DS-InSAR homogeneous pixel selection method considering landform characteristics

Abstract

The invention discloses a DS-InSAR homogeneous pixel selection method considering topographic and geomorphic features. The method comprises the following steps: preprocessing SAR image data; extracting the gradient, the slope direction and the ground object coverage type of the target area based on the external DEM data; the SAR image data and the landform feature data are ensured to be in the same coordinate system; carrying out the recognition and selection of homogeneous pixels in combination with the gradient, slope direction and ground feature coverage type topographic and geomorphic feature data; optimizing the selection process of homogeneous pixels through a hard threshold selection algorithm and a soft threshold selection algorithm; calculating and analyzing the selection precision of homogeneous pixels; and resolving the deformation rate of the target area by using the point selection result of the homogeneous pixels and combining a DS-InSAR data processing flow. According to the method, the uniformity and accuracy of DS-InSAR homogeneous pixel selection are improved, scattering variation pixels caused by landform features are effectively eliminated, and the precision of deformation rate calculation is improved.

Description

Technical Field

[0001] The invention relates to the field of geographic information system and time-series InSAR technology, and in particular to a DS-InSAR homogeneous pixel selection method taking into account topographic features. Background Art

[0002] In distributed scatterer interferometry (DS-InSAR), the selection of homogeneous points is a key step in achieving high-precision surface deformation monitoring. Homogeneous points refer to a set of pixels with the same backscattering properties and the same phase scattering center in a time series SAR (synthetic aperture radar) image. These points usually represent areas on the surface with relatively stable physical properties and geometric structures, such as urban buildings, rocky areas, water bodies or other artificial surfaces. The existing permanent scatterer interferometry (PS-InSAR) method relies on the density of stable scatterers. When applied in large-scale areas, it faces challenges such as explosive growth of PS points and uneven data distribution. In areas with more natural features such as suburbs, this density is often low. Therefore, DS-InSAR technology was developed to increase the density of measured scatterers (MS). In deformation analysis, time series InSAR technology with strong noise resistance, long distance, and high resolution for monitoring large-scale scenes is favored. Homogeneous points can provide a stable and reliable reference benchmark, so that small deformation signals can be separated from noise. This is particularly important for fine deformation monitoring of large-scale complex scenes, and can significantly improve the accuracy and reliability of monitoring results. With the development of SAR technology, long-time series deformation monitoring has become a key means. The quality of homogeneous point selection directly affects the accuracy of InSAR time series analysis. If the selected homogeneous points are inaccurate, it may lead to deviations in deformation estimation, which in turn affects the final deformation monitoring results. In addition, the spatial distribution density of homogeneous points also has an important impact on the resolution and coverage of the monitoring results. Therefore, the development of high-quality homogeneous point selection algorithms is of great significance to improving the practicality and reliability of DS-InSAR technology.

[0003] At present, the research on homogeneous point selection algorithms mainly focuses on improving the algorithm's computational efficiency, enhancing the algorithm's adaptability, and improving the selection accuracy. The existing selection algorithms, such as coherence screening based on empirical thresholds, often rely on artificially set parameters, which may not be flexible enough under different surface conditions and are difficult to adapt to complex land cover types. Researchers have proposed a variety of homogeneous point selection algorithms based on statistical models, which use hypothesis tests to identify homogeneous points by analyzing the time series of pixel intensity. For example, by constructing a statistical model of pixel intensity, the probability of similarity between pixels can be calculated, thereby evaluating homogeneity in the form of probability. At present, homogeneous point selection algorithms are mainly divided into two categories: parametric statistics and non-parametric statistics. Parametric statistical methods such as the FaSHPS algorithm are favored for their fast computing speed and low memory consumption, but may not be robust enough when the sample size is small. Non-parametric statistical methods such as the Baumgartner-Wei-Schindler (BWS) test show better robustness when processing non-Gaussian distribution data. Although existing homogeneous point selection algorithms have made progress in some aspects, they still face challenges, such as the computational complexity of the algorithm, sensitivity to outliers, and applicability under non-uniform surface conditions, which need to be further optimized to adapt to different monitoring scenarios and data characteristics. In addition, these methods are prone to heterogeneous objects with the same spectrum in complex terrain, and heterogeneous pixels are mistakenly judged as homogeneous pixels. Summary of the invention

[0004] The present invention proposes a new homogeneous point selection technology, which optimizes the selection process of homogeneous points by introducing external terrain data, such as surface feature data such as slope, aspect, and land cover type. This technology not only improves the adaptability and selection accuracy of the algorithm, but also can effectively eliminate scattered variation pixels caused by terrain features, thereby improving the accuracy of subsequent deformation rate solution. Compared with the existing technology, the new technology has shown obvious advantages in ensuring the uniform distribution of homogeneous points in space and improving the selection accuracy. Especially in areas with drastic terrain changes, the new technology can significantly reduce the risk of mistakenly judging heterogeneous pixels as homogeneous pixels, thereby proving its applicability and efficiency in dealing with complex terrain.

[0005] In view of the defects of the prior art, the present invention provides a DS-InSAR homogeneous pixel selection method taking into account the topographic features, comprising the following steps:

[0006] Step 1: Preprocess the SAR image data to ensure data quality and availability;

[0007] Step 2: Extract the slope, aspect and land cover type of the target area based on the external DEM data using the following formula;

[0008]

[0009] in, It represents the rate of change in the horizontal direction starting from the center pixel. Indicates the rate of change in the vertical direction starting from the center pixel, tan -1 is the inverse tangent function, tan2 -1 is the four-quadrant inverse tangent function. The slope calculated by the formula ranges from 0 to 90°, and the aspect ranges from 0 to 360°.

[0010] Step 3: Ensure that the SAR image data and terrain and geomorphic feature data such as slope, aspect, and land cover type are in the same coordinate system.

[0011] Step 4: Combine terrain and geomorphic feature data such as slope, aspect, and land cover type to identify and select homogeneous pixels.

[0012] Step 5: Optimize the selection process of homogeneous pixels through hard threshold selection algorithm and soft threshold selection algorithm. As shown in formula (3) and formula (4):

[0013]

[0014] In the formula, Indicates p i The sample mean of , P{p} represents the interval estimate of the pixel point p to be estimated, For p i The type of land cover, D P is the ground object coverage type of the central pixel point p to be estimated.

[0015]

[0016] Where CI is the confidence interval, is the weighted sample mean, is the weighted sample variance, N is the number of samples, Represents the standard normal distribution Percentile.

[0017] Step 6: Calculate and analyze the selection quality and accuracy of homogeneous pixels.

[0018] Step 7: Using the point selection results of homogeneous pixels and combining them with the DS-InSAR data processing flow, the deformation rate of the target area is calculated.

[0019] Preferably, step 3 includes the following sub-steps:

[0020] Step 3.1: Check whether the SAR intensity image and the terrain feature data are in the same coordinate system. If not, proceed to step 3.2; if consistent, skip step 3.2.

[0021] Step 3.2: Convert both to the same coordinate system.

[0022] Step 3.3: For ease of calculation, convert both to the WGS84 geographic coordinate system.

[0023] Preferably, step 6 and step 7 respectively include the following sub-steps:

[0024] Step 6.1: Obtain the homogeneous point quantity information file and evaluate its distribution uniformity.

[0025] Step 6.2: Obtain the homogeneous point location information file and analyze the point selection accuracy.

[0026] Step 7.1: Based on the point selection results, perform phase optimization on the interference pattern.

[0027] Step 7.2: Perform phase unwrapping on the optimized interferogram.

[0028] Step 7.3: Perform deformation calculation to obtain the deformation rate result of the target area.

[0029] The above scheme of the present invention has the following beneficial effects:

[0030] 1. The present invention improves the uniformity and accuracy of DS-InSAR homogeneous pixel selection by comprehensively considering the topographic and geomorphic features, especially in areas with large terrain undulations, and can effectively eliminate scattered variation pixels caused by topographic and geomorphic features.

[0031] 2. When selecting homogeneous pixels, the present invention significantly reduces the misselection of heterogeneous pixels while maintaining a high signal-to-noise ratio, thereby improving the accuracy of deformation rate solution, proving its applicability and effectiveness under complex terrain conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flow chart of the DS-InSAR homogeneous pixel selection method taking into account topographic and geomorphic features of the present invention;

[0033] Figure 2 is the SAR image coverage of the study area of ​​the embodiment of the present invention;

[0034] Figure 3 is the type of land cover in the study area of ​​the embodiment of the present invention;

[0035] Figure 4 is a map of the local central pixel (red) and its homogeneous pixel (yellow) in the study area of ​​the embodiment;

[0036] Among them, (a) and (d) use the existing technology algorithm; (b) and (e) use the hard threshold algorithm; (c) and (f) use the soft threshold algorithm;

[0037] (a), (b), and (c) are the point selection results under the background of the average intensity image;

[0038] (d), (e), and (f) are the point selection results under the background of the ground cover type classification image;

[0039] Figure 5 This is the deformation rate solution result of the embodiment based on the existing point selection technology, and the red circles are landslide area A and landslide area B;

[0040] Figure 6 This is the deformation rate solution result of the embodiment based on the new technology of the present invention, and the red circles are landslide area A and landslide area B;

[0041] Figure 7 It is the deformation result of the landslide area A in the embodiment using the prior art algorithm, and the white box indicates the approximate range;

[0042] Figure 8 It is the deformation result of the landslide area A in the embodiment using the method of the present invention, and the white frame indicates the approximate range;

[0043] Fig. 9 is the time-series deformation result of the characteristic points P1 and P2 of the landslide area A in the embodiment;

[0044] Fig.10 It is the deformation result of the landslide area B in the embodiment using the prior art algorithm, and the white box indicates the approximate range;

[0045] Fig.11 It is the deformation result of the landslide area B in the embodiment using the method of the present invention, and the white frame indicates the approximate range;

[0046] Fig.12 It is the time-series deformation result of the characteristic points P3 and P4 in the landslide area B of the embodiment. DETAILED DESCRIPTION

[0047] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0048] like Figure 1A DS-InSAR homogeneous pixel selection method taking into account topographic features includes the following steps:

[0049] Step 1: Perform preprocessing such as denoising and registration on the SAR image data to ensure the quality and availability of the data.

[0050] Step 2: Use formula (1) and formula (2) to extract the slope, aspect and land cover type of the target area based on external DEM data.

[0051]

[0052] in, It represents the rate of change in the horizontal direction starting from the center pixel. Indicates the rate of change in the vertical direction starting from the center pixel, tan -1 is the inverse tangent function, tan2 -1 is the four-quadrant inverse tangent function. The slope calculated by the formula ranges from 0 to 90°, and the aspect ranges from 0 to 360°.

[0053] Step 3: To minimize the impact of different coordinate bases on the matching results, check the coordinate systems of the SAR image data and the terrain and geomorphic feature data. If they are inconsistent, convert them to the same coordinate system.

[0054] Step 4: Combine terrain and geomorphic feature data such as slope, aspect, and land cover type to identify and select homogeneous pixels.

[0055] Step 5: Optimize the selection process of homogeneous pixels through hard threshold selection algorithm and soft threshold selection algorithm.

[0056] The hard threshold selection algorithm combines the ground object coverage type with statistical tests. By introducing the ground object coverage type information, the algorithm can take into account the scattering characteristics of different ground objects, thereby achieving more accurate homogeneous point identification. Specifically, with the assistance of the ground object coverage type, the algorithm can identify and exclude non-homogeneous pixels caused by changes in ground object coverage, which may be mistakenly included in the process of selecting homogeneous points through statistical tests. In the optimization algorithm, for a pixel point p to be estimated, there are p given a window size. i , i = 1, 2, ..., k, a total of k pixels, for the remaining k-1 pixels except the central pixel p, the following formula is used to select homogeneous points and screen out heterogeneous points:

[0057]

[0058] In the formula, Indicates p iThe sample mean of , P{p} represents the interval estimate of the pixel point p to be estimated, For p i The type of land cover, D P is the ground object coverage type of the central pixel point p to be estimated.

[0059] The soft threshold selection algorithm combines the data of slope, aspect and ground object coverage type with the SAR image intensity information to provide richer information for homogeneous pixel selection, optimizes the statistical test by using external data, and adapts to the scattering characteristics under different terrain and ground object conditions by adjusting the test parameters. In the prior art, interval estimation is usually determined based on hypothesis testing. The present invention adopts a weighted statistical method to adjust the sample weight according to the terrain and ground object characteristics, so as to obtain a more accurate interval estimation. For each pixel p, a weight factor is determined according to the attributes of its slope, aspect and ground object coverage type. The ground object coverage type weight factor can be determined based on prior knowledge, and the slope and aspect weight factors can be determined based on statistical analysis to reflect the influence of different surface features on the SAR backscattering characteristics. The sample mean of the prior art is obtained by adding the intensity values ​​of all samples and dividing it by the number of samples. After the weight factor is introduced, the calculation of the sample mean becomes a weighted average, that is, the intensity value of each sample is multiplied by its corresponding weight factor and then divided by the sum of all weight factors. Similarly, the sample variance also needs to be weighted, and the weighted variance takes into account the weight of each sample to reflect the contribution of different surface features to the scattering variability. Assuming that the distribution of the sample mean is close to a normal distribution, the interval estimate with a confidence level of 1-α can be expressed as follows:

[0060]

[0061] Where CI is the confidence interval, is the weighted sample mean, is the weighted sample variance, N is the number of samples, Represents the standard normal distribution Percentile.

[0062] Step 6: Calculate and analyze the selection quality and accuracy of homogeneous pixels.

[0063] Step 7: Using the point selection results of homogeneous pixels and combining them with the DS-InSAR data processing flow, the deformation rate of the target area is calculated.

[0064] The present invention effectively solves the main problems of the current traditional DS-InSAR homogeneous pixel selection, and provides a DS-InSAR homogeneous pixel selection method that takes into account the topographic and geomorphic features. The method described in the present invention fully considers the influence of topographic and geomorphic features on the homogeneous pixel selection, is suitable for the homogeneous pixel selection under complex terrain, has a clear mathematical model, and the obtained selection results can be used for multiple purposes according to actual needs.

[0065] When the present invention is implemented, the implementation steps based on the example data are as follows:

[0066] Data preparation: SAR image data uses Sentinel-1 data products; external DEM data uses SRTM3 DEM products; external land cover type products use GlobeLand30 datasets.

[0067] Step 1: Perform denoising and registration operations on Sentinel-1SAR image data to ensure data quality and availability.

[0068] Step 2: Using formula (1) and formula (2), extract the slope, aspect and land cover type of the target area based on SRTM3 DEM data and GlobeLand30 dataset. Figure 2 , the type of land cover is as follows Figure 3 shown.

[0069] Step 3: Check whether the coordinate systems of SAR image data and surface feature data such as slope, aspect and land cover type are unified. The SAR image coordinate system is the radar coordinate system, and the surface feature data is the WGS84 geographic coordinate system. Unify the two into the WGS84 geographic coordinate system.

[0070] Step 4: Combine terrain and geomorphic feature data such as slope, aspect, and land cover type to identify and select homogeneous pixels.

[0071] Step 5: Use formula (3) and formula (4) to optimize the selection process of homogeneous pixels through hard threshold selection algorithm and soft threshold selection algorithm. First, use external surface feature data to distinguish different types of objects in the study area. Then, for each pixel's slope, aspect, and object coverage type information, the algorithm adjusts the parameters of the statistical test, such as threshold and weight, to adapt to the specific scattering characteristics of the type, construct a new interval estimate, and finally select high-quality homogeneous pixels.

[0072] Step 6: Calculate and analyze the selection accuracy of homogeneous pixels to ensure that the selected homogeneous pixels have high uniformity and accuracy.

[0073] Step 7: Using the point selection results of homogeneous pixels and combining them with the DS-InSAR data processing flow, the deformation rate of the target area is calculated.

[0074] like Figure 4 The figure shows the point selection results of the sample data in the existing point selection technology and the new technology of the present invention. The existing technology selects 51 pixels in the window as homogeneous samples, the hard threshold algorithm of the present invention selects 49 pixels in the same window as homogeneous samples, and the soft threshold algorithm selects 44 pixels in the same window as homogeneous samples. Figure 4 As can be seen from (d), (e), and (f) in the figure, the hard threshold algorithm eliminates two pixels belonging to the forest type, which is different from the shrub type of the central pixel. The soft threshold algorithm eliminates five more pixels on this basis. This is achieved by refitting the interval estimation of the SAR image intensity information, slope aspect, and ground object coverage information through statistical test methods to select homogeneous pixels, and more accurately eliminate pixels that are heterogeneous with the central pixel.

[0075] The subsequent deformation solution results are as follows Figure 5 and Figure 6 As shown, the new algorithm of the present invention exhibits higher accuracy and reliability in calculating deformation rate, especially in landslide area A and landslide area B. The new algorithm can provide a more continuous and smoother deformation rate distribution, which indicates that the new algorithm can effectively capture slight changes in the surface, especially in areas with large terrain undulations.

[0076] Further analysis of the deformation rate results of landslide area A and landslide area B shows that Figure 7 , Figure 8 and Fig.10 , Fig.11 It can be clearly seen from the comparison that the deformation rate map of the existing algorithm in the landslide area appears to be more chaotic, and some deformation features are covered by noise. This is due to the misselection of heterogeneous points by only considering the SAR image intensity information. In contrast, the deformation rate map calculated by the new algorithm of the present invention is basically consistent with the landslide direction and range shown in the optical image, which further verifies the applicability and effectiveness of the new algorithm under complex terrain conditions.

[0077] Fig. 9 and Fig.12 The time series deformation results of four characteristic points in the landslide area based on the existing algorithm and the new algorithm of the present invention are shown respectively. The new algorithm can provide more stable and accurate deformation trends at these key characteristic points, which further proves the superiority of the new algorithm in deformation monitoring, especially in the area of ​​sudden change of deformation rate, the new algorithm can more accurately capture the start and end of the deformation signal, which is crucial for early warning and risk assessment of landslide disasters.

Claims

1. A DS-InSAR homogeneous pixel selection method taking into account topographic features, characterized in that: The following steps are involved: Step 1: Preprocess the SAR image data to ensure data quality and availability; Step 2: Extract the slope, aspect and land cover type of the target area based on the external DEM data using the following formula; in, It represents the rate of change in the horizontal direction starting from the center pixel. Indicates the rate of change in the vertical direction starting from the center pixel, tan -1 is the inverse tangent function, tan2 -1 is a four-quadrant inverse tangent function; the slope range calculated by the formula is 0 to 90°, and the aspect range is 0 to 360°; Step 3: Ensure that the SAR image data and the terrain and geomorphic feature data of slope, aspect and land cover type are in the same coordinate system; Step 4: Combine the topographic and geomorphic feature data of slope, aspect and land cover type to identify and select homogeneous pixels; Step 5: Optimize the selection process of homogeneous pixels through hard threshold selection algorithm and soft threshold selection algorithm; as shown in formula (3) and formula (4): In the formula, Indicates p i The sample mean of , P{p} represents the interval estimate of the pixel point p to be estimated, For p i The type of land cover, D P is the ground object coverage type of the central pixel point p to be estimated; Where CI is the confidence interval, is the weighted sample mean, is the weighted sample variance, N is the number of samples, Represents the standard normal distribution quantile; Step 6: Calculate and analyze the selection quality and accuracy of homogeneous pixels; Step 7: Using the point selection results of homogeneous pixels and combining them with the DS-InSAR data processing flow, the deformation rate of the target area is calculated.

2. The DS-InSAR homogeneous pixel selection method taking into account topographic features according to claim 1, characterized in that: The step 3 includes the following sub-steps: Step 3.1: Check whether the SAR intensity image and the terrain feature data are in the same coordinate system; if not, proceed to step 3.2; if consistent, skip step 3.2; Step 3.2: Convert both to the same coordinate system; Step 3.3: For ease of calculation, convert both to the WGS84 geographic coordinate system.

3. The DS-InSAR homogeneous pixel selection method taking into account topographic features according to claim 1, characterized in that: The step 6 comprises the following sub-steps: Step 6.1: Obtain the homogeneous point quantity information file and evaluate its distribution uniformity; Step 6.2: Obtain the homogeneous point location information file and analyze the point selection accuracy.

4. The DS-InSAR homogeneous pixel selection method taking into account topographic features according to claim 1, characterized in that: The step 7 comprises the following sub-steps: Step 7.1: Based on the point selection results, perform phase optimization on the interference pattern; Step 7.2: Perform phase unwrapping on the optimized interferogram; Step 7.3: Perform deformation calculation to obtain the deformation rate result of the target area.

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