Remote sensing image pixel and sampling point matching method and device based on Gaussian distribution

Abstract

The invention discloses a Gaussian distribution-based remote sensing image pixel and sampling point matching method and device, and relates to the technical field of remote sensing data processing and environment monitoring. The method comprises the following steps: firstly, acquiring latitude and longitude coordinates of a ground sampling point and remote sensing image data of a coverage area, and extracting geographic reference information of image spatial resolution and pixels; then determining an initial weighted window range according to the image resolution, and calculating a Gaussian kernel standard deviation by combining a preset boundary weight threshold and a maximum coverage distance; constructing a two-dimensional Gaussian weight field by taking the sampling point as a center, calculating a weight according to an Euclidean distance between the sampling point and a pixel center in the image, and dynamically determining a pixel set participating in calculation according to a Gaussian kernel coverage range; and finally, multiplying the reflectivity of each pixel by the corresponding weight, summing and normalizing to obtain the estimated reflectivity at the sampling point. According to the method, neighborhood pixel information can be effectively fused when the sampling points and the pixel centers do not coincide, and the reflectivity extraction precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing data processing and geoscience information analysis, and in particular to a remote sensing image pixel and sampling point matching method and device based on Gaussian distribution. BACKGROUND

[0002] In the fields of water quality monitoring, environmental investigation and ecological assessment, it is often necessary to use remote sensing images and ground sampling point data for joint analysis. Since remote sensing images have fixed spatial resolution and pixel grid structure, the longitude and latitude positions of ground sampling points often do not completely coincide with the center of the image pixels, and directly extracting the reflectance value of a single pixel may introduce large errors.

[0003] Traditional pixel-sampling point matching methods mainly include nearest neighbor method and simple average method, but these methods do not fully consider the spatial distance difference between the sampling point and the pixel center, which easily leads to information loss and spatial heterogeneity distortion. In addition, when the sampling point falls near the pixel boundary, the spectral information of the neighboring pixels cannot be reasonably weighted to the actual sampling point position, thereby reducing the reflectance estimation accuracy and affecting the reliability of subsequent water quality parameter inversion.

[0004] Therefore, there is an urgent need for a method that can dynamically determine the pixel set participating in the calculation according to the spatial relationship between the sampling point and the surrounding pixels, and reasonably weight and fuse the reflectance, in order to improve the accuracy of matching remote sensing data with ground measured data. SUMMARY

[0005] To solve the above problems, the present application provides a remote sensing image pixel and sampling point matching method and device based on Gaussian distribution, which solves the reflectance estimation error problem caused by the non-coincidence of the sampling point position and the pixel center by constructing a two-dimensional Gaussian weight field and weighting and fusing according to the spatial distance between the sampling point and the pixel center, thereby improving the data matching accuracy.

[0006] The technical scheme of the present application is:

[0007] A remote sensing image pixel and sampling point matching method based on Gaussian distribution, comprising the following steps:

[0008] S1: Collect the longitude and latitude coordinates of the ground sampling points, and obtain the remote sensing image data covering the sampling point area; extract the spatial resolution, pixel row and column number and geographic reference information of the remote sensing image;

[0009] S2: Determine the spatial range of the initial weighting window according to the spatial resolution of the remote sensing image;

[0010] S3: Set the weight threshold of the Gaussian function at the maximum coverage distance of the window , and calculate the Gaussian kernel standard deviation . ;

[0011] S4: Construct a two-dimensional Gaussian weight field centered on the sampling point position, and calculate the weight of each pixel ;

[0012] S5: Calculate the Euclidean distance according to the spatial position difference between the sampling point and the center of the adjacent pixel ;

[0013] S6: Determine the dynamic pixel set based on the coverage range of the Gaussian kernel, so that the coverage range is consistent with the spatial relationship of the sampling point position;

[0014] S7: Multiply the reflectivity of each pixel in the dynamic pixel set by the corresponding weight and sum; divide the resulting weighted sum by the sum of all weights to obtain the estimated reflectivity at the sampling point position;

[0015] S8: Use the estimated reflectivity as input data for remote sensing inversion modeling.

[0016] A remote sensing image pixel and sampling point matching device based on Gaussian distribution, comprising the following modules:

[0017] The sampling point module collects the latitude and longitude coordinates of the ground sampling points and obtains remote sensing image data covering the sampling point area; extracts the spatial resolution, pixel row and column number and geographic reference information of the remote sensing image;

[0018] The window module determines the spatial range of the initial weighting window according to the spatial resolution of the remote sensing image;

[0019] The standard deviation calculation module sets the weight threshold of the Gaussian function at the maximum coverage distance of the window , and calculates the Gaussian kernel standard deviation ;

[0020] The Gaussian weight field construction module constructs a two-dimensional Gaussian weight field centered on the sampling point position, and calculates the weight of each pixel ;

[0021] The Euclidean distance calculation module calculates the Euclidean distance according to the spatial position difference between the sampling point and the center of the adjacent pixel ;

[0022] The dynamic pixel set determination module determines the dynamic pixel set based on the coverage range of the Gaussian kernel, so that the coverage range is consistent with the spatial relationship of the sampling point position;

[0023] The reflectivity estimation module multiplies the reflectivity of each pixel in the dynamic pixel set by the corresponding weight multiplying and summing; dividing the resulting weighted sum by the sum of all weights to obtain the estimated reflectance at the sample point location;

[0024] an input module, the estimated reflectance being input data for remote sensing inversion modeling.

[0025] An electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0026] A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to implement the method.

[0027] The present application has the following beneficial effects:

[0028] The present application can effectively fuse the spectral information of neighboring pixels under the condition that the sample point and the pixel center do not coincide, and can perform smoothing weighting according to the spatial distance to reduce the estimation error introduced by the position deviation; the pixel set is dynamically determined, so that invalid calculation caused by the fixed window method is avoided; the present application is suitable for multispectral and hyperspectral remote sensing data, and provides high-precision input data for environmental monitoring research. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of a pixel and sample point matching method based on Gaussian distribution of a remote sensing image;

[0030] Figure 2 is a comparison graph of error band curves of three reflectance extraction methods on 251 spectral bands;

[0031] Figure 3 is a p-value distribution histogram of three reflectance extraction methods and ground measured values. DETAILED DESCRIPTION

[0032] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments, which are illustrative examples of the present application and serve as a description of the present application. The present application is only explained, but not limited.

[0033] As shown in the figure: the present application proposes a pixel and sample point matching method based on Gaussian distribution of a remote sensing image, comprising the following steps: Figure 1

[0034] S1: the latitude and longitude coordinates of 10 sample points are obtained, and 0.75m airborne hyperspectral remote sensing image data covering the sample point area on the same day is obtained, and the sample point position is mapped to a specific position of the remote sensing image according to the latitude and longitude of the sample point;

[0035] ​S2: Taking the satellite image with a spatial resolution of 20m as the reference scale, the pixel window within a distance of 20m from the center pixel is taken as the reference window scale according to the same physical coverage range;

[0036] S3: The weight threshold of the Gaussian function at the maximum coverage distance of the window is set to 20m, and the formula is:

[0037] ;

[0038] wherein, the value range of . The Gaussian kernel standard deviation is calculated to ensure that different resolution images have consistent weight decay characteristics in the same geographical range;

[0039] S4: Taking the actual geographical position of the sampling point as the center, a two-dimensional Gaussian weight field is constructed in its neighborhood range, and the weight calculation formula is:

[0040] ;

[0041] wherein, is the Euclidean distance between the th pixel center and the sampling point;

[0042] S5: The spatial distance between the sampling point and each adjacent pixel center is calculated , and the pixel weight is obtained according to the above formula, the spatial distance between the sampling point and each adjacent pixel center is calculated by the Euclidean distance formula:

[0043] ;

[0044] S6: When the sampling point position deviates from the pixel center, the pixel set participating in the weighting is dynamically selected according to the coverage range of the Gaussian kernel;

[0045] S7: The reflectivity of each participating pixel is multiplied by the corresponding weight , and then summed and divided by the sum of all weights to obtain the estimated reflectivity at the sampling point:

[0046] ;

[0047] S8: The estimated reflectivity obtained is taken as the input data for subsequent remote sensing inversion modeling.

[0048] Step S1 includes: ​​​S11: Obtain the longitude and latitude coordinates of the sampling point using GPS or other positioning devices; S12: Retrieve image data covering the sampling point area from the remote sensing database; S13: Georeference the image data so that the pixels are consistent with the geographic coordinates.

[0049] The determination of the dynamic pixel set is based on the actual landing position of the sampling point within the pixel, and is automatically determined by calculating the intersection area of the Gaussian kernel coverage range and the grid system.

[0050] Embodiment:

[0051] This embodiment uses the pixel matching reflectance extraction method based on Gaussian distribution (Method C) proposed in the present application, and compares and evaluates it with the traditional single-pixel extraction method (Method A) and the pixel window mean method (Method B). The evaluation indicators include reflectance extraction accuracy and statistical significance test. Figure 1 The steps of the pixel matching reflectance extraction method based on Gaussian distribution proposed in the present application are shown, Figure 2 The reflectance extraction error band curves of the three methods at 251 spectral bands are shown, Figure 3 The p-value distribution histograms between the three methods and the ground measured reflectance are shown.

[0052] From Figure 2 It can be seen that the error band curve of Method C is below Methods A and B at most waveband positions, showing better extraction accuracy. Statistical calculation shows that the mean absolute error (MAE) of the three methods at all wavebands is 0.0025, 0.0023 and 0.0022 respectively, among which the MAE of Method C is the lowest, proving its obvious advantage in reflectance extraction accuracy. The width of the error band reflects the volatility of the extraction error, i.e. the stability of the method. The analysis results show that the error band width of Method C is generally narrower, especially in the 500-630 nm and 680 nm after wavebands, and its MAE standard deviation is significantly lower than that of Methods A and B, indicating that Method C has higher stability and smaller error fluctuation.

[0053] Figure 3 It is shown that the p-value of Method A is mainly concentrated in the lower interval (0-0.05) and is relatively dispersed, indicating that its extraction results at multiple wavebands are significantly different from the ground measured reflectance. In contrast, the p-value distribution of Method C is more concentrated than that of Methods A and B, and compared with Method B, its distribution is more concentrated, reflecting obvious statistical significance advantage. Therefore, the pixel matching method based on Gaussian distribution is proved to be the preferred solution for image reflectance extraction.

Claims

1. A method for matching pixels and sampling points of remote sensing images based on Gaussian distribution, characterized in that, The method comprises the following steps: S1: collecting the longitude and latitude coordinates of the ground sampling points, and obtaining remote sensing image data covering the sampling point area; extracting the spatial resolution, pixel row and column numbers and geographic reference information of the remote sensing image; S2: determining the spatial range of the initial weighted window according to the spatial resolution of the remote sensing image; S3: Set the weight threshold of the Gaussian function at the maximum coverage distance of the window and calculate the Gaussian kernel standard deviation ;​ S4: Construct a two-dimensional Gaussian weight field centered on the sampling point position, and calculate the weight of each pixel ; S5: Calculate the Euclidean distance according to the spatial position difference between the sampling point and the center of the adjacent pixel ; S6: determining the dynamic pixel set based on the coverage range of the Gaussian kernel, so that the coverage range is consistent with the spatial relationship of the sampling point position; S7: Calculate the reflectance of each pixel in the dynamic pixel set. With corresponding weights Multiply and sum; divide the weighted sum by the sum of all weights to obtain the estimated reflectance at the sampling point location; S8: taking the estimated reflectivity as the input data of the remote sensing inversion modeling.

2. The method of claim 1, wherein, Step S1 comprises: S11: obtaining the longitude and latitude coordinates of the sampling points using GPS or other positioning devices; S12: calling the image data covering the sampling point area from the remote sensing database; S13: performing geographic registration on the image data so that the pixels are consistent with the geographic coordinates.

3. The method of claim 1, wherein, the gaussian kernel standard deviation by the window maximum coverage distance with the weight threshold determined by the constraint relationship and satisfies the formula: ； wherein, the value range is 0 <1.

4. The method of claim 1, wherein, the weights are calculated according to the following formula: ； wherein is the Euclidean distance between the center of the th pixel and the sampling point, is the Gaussian kernel standard deviation calculated in step S3.

5. The method of claim 1, wherein, The determination of the dynamic pixel set is based on the actual landing position of the sampling point inside the pixel, and the intersection area of the Gaussian kernel coverage range and the grid system is automatically determined.

6. The method of claim 1, wherein, The calculation formula of the estimated reflectivity is: ； wherein, is the reflectance value of the pixel, is the weight value of the corresponding pixel.

7. The method of claim 1, wherein, sampling points the spatial distance to each neighboring pixel center calculated by the euclidean distance formula​ 。 8. A device for matching a remote sensing image pixel and a sampling point based on a Gaussian distribution, characterized in that, The method comprises the following modules: A sampling point module for collecting the longitude and latitude coordinates of the ground sampling points, and obtaining remote sensing image data covering the sampling point area; extracting the spatial resolution, pixel row and column numbers and geographic reference information of the remote sensing image; A window module for determining the spatial range of the initial weighted window according to the spatial resolution of the remote sensing image; a standard deviation calculation module sets a weight threshold of the Gaussian function at a maximum coverage distance of the window and calculates a standard deviation of the Gaussian kernel ;​ a Gaussian weight field constructing module, a two-dimensional Gaussian weight field is constructed with the sampling point position as the center, and the weight of each pixel is calculated ; a Euclidean distance calculation module for calculating the Euclidean distance according to the spatial position difference between the sampling point and the center of the adjacent pixel ; A dynamic pixel set determination module for determining the dynamic pixel set based on the coverage range of the Gaussian kernel, so that the coverage range is consistent with the spatial relationship of the sampling point position; The reflectance estimation module calculates the reflectance of each pixel in the dynamic pixel set. With corresponding weights Multiply and sum; divide the weighted sum by the sum of all weights to obtain the estimated reflectance at the sampling point location; An input module for taking the estimated reflectivity as the input data of the remote sensing inversion modeling.

9. An electronic device, comprising: It comprises: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Executable instructions are stored thereon, which are executed by a processor to make the processor implement the method of any one of claims 1 to 7.

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