Aperture Radar) technology has been widely used in fields such as surface settlement monitoring. However, there are some limitations in the existing InSAR settlement result data processing methods. The traditional global detrending method is difficult to effectively eliminate the trend term in the non-uniform settlement area and is prone to loss of local detail information. At the same time, the method of manually selecting the detrending area is not only inefficient but also highly subjective, affecting the accuracy and reliability of settlement monitoring. Therefore, there is an urgent need for a new InSAR settlement result image processing method to improve the accuracy and efficiency of settlement monitoring and better meet the needs in aspects such as geological disaster monitoring and urban sustainable development. Summary of the Invention

[0003] This application provides an InSAR settlement result image processing method, apparatus, readable medium, and electronic device to improve the accuracy and efficiency of InSAR settlement monitoring.

[0004] In a first aspect, this application provides an InSAR settlement result image processing method, and the method includes: Calculating a first settlement gradient and a second settlement gradient of the InSAR settlement result image; wherein, the first settlement gradient is the settlement gradient in the horizontal direction, and the second settlement gradient is the settlement gradient in the vertical direction; Dividing the InSAR settlement result image into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image; For each image region, using a preset linear model to determine the trend information of the image region, and using the trend information of the image region to perform detrending information processing on the image region to obtain a processed image region; Stitching the processed image regions together, and performing fusion processing on the boundaries between the processed image regions to obtain a processed InSAR settlement result image.

[0005] In a second aspect, this application provides an InSAR settlement result image processing apparatus, and the apparatus includes: A first unit for calculating a first settlement gradient and a second settlement gradient of the InSAR settlement result image; wherein, the first settlement gradient is the settlement gradient in the horizontal direction, and the second settlement gradient is the settlement gradient in the vertical direction; A second unit, configured to divide the InSAR settlement result image into a plurality of image regions according to a first settlement gradient and a second settlement gradient of the InSAR settlement result image; A third unit, configured to, for each image region, determine trend information of the image region by using a preset linear model, and perform detrending processing on the image region by using the trend information of the image region to obtain a processed image region; A fourth unit, configured to splice the processed image regions and perform fusion processing on boundaries between the processed image regions to obtain a processed InSAR settlement result image.

[0006] In a third aspect, the present application provides a readable medium, including execution instructions. When a processor of an electronic device executes the execution instructions, the electronic device executes the method according to any one of the first aspect.

[0007] In a fourth aspect, the present application provides an electronic device, including a processor and a memory storing execution instructions. When the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of the first aspect.

[0008] It can be seen from the above technical solutions that the present application has the following beneficial effects compared with the prior art: 1. Improve settlement monitoring accuracy: By dividing the InSAR image into image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image, targeted detrending processing can be performed according to the settlement characteristics of different image regions. Compared with the traditional global detrending method, it can more effectively eliminate the trend term, reduce the influence of systematic errors such as atmospheric delay and orbital error on the settlement result, thereby improving the accuracy of settlement monitoring and providing more reliable data support for geological disaster monitoring, etc.

[0009] 2. Retain more detailed information: Avoid the problem of loss of local detailed information caused by global detrending, so that the local settlement characteristics of each image region can be better retained, which helps to more comprehensively and accurately understand the subtle changes in the surface settlement situation and is of great significance for analyzing the settlement cause and predicting the settlement development trend.

[0010] 3. Ensure data continuity: After performing detrending processing on the image region by using the trend information of the image region, fusion processing is performed on the boundaries between the processed image regions, which can effectively eliminate the traces of the boundaries of the spliced processed image regions, ensure the continuity and integrity of the entire settlement result data, make the finally obtained settlement result smoother and more natural, conform to the actual situation, and facilitate subsequent data analysis and application.

[0011] The further effects of the above-mentioned non-conventional preferred manners will be described below in conjunction with specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0013] Figure 1 It is a schematic flow chart of a method for processing InSAR settlement result images provided by the present application; Figure 2 It is a schematic flow chart of calculating a first settlement gradient and a second settlement gradient provided by the present application; Figure 3 It is a schematic flow chart of calculating a first settlement gradient and a second settlement gradient provided by the present application; Figure 4 It is a schematic flow chart of calculating a first settlement gradient and a second settlement gradient provided by the present application; Figure 5 It is a schematic flow chart of calculating a first settlement gradient and a second settlement gradient provided by the present application; Figure 6 It is a schematic flow chart of calculating a first settlement gradient and a second settlement gradient provided by the present application; Figure 7 It is a schematic structural diagram of an InSAR settlement result image processing device provided by the present application; Figure 8 It is a schematic structural diagram of an electronic device provided by the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] To make the objectives, technical solutions, and advantages of the present application clearer, the following will clearly and completely describe the technical solutions of the present application in conjunction with specific embodiments and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0015] The following will, in conjunction with the drawings, detail various non-restrictive embodiments of the present application.

[0016] See Figure 1, which shows an InSAR settlement result image processing method in an embodiment of the present application. In this embodiment, the method may, for example, include the following steps: S101: Calculate the first settlement gradient and the second settlement gradient of the InSAR settlement result image.

[0017] Among them, the first settlement gradient is the settlement gradient in the horizontal direction, that is, the settlement gradient of the settlement result in the x horizontal direction; the second settlement gradient is the settlement gradient in the vertical direction, that is, the settlement gradient of the settlement result in the y vertical direction. It can be understood that the first settlement gradient and the second settlement gradient of the InSAR settlement result image include the first settlement gradient and the second settlement gradient of each pixel point in the InSAR settlement result image.

[0018] In one implementation, the Sobel operator or the Prewitt operator can be used to calculate the first settlement gradient and the second settlement gradient of each pixel point in the InSAR settlement result image. That is, first use the Sobel operator or the Prewitt operator to calculate the settlement gradients of the InSAR image in the horizontal and vertical directions. As an example, assume that the operator used is the Sobel operator, and each operator is a 3×3 window. Slide this window in the InSAR settlement result image, from left to right, from top to bottom, moving one pixel each time. Multiply the values of the window by the pixel values of the InSAR settlement result image first, and then add up all the products. The sum of the additions is used as the pixel value at the pixel point position in the center of the window.

[0019] The gradient in the X direction in the Sobel operator is: -1 0 1 -2 0 2 -1 0 1 The gradient in the Y direction in the Sobel operator is: -1 -2 -1 0 0 0 1 2 1 The gradient in the X direction in the Prewitt operator is: -1 0 1 -1 0 1 -1 0 1 The gradient in the Y direction in the Prewitt operator is: -1 -1 -1 0 0 0 1 1 1 After calculation, each pixel point on the settlement result has two values, which are the gradient values in the X direction and the Y direction respectively.

[0020] Next, for example, assume that the pixel values of the InSAR settlement result image are as Figure 1 shown. After passing through the sobel operator for calculation, the result as Figure 3 shown is obtained. Then, slide the window one pixel point to the right, and the result as Figure 4 shown is obtained. Then, from Figure 3Slide the window downward by one pixel from the position of, and obtain Figure 5 the result shown. Immediately afterwards, slide the window to the right by one pixel from the position of Figure 5 and obtain Figure 6 the result shown. Thus, the first settlement gradient and the second settlement gradient of the InSAR settlement result image are obtained (as shown in Table 1 below). There are two values at each position, namely the gradient in the horizontal row direction and the gradient in the vertical direction. At the edge positions of the InSAR settlement result image, it can be set to (0, 0) or NaN.

[0021] Table 1 NaN NaN NaN NaN NaN （8，32） （8，32） NaN NaN （8，32） （12，26） NaN NaN NaN NaN NaN S102: Divide the InSAR settlement result image into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image.

[0022] In this embodiment, the InSAR settlement result image can be divided into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image.

[0023] Specifically, for each pixel point in the InSAR settlement result image, a feature vector of the pixel point can be generated according to the first settlement gradient and the second settlement gradient of the pixel point. That is to say, for each pixel point (i, j), combine its horizontal gradient gx[i][j] and vertical gradient gy[i][j] into a feature vector: features[i][j] = [gx[i][j], gy[i][j]].

[0024] Then, the feature vectors of all pixel points in the InSAR settlement result image can be clustered based on the K-means clustering algorithm to obtain a clustering result; where the clustering result is K clusters. K-means is an iterative clustering algorithm. Its goal is to divide the data set into K clusters, so that each data point belongs to the cluster closest to it, and the centroid of each cluster is the mean value of all data points in the cluster. The specific algorithm steps are as follows: (1) Select the value of K: Determine how many clusters to divide the data into; (2) Select the value of K: Determine how many clusters to divide the data into; (3) For each data point in the data set, calculate its distances from the K centroids; (4) Assign each data point to the cluster where the centroid closest to it is located; (5) For each cluster, recalculate its center point, which is the mean of all data points in the cluster; (6) Repeat steps (3), (4), and (5) until the movement of the center point is less than a certain threshold.

[0025] Then, according to the clustering result, the clustering effect index corresponding to the clustering result can be determined, and, according to the clustering effect index corresponding to the clustering result, the clustering result can be optimized to obtain an optimized clustering result. The clustering effect index is one of the following: Silhouette Coefficient, Calinski-Harabasz index.

[0026] The Silhouette Coefficient is an index for evaluating the clustering effect, which comprehensively considers the cohesion and separation of clusters. For each data point i, the calculation formula for its Silhouette coefficient si is as follows: si = (bi - ai) / max(ai, bi) where ai is the average distance between data point i and other data points in its cluster, and bi is the minimum average distance between data point i and all data points in other clusters.

[0027] The value range of the Silhouette coefficient is [-1, 1]. The closer the Silhouette coefficient is to 1, the better the clustering effect; the closer the Silhouette coefficient is to -1, the worse the clustering effect; the Silhouette coefficient close to 0 indicates that the clustering effect is average.

[0028] The Calinski-Harabasz index, also known as the Variance Ratio Criterion, evaluates the clustering effect by calculating the ratio of the between-cluster dispersion and the within-cluster dispersion. The calculation formula for the Calinski-Harabasz index is as follows: CH = (SSB / (K - 1)) / (SSW / (N - K)) SSB is the between-cluster dispersion, which represents the weighted sum of squared distances between the center points of all clusters and the center point of the entire data set; SSW is the within-cluster dispersion, which represents the sum of squared distances between each data point and the center point of its cluster; K is the number of clusters; N is the number of data points. The larger the CH (Calinski-Harabasz) index, the better the clustering effect.

[0029] Finally, according to the optimized clustering result, the InSAR settlement result image can be divided into multiple image regions. For example, if the optimized clustering result is N clusters, the InSAR settlement result image can be divided into N image regions, where N is greater than 1.

[0030] That is to say, the K-means clustering algorithm can be used to cluster the feature vectors, and the InSAR image can be divided into K regions. The selection of the K value can be optimized based on indicators such as the Silhouette Coefficient or the Calinski-Harabasz index (CH index). For example, first set a range of K values such as [5, 20]. After partitioning the classes through the K-means clustering algorithm within this range, calculate the silhouette coefficient or CH index for each classification, and select the K value when the silhouette coefficient or CH index is the largest.

[0031] S103: For each image region, use a preset linear model to determine the trend information of the image region, and use the trend information of the image region to perform detrending processing on the image region to obtain a processed image region.

[0032] In this embodiment, a preset linear model can be used to determine the trend information of the image region. It should be noted that both the atmospheric data and the orbital data are the data that need to be input during the InSAR processing. If the input data is inaccurate, the obtained result will be inaccurate. If the input data has errors, the obtained result will also have errors. The trend information can be understood as the error term in the InSAR result caused by inaccurate input orbital data, that is, the trend information is the error information existing in the InSAR settlement result image due to inaccurate data.

[0033] Specifically, the pixel change trend type of the image region can be determined first. If the pixel change trend type of the image region is the first change trend type, the least squares method is used to fit a quadratic linear model, and the quadratic linear model is used as the preset linear model. The first change trend type is the change in which the pixel values of the pixel points in the image region increase or decrease in one direction. The quadratic linear model is: z = a * x^2 + b * y^2 + c * x * y + d * x + e * y + f; where z represents the fitting value of the quadratic linear model, that is, the trend information, a represents the quadratic term coefficient of x, that is, the influence degree of the square term of x on z, x represents the row number, b represents the quadratic term coefficient of y, that is, the influence degree of the square term of y on z, c represents the coefficient of x*y, that is, the influence degree of the product term of x and y on z, y represents the column number, d represents the linear term coefficient of x, that is, the influence degree of x on z, e represents the linear term coefficient of y, that is, the influence degree of y on z, and f represents the constant term, that is, the intercept.

[0034] If the pixel change trend type of the image region is the second change trend type, the least squares method is used to fit a cubic linear model, and the cubic linear model is used as the preset linear model. The second change trend type is the change in which the pixel values of the pixel points in the image region increase or decrease in multiple directions. The cubic linear model is z = a * x^3 + b * y^3 + c * x^2 * y + d * x * y^2 + e * x^2 + f * y^2 + g * x * y + h * x + i * y + j; where z represents the fitting value of the cubic linear model, that is, the trend information, a represents the cubic term coefficient of x, that is, the influence degree of the cubic term of x on z, x represents the row number, b represents the cubic term coefficient of y, that is, the influence degree of the cubic term of y on z, c represents the influence of the first product of y and the square of x on z, y represents the column number, d represents the influence of the first product of x and the square of y on z, e represents the quadratic term coefficient of x, that is, the influence degree of the square term of x on z, f represents the quadratic term coefficient of y, that is, the influence degree of the square term of y on z, g represents the influence degree of the product term of x and y on z, h represents the linear term coefficient of x, that is, the influence degree of x on z, i represents the linear term coefficient of y, that is, the influence degree of y on z, and j represents the constant term, that is, the intercept.

[0035] The least squares fitting can be understood as using the least squares method to estimate the model parameters of a linear model for a selected linear model, so that the sum of the squares of the residuals is minimized. Taking the quadratic polynomial model as an example, the goal is to find the parameters a, b, c, d, e, f to minimize the following expression sum((z_i - (a * x_i^2 + b * y_i^2 + c * x_i * y_i + d * x_i + e * y_i + f))^2), where (x_i, y_i) are the coordinates of the pixel, and z_i is the settlement amount of the pixel.

[0036] Finally, the row and column numbers of the image area can be input into the preset linear model to obtain the trend information of the image area.

[0037] That is to say, after the present application partitions the settlement results through the K-means clustering algorithm, the trend terms after each partition are eliminated. The specific method is to use the least squares method to fit a linear model and subtract the linear model from the settlement amount. The constructed linear model can be set as a quadratic or cubic linear model. If the trend is relatively complex, it is set as cubic, otherwise it is set as quadratic. It is also possible to set the constructed linear model as a cubic linear model. It can be understood that if the result is a change that increases or decreases in one direction, then it belongs to a simple trend and can be set as quadratic. If it increases or decreases in multiple directions, then it is complex and is set as cubic. It is also possible to uniformly set it as cubic, because if it is a simple trend, the cubic term of the linear model may be 0, which is the same as the quadratic linear model.

[0038] In this way, the present application obtains a linear model through the least squares fitting. The input of the linear model is the row and column numbers, and the output is the trend information. By inputting the row and column numbers of the entire image area, the trend information of the entire image area is obtained through the linear model. Subtracting the obtained trend information from the image area, the image area after removing the trend information is obtained.

[0039] After determining the trend information of the image area, the trend information of the image area can be used to perform trend information removal processing on the image area to obtain the processed image area. For example, the pixel values of the image area can be subtracted from the trend information of the image area to obtain the processed image area.

[0040] S104: Stitch the processed image areas, and perform fusion processing on the boundaries between the processed image areas to obtain the processed InSAR settlement result image.

[0041] In this embodiment, the boundaries between the processed image areas can be fused to obtain the processed InSAR settlement result image.

[0042] As an example, for each image region, according to the boundary between the image region and adjacent image regions, a pixel buffer of the image region is determined. According to the distances between each pixel point in the pixel buffer and the boundary, a first weight and a second weight respectively corresponding to each pixel point are determined, that is. According to the first weight and the second weight respectively corresponding to each pixel point, the pixel value of each pixel point in the InSAR settlement result image, and the pixel value of each pixel point in the processed image region, the pixel value of each pixel point is adjusted respectively to obtain an adjusted image region. The pixel buffer can be understood as a region of a set of pixel points within a range of a preset number of pixel points from the boundary. It can be understood that the first weight and the second weight respectively corresponding to each pixel point are preset. For example, they can be as shown in the content of Table 2 below.

[0043] For example, after performing detrending information operations on each image region, all the image regions need to be mosaicked together. Direct mosaicking will result in obvious traces of the mosaic boundary. To eliminate the traces of the mosaic boundary, the pixel values within n pixel points (i.e., the pixel buffer) from the boundary of each image region are recalculated, and an increasing and a decreasing weight are assigned to the original InSAR result and the detrended InSAR result. The specific calculation method of the weights is as follows.

[0044] If a pixel buffer of n (i.e., the pixel buffer) is established, the step size of the increasing or decreasing weight is 1 / n. Table 2 below shows the weights for establishing 20 pixel buffers: Table 2 Distance from the boundary (pixels) 1 2 3 ... 19 20 Weight of the original result 0 0.05 0.1 ... 0.95 1 Weight of the detrended result 1 0.95 0.9 ... 0.05 0 Then, the pixel value of each pixel point among these 20 pixels is equal to the value of the original result (i.e., the pixel value of the pixel point in the InSAR settlement result image) × the weight of the original result (i.e., the first weight) + the value of the detrended result (i.e., the pixel value of the pixel point in the processed image region) × the weight of the detrended result (i.e., the second weight).

[0045] Finally, based on all the adjusted image regions, a processed InSAR settlement result image can be obtained. That is to say, by mosaicking the partitions after detrending and boundary processing together, the final settlement result is obtained.

[0046] It can be seen from the above technical solutions that compared with the prior art, the present application has the following beneficial effects: 1. Improve the settlement monitoring accuracy: By dividing the InSAR image into image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image, targeted detrending processing can be carried out according to the settlement characteristics of different image regions. Compared with the traditional global detrending method, it can more effectively eliminate the trend term and reduce the influence of systematic errors such as atmospheric delay and orbital error on the settlement result, thereby improving the accuracy of settlement monitoring and providing more reliable data support for geological disaster monitoring, etc.

[0047] 2. Retain more detailed information: Avoid the problem of loss of local detailed information caused by global detrending, so that the local settlement characteristics of each image region can be better retained, which helps to more comprehensively and accurately understand the subtle changes in the surface settlement situation and is of great significance for analyzing the settlement cause and predicting the settlement development trend.

[0048] 3. Ensure data continuity: After processing the image region to remove the trend information by using the trend information of the image region, the boundaries between the processed image regions are fused, which can effectively eliminate the traces of the boundaries of the stitched processed image regions, ensure the continuity and integrity of the entire settlement result data, make the finally obtained settlement result smoother and more natural, conform to the actual situation, and facilitate subsequent data analysis and application.

[0049] 4. Improve processing efficiency and applicability: Use the K-means clustering algorithm, etc. to achieve adaptive region division and detrending processing, with high automation, reduced manual intervention, and improved data processing efficiency. At the same time, this method is applicable to different types and characteristics of InSAR settlement data, has strong versatility and applicability, and can be widely applied to multiple fields such as geological disaster monitoring and urban sustainable development, providing strong technical support for related research and decision-making.

[0050] That is to say, adaptive region division: Compared with the traditional global detrending method, the present application can divide the InSAR image into multiple regions with different settlement characteristics according to the settlement characteristics, so as to better adapt to the non-uniform settlement region. Adaptive model selection: For different regions, a linear model, a quadratic polynomial model or a cubic polynomial model can be selected for fitting, so as to better eliminate the trend term and extract local settlement information. Combine local detrending and mosaicking: Combine local detrending and mosaicking techniques to ensure data continuity while eliminating the trend term.

[0051] Obviously, the InSAR settlement result image processing proposed in the present application can effectively improve the settlement monitoring accuracy, retain more detailed information, ensure data continuity, improve processing efficiency and applicability, and provide more reliable technical support for geological disaster monitoring and urban sustainable development.

[0052] As shown Figure 7 in the figure, it is a specific embodiment of an InSAR settlement result image processing device provided by the present application. The device in this embodiment is the entity device for executing the method described in the above embodiment. Its technical solution is essentially the same as that of the above embodiment, and the corresponding descriptions in the above embodiment also apply to this embodiment. The device includes: A first unit 701, configured to calculate a first settlement gradient and a second settlement gradient of the InSAR settlement result image; wherein, the first settlement gradient is the settlement gradient in the horizontal direction, and the second settlement gradient is the settlement gradient in the vertical direction; A second unit 702, configured to divide the InSAR settlement result image into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image; A third unit 703, configured to, for each image region, use a preset linear model to determine the trend information of the image region, and use the trend information of the image region to perform detrending processing on the image region to obtain a processed image region;<9000243>A fourth unit 704, configured to splice the processed image regions and fuse the boundaries between the processed image regions to obtain a processed InSAR settlement result image.<(

[0053] Optionally, the first unit 701 is configured to: Use a Sobel operator or a Prewitt operator to calculate the first settlement gradient and the second settlement gradient of each pixel point in the InSAR settlement result image.

[0054] Optionally, the second unit 702 is configured to: For each pixel point in the InSAR settlement result image, generate a feature vector of the pixel point according to the first settlement gradient and the second settlement gradient of the pixel point; Cluster the feature vectors of all pixel points in the InSAR settlement result image based on the K-means clustering algorithm to obtain a clustering result; wherein, the clustering result is K clusters; Determine a clustering effect index corresponding to the clustering result according to the clustering result; [[ID=**28]]Optimize the clustering result according to the clustering effect index corresponding to the clustering result to obtain an optimized clustering result; Divide the InSAR settlement result image into multiple image regions according to the optimized clustering result.

[0055] Optionally, the clustering effect index is one of the following: silhouette coefficient, Calinski-Harabasz index.

[0056] Optionally, the third unit 703 is configured to: Determine the type of pixel change trend of the image region; If the type of pixel change trend of the image region is the first change trend type, use the least squares method to fit a quadratic linear model, and use the quadratic linear model as the preset linear model; If the type of pixel change trend of the image region is the second change trend type, use the least squares method to fit a cubic linear model, and use the cubic linear model as the preset linear model; Input the row and column numbers of the image region into the preset linear model to obtain the trend information of the image region.

[0057] Optionally, the third unit 703 is configured to: Subtract the trend information of the image region from the pixel values of the image region to obtain a processed image region.

[0058] Optionally, the fourth unit 704 is configured to: For each image region, determine the pixel buffer of the image region according to the boundary between the image region and the adjacent image regions; determine the first weight and the second weight corresponding to each pixel point according to the distance between each pixel point in the pixel buffer and the boundary; respectively adjust the pixel values of each pixel point according to the first weight and the second weight corresponding to each pixel point, the pixel value of each pixel point in the InSAR settlement result image, and the pixel value of each pixel point in the processed image region to obtain an adjusted image region; Obtain a processed InSAR settlement result image according to all the adjusted image regions.

[0059] In this way, the device can effectively improve the settlement monitoring accuracy, retain more detailed information, ensure data continuity, improve the processing efficiency and applicability, and provide more reliable technical support for geological disaster monitoring and urban sustainable development.

[0060] Figure 8It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0061] The processor, network interface, and memory can be interconnected through an internal bus, and the internal bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of simplicity in representation, Figure 8 only a bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0062] The memory is used to store executable instructions. Specifically, the executable instructions are computer programs that can be executed. The memory can include a memory and a non-volatile memory, and provide executable instructions and data to the processor.

[0063] In a possible implementation manner, the processor reads the corresponding executable instructions from the non-volatile memory into the memory and then runs them, or can also obtain the corresponding executable instructions from other devices to form an InSAR settlement result image processing device at the logical level. The processor executes the executable instructions stored in the memory to implement the InSAR settlement result image processing method provided in any embodiment of the present application through the executed executable instructions.

[0064] As described above in the present application Figure 1The method executed by the InSAR settlement result image processing device provided by the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, the steps of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0065] The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or implemented and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0066] The embodiments of the present application also propose a readable medium. When the execution instructions stored in the readable storage medium are executed by the processor of the electronic device, the electronic device can execute the InSAR settlement result image processing method provided in any embodiment of the present application, and is specifically used to execute the above evaluation method.

[0067] The electronic device described in each of the foregoing embodiments may be a computer.

[0068] Those skilled in the art should understand that the embodiments of the present application may be provided as a method or a computer program product. Therefore, the present application may adopt a completely hardware embodiment, a completely software embodiment, or a form combining software and hardware.

[0069] Each embodiment in this application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference can be made to the corresponding parts of the method embodiments for the relevant content.

[0070] It should also be noted that the term "comprise", "include" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, commodity or device comprising the said element.

[0071] The above description is only for the embodiments of this application and is not intended to limit this application. For those skilled in the art, various modifications and changes can be made to this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the scope of the claims of this application.

Claims

1. An InSAR settlement result image processing method, characterized in that The method includes: Calculating a first settlement gradient and a second settlement gradient of the InSAR settlement result image; wherein, the first settlement gradient is the settlement gradient in the horizontal direction, and the second settlement gradient is the settlement gradient in the vertical direction; Dividing the InSAR settlement result image into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image; For each image region, using a preset linear model to determine the trend information of the image region, and performing detrending processing on the image region using the trend information of the image region to obtain a processed image region; Stitching the processed image regions together, and performing fusion processing on the boundaries between the processed image regions to obtain a processed InSAR settlement result image.

2. The method according to claim 1, wherein The calculating of the first settlement gradient and the second settlement gradient of the InSAR settlement result image includes: Using a Sobel operator or a Prewitt operator to calculate the first settlement gradient and the second settlement gradient of each pixel point in the InSAR settlement result image.

3. The method according to claim 2, wherein The dividing of the InSAR settlement result image into multiple image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image includes: For each pixel point in the InSAR settlement result image, generating a feature vector of the pixel point according to the first settlement gradient and the second settlement gradient of the pixel point; Performing clustering on the feature vectors of all pixel points in the InSAR settlement result image based on the K-means clustering algorithm to obtain a clustering result; wherein, the clustering result is K clusters; Determining a clustering effect index corresponding to the clustering result according to the clustering result; Optimizing the clustering result according to the clustering effect index corresponding to the clustering result to obtain an optimized clustering result; Dividing the InSAR settlement result image into multiple image regions according to the optimized clustering result.

4. The method according to claim 3, characterized in that, The clustering effect index is one of the following: silhouette coefficient, Calinski-Harabasz index.

5. The method according to claim 1, characterized in that The determining of the trend information of the image region using a preset linear model includes: Determining the pixel change trend type of the image region; If the pixel change trend type of the image region is the first change trend type, then using the least squares method to fit a quadratic linear model and taking the quadratic linear model as the preset linear model; If the pixel change trend type of the image region is the second change trend type, then using the least squares method to fit a cubic linear model and taking the cubic linear model as the preset linear model; Inputting the row and column numbers of the image region into the preset linear model to obtain the trend information of the image region.

6. The method according to claim 1, characterized in that, The performing of detrending processing on the image region using the trend information of the image region to obtain a processed image region includes: Subtracting the trend information of the image region from the pixel values of the image region to obtain a processed image region.

7. The method according to claim 1, characterized in that, Performing a fusion process on the boundaries between the processed image regions to obtain a processed InSAR settlement result image, including: For each image region, determining a pixel buffer of the image region according to the boundary between the image region and the adjacent image region; determining a first weight and a second weight respectively corresponding to each pixel point according to the distance between each pixel point in the pixel buffer and the boundary; and respectively adjusting the pixel value of each pixel point according to the first weight and the second weight respectively corresponding to each pixel point, the pixel value of each pixel point in the InSAR settlement result image, and the pixel value of each pixel point in the processed image region to obtain an adjusted image region; Obtaining a processed InSAR settlement result image according to all the adjusted image regions.

8. The InSAR settlement result image processing device is characterized in that, The apparatus includes: A first unit, configured to calculate a first settlement gradient and a second settlement gradient of the InSAR settlement result image; wherein, the first settlement gradient is the settlement gradient in the horizontal direction, and the second settlement gradient is the settlement gradient in the vertical direction; A second unit, configured to divide the InSAR settlement result image into a plurality of image regions according to the first settlement gradient and the second settlement gradient of the InSAR settlement result image; A third unit, configured to, for each image region, determine the trend information of the image region by using a preset linear model, and perform a detrending process on the image region by using the trend information of the image region to obtain a processed image region; A fourth unit, configured to splice the processed image regions and perform a fusion process on the boundaries between the processed image regions to obtain a processed InSAR settlement result image.

9. A readable medium, characterized in that, The readable medium includes execution instructions, and when the processor of the electronic device executes the execution instructions, the electronic device executes the method according to any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory storing execution instructions, and when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1-7.

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