A mixed pixel correction method for extracting water bloom area

Abstract

The application provides a mixed pixel correction water bloom area extraction method, comprising the following steps: acquiring medium-low spatial resolution image data and high spatial resolution image data of a research area, and pre-processing the image data; calculating NDVI to obtain a gray image, and performing threshold segmentation on the obtained gray image by using a segmentation threshold to obtain a water bloom distribution binary image; acquiring a medium-low spatial resolution cyanobacterial water bloom pixel set according to the medium-low spatial resolution water bloom distribution binary image; referring to a high spatial resolution image water bloom distribution binary image corresponding to a spatial position, calculating a water bloom area proportion of a pixel in the medium-low spatial resolution cyanobacterial water bloom pixel set; adopting a polynomial regression analysis model, establishing a relationship model of NDVI and the water bloom area proportion for the NDVI value and the water bloom area proportion of the pixel in the medium-low spatial resolution cyanobacterial water bloom pixel set; and acquiring a corrected water bloom area of the medium-low spatial resolution image by using a water bloom pixel proportion accumulation formula.

Description

Technical Field

[0001] This invention relates to the field of hybrid pixel decomposition technology for ecological environment and remote sensing images, and in particular to a method for extracting algal bloom area using hybrid pixel correction. Background Technology

[0002] Cyanobacterial blooms are widely recognized as one of the most serious lake water environment problems globally. Remote sensing technology, as an information-based and digital monitoring method, can provide multi-temporal, multi-scale, multi-spectral, and multi-platform observational information for the same region, featuring large-scale and periodic observation capabilities. Through the analysis and processing of image data, cyanobacteria and their spatial distribution can be quickly and effectively identified, thereby enabling rapid and accurate acquisition of information such as the outbreak range, severity, and duration of cyanobacterial blooms.

[0003] Generally, using satellite imagery with medium to low spatial resolution increases monitoring frequency but leads to lower monitoring accuracy and relatively larger errors, making it difficult to leverage the advantages of wide spatial coverage and high monitoring frequency of medium to low spatial resolution imagery. Conversely, using high spatial resolution satellites faces the problem of insufficient monitoring cycles; for example, the regression cycle of two Sentinel-2 satellites is approximately 5 days, which is insufficient to meet the timeliness requirements of monitoring operations. Therefore, it is necessary to analyze the spatial scale differences in cyanobacterial bloom extraction from medium-to-low spatial resolution and high spatial resolution imagery, research and establish a bloom area correction model for medium-to-low spatial resolution satellites, improve the monitoring accuracy of medium-to-low spatial resolution satellites, and ultimately obtain high-frequency, high-precision cyanobacterial bloom extraction results.

[0004] Therefore, how to provide a technical solution that can quickly and accurately extract the area of ​​algal blooms from remote sensing monitoring at low to medium spatial resolution is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] While low-to-medium spatial resolution satellite remote sensing has a unique advantage in monitoring cyanobacterial blooms due to its high frequency, it also leads to inaccurate monitoring areas caused by the low monitoring scale. To address this issue, this invention provides a method for correcting the proportion of bloom area in low-to-medium resolution mixed pixels based on high spatial resolution satellite bloom extraction results. This method can improve the accuracy and objectivity of monitoring bloom area at low-to-medium resolution, providing accurate and effective data support for water environment supervision and other purposes.

[0007] (II) Technical Solution

[0008] This invention provides a method for extracting the area of ​​algal blooms in mixed pixels, which corrects the proportion of algal bloom area in medium- and low-resolution mixed pixels based on high spatial resolution satellite algal bloom extraction results, including the following steps:

[0009] (1) Obtain the low-resolution image GIM of the study area and its quasi-synchronous high-resolution image SIm, and preprocess them to obtain the processed images GI and SI.

[0010] The preprocessing steps are: radiometric calibration, atmospheric correction, geometric correction, and water area cropping. The size of the GI image is row×col.

[0011] (2) Calculate the Normalized Difference Vegetation Index (NDVI) of the image GI and SI to obtain grayscale images of the vegetation index, which are g NDVI and s NDVI The visual interpretation method was used to obtain the algal bloom segmentation thresholds t1 and st, respectively. Then, the obtained grayscale images were segmented by thresholding to obtain various binary images, namely G. NDVI and S NDVI ;

[0012] Among them, G NDVI and S NDVI Each pixel in the image has a value of 0 or 1, where 0 represents a non-cyanobacterial bloom and 1 represents a cyanobacterial bloom.

[0013] (3) Using the binary image G obtained in step (2) NDVI , for g NDVI Each pixel (i,j) is classified and assigned to a pixel set P, including the cyanobacterial bloom pixel set P. algae Non-algal bloom water body pixel set P no-algae ;

[0014] (4) Using the binary image S obtained in step (2) NDVI For the pixel set P of cyanobacterial blooms algae Calculate the area ratio of algal blooms in pixels. R (i,j);

[0015] Among them, the percentage of algal bloom area in a pixel R The formula for calculating (i,j) is as follows:

[0016]

[0017] In the formula, M represents the corresponding value of S within the pixel space of pixel(i,j). NDVI The number of pixels, m is the corresponding S in the pixel space of pixel(i,j). NDVI The number of pixels with a value of 1 is the number of algal bloom pixels, and the remaining Mm pixels are non-cyanobacterial bloom pixels.

[0018] (5) Using the cyanobacterial bloom pixel set P obtained in step (3) algae , for P algaeg corresponding to pixel(i,j) NDVI Value and pixel R (i,j), a multinomial regression analysis is performed using p to obtain the parameter k for solving the regression equation. i (i = 0, 1, 2, ..., n);

[0019] The formula for polynomial p is as follows:

[0020]

[0021] In the formula, k i Let x be the parameter to be solved, and let P be the parameter to be solved. algae The middle pixel (i,j) corresponds to g NDVI The image value, p, is the set of pixels in the cyanobacterial bloom. algae pixel(i,j) corresponds to the area ratio of algal bloom. R (i,j), i = 0, 1, 2, ..., n;

[0022] (6) Using the parameter k obtained in step (5) i (i=0,1,2,…,n), establish a relationship model r(x) between NDVI and the area ratio of algal blooms;

[0023] The relationship model r(x) between NDVI and the proportion of algal bloom area is established as follows:

[0024]

[0025] In the formula, k i The parameter k obtained in step (5) i (i = 0, 1, 2, ..., n), x is P algae g corresponding to pixel(i,j) NDVI The value t1 is obtained using the visual interpretation method for g. NDVI The algal bloom segmentation threshold, t2 is the equation The solution x;

[0026] (7) Using the relationship model r(x) between NDVI and algal bloom area obtained in step (6) and the grayscale image g obtained in step (2), NDVI The area of ​​bloom after GI correction is obtained by using the formula S(r) for the cumulative proportion of bloom pixels.

[0027] The formula for the cumulative proportion of water bloom pixels, S(r), is as follows:

[0028]

[0029] In the formula, S is the area of ​​algal bloom after GI correction; n is the total number of pixels in the study area from the GI image; r i The relationship model r(x) between NDVI and algal bloom area ratio obtained in step (6) is used to calculate the algal bloom area ratio of the i-th (1≤i≤n) pixel; c is the coverage area of ​​a single pixel of image GI.

[0030] (III) Beneficial Effects

[0031] The advantages of this method are as follows:

[0032] This invention provides a method for extracting the area of ​​algal blooms using mixed pixel correction. This method comprehensively considers the relationship between NDVI and the proportion of algal bloom area in mixed pixels of low-resolution images. Based on regression analysis of NDVI and algal bloom area proportion, a method is constructed to correct the proportion of algal bloom area in mixed pixels of medium- and low-resolution images based on algal bloom extraction results from high-resolution images, thereby correcting the algal bloom area in medium- and low-resolution images. In summary, the technical solution of this invention can improve the accuracy of algal bloom monitoring using medium- and low-spatial-resolution satellites, better leverage the value of high-frequency monitoring, and improve the theory and methods of remote sensing algal bloom monitoring.

[0033] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0034] To more clearly illustrate the embodiments of the present invention or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the present invention;

[0036] Figure 2 This is a detailed flowchart of an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0038] First, the practical application of the method provided in the embodiments of the present invention will be described.

[0039] Reference Figure 1 Taking Taihu Lake as the study area, GOCI imagery as medium-to-low resolution imagery, and Sentinel-2 imagery as high resolution imagery as an example, the detailed process of this embodiment of the invention is as follows: Figure 2 As shown, the specific implementation steps are as follows:

[0040] (1) Obtain the GOCI image Gim and its quasi-synchronous Sentinel-2 image SIm of the Taihu Lake study area, and preprocess them to obtain the processed images GI and SI.

[0041] The preprocessing steps include: radiometric calibration, atmospheric correction, geometric correction, and water area cropping. The size of the GI image is row×col.

[0042] (2) Calculate the Normalized Difference Vegetation Index (NDVI) of the images GI and SI to obtain grayscale images of the vegetation indices, which are G and SI respectively. NDVI and s NDVI The visual interpretation method was used to obtain the algal bloom segmentation thresholds t1 and st, respectively. Then, the obtained grayscale images were segmented by thresholding to obtain various binary images, namely G. NDVI and S NDVI ;

[0043] Among them, g NDVI and s NDVI The calculation steps are as follows:

[0044]

[0045] In the formula, (i,j) represents the j-th column of the i-th row, i = 1, 2, ..., row, j = 1, 2, ..., col, g NDVI (i,j) and S NDVI (i,j) represent g respectively NDVI and s NDVI The pixel value at (i,j), ρ gNIR (i,j), ρ gR (i,j), ρ sNIR (i,j), ρ sR (i,j) represent the reflectance of the near-infrared band (B12) and red band (B8) of the pixel at image GI(i,j) and the near-infrared band (B8) and red band (B4) of the pixel at image SI(i,j), respectively. NDVI and S NDVI Each pixel in the image has a value of 0 or 1, where 0 represents a non-cyanobacterial bloom and 1 represents a cyanobacterial bloom.

[0046] (3) Using the binary image G obtained in step (2) NDVI , for G NDVIEach pixel (i,j) is classified and assigned to a pixel set P, including the cyanobacterial bloom pixel set P. algae Non-algal bloom water body pixel set P no-algae ;

[0047] The classification process for each pixel (i,j) is as follows:

[0048]

[0049] In the formula, G NDVI (i,j) is G NDVI The cell value at (i,j) represents the j-th column of the i-th row, where i = 1, 2, ..., row and j = 1, 2, ..., col.

[0050] (4) Using the binary image S obtained in step (2) NDVI For the pixel set P of cyanobacterial blooms algae Calculate the area ratio of algal blooms in pixels. R (i,j);

[0051] Among them, the percentage of algal bloom area in a pixel R The formula for calculating (i,j) is as follows:

[0052]

[0053] In the formula, M represents the corresponding value of S within the pixel space of pixel(i,j). NDVI The number of pixels, m is the corresponding S in the pixel space of pixel(i,j). NDVI The number of pixels with a value of 1 is the number of algal bloom pixels in the image SI, and the remaining Mm pixels are non-cyanobacterial bloom pixels.

[0054] (5) Using the cyanobacterial bloom pixel set P obtained in step (3) algae , for P algae g corresponding to pixel(i,j) NDVI Value and pixel R (i,j), a multinomial regression analysis is performed using p to obtain the parameter k for solving the regression equation. i (i = 0, 1, 2, ..., n);

[0055] The formula for polynomial p is as follows:

[0056]

[0057] In the formula, k i Let x be the parameter to be solved, and let P be the parameter to be solved. algae The middle pixel (i,j) corresponds to gNDVI The image value, p, is the set of pixels in the cyanobacterial bloom. algae pixel(i,j) corresponds to the area ratio of algal bloom. R (i,j), i=0,1,2,…,n.

[0058] (6) Using the parameter k obtained in step (5) i (i=0,1,2,…,n), establish a relationship model r(x) between NDVI and the area ratio of algal blooms;

[0059] The relationship model r(x) between NDVI and the proportion of algal bloom area is established as follows:

[0060]

[0061] In the formula, k i The parameter k obtained in step (5) i (i = 0, 1, 2, ..., n), x is P algae g corresponding to pixel(i,j) NDVI The value, t1, is obtained by visual interpretation in step (2) for g. NDVI The algal bloom segmentation threshold, t2 is the equation The solution x.

[0062] (7) Using the relationship model r(x) between NDVI and algal bloom area obtained in step (6) and the grayscale image g obtained in step (2), NDVI The area of ​​bloom after GI correction is obtained by using the formula S(r) for the cumulative proportion of bloom pixels.

[0063] The formula for the cumulative proportion of water bloom pixels, S(r), is as follows:

[0064]

[0065] In the formula, S is the area of ​​algal bloom after GI correction; n is the total number of pixels in the study area from the GI image; r i The relationship model r(x) between NDVI and algal bloom area ratio obtained in step (6) is used to calculate the algal bloom area ratio of the i-th (1≤i≤n) pixel; c is the coverage area of ​​a single pixel of image GI.

[0066] Those skilled in the art will understand that embodiments of the present invention can be provided as methods or computer program products. Therefore, the present invention can be implemented in a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0067] The various embodiments in this invention are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0068] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0069] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for extracting the area of ​​algal blooms with hybrid pixel correction, characterized in that, The method includes the following steps: (1) Acquire medium- and low spatial resolution image data GIM and high spatial resolution image data SIm of the study area, and perform data preprocessing on the images. Data preprocessing includes radiometric calibration, atmospheric correction, geometric correction and image cropping. After preprocessing, GI and SI are obtained respectively, where the size of the GI image is row×col. (2) Calculate the Normalized Difference Vegetation Index (NDVI) of the image GI and SI to obtain grayscale images of the vegetation index, which are g NDVI and s NDVI The visual interpretation method was used to obtain the algal bloom segmentation thresholds t1 and st, respectively. Then, the obtained grayscale images were segmented by thresholding to obtain various binary images, namely G. NDVI and S NDVI ; Among them, G NDVI and S NDVI Each pixel in the image has a value of 0 or 1, where 0 represents a non-cyanobacterial bloom and 1 represents a cyanobacterial bloom. (3) Using the binary image G obtained in step (2) NDVI , for G NDVI Each pixel (i,j) is classified and assigned to a pixel set P, including the cyanobacterial bloom pixel set P. algae Non-algal bloom water body pixel set P no-algae ; Where (i,j) represents the j-th column of the i-th row, i = 1, 2, ..., row, j = 1, 2, ..., col; (4) Using the binary image S obtained in step (2) NDVI For the pixel set P of cyanobacterial blooms algae Calculate the area ratio of algal blooms in pixels. R (i,j); (5) Using the cyanobacterial bloom pixel set P obtained in step (3) algae , for P algae The g corresponding to pixel(i,j) NDVI Value and pixel R (i,j), a multinomial regression analysis is performed using p to obtain the parameter k for solving the regression equation. i (i = 0, 1, 2, ..., n); (6) Using the parameter k obtained in step (5) i Where i = 0, 1, 2, ..., n, establish a relationship model r(x) between the Normalized Difference Vegetation Index (NDVI) and the proportion of algal bloom area; (7) Using the normalized vegetation index (NDVI) obtained in step (6) to establish the relationship model r(x) between the area ratio of algal bloom and the grayscale image g obtained in step (2). NDVI The area of ​​bloom after GI correction is obtained by using the formula S(r) to accumulate the percentage of bloom pixels.

2. The method according to claim 1, characterized in that, In step (4), the pixel area ratio of algal bloom is calculated. R The (i,j) method is as follows: Pixel algal bloom area ratio R The formula for calculating (i,j) is as follows: In the formula, M represents the corresponding value of S within the pixel space of pixel(i,j). NDVI The number of pixels, m is the corresponding S in the pixel space of pixel(i,j). NDVI The number of pixels with a value of 1 is the number of algal bloom pixels in the image SI, and the remaining Mm pixels are non-cyanobacterial bloom pixels.

3. The method according to claim 1, characterized in that, In step (5), the polynomial p formula is as follows: In the formula, k i Let x be the parameter to be solved, and let P be the parameter to be solved. algae The middle pixel (i,j) corresponds to g NDVI The image value, p, is the set of pixels in the cyanobacterial bloom. algae pixel(i,j) corresponds to the area ratio of algal bloom. R (i,j), i=0,1,2,…,n.

4. The method according to claim 1, characterized in that, In step (6), the relationship model r(x) between the Normalized Difference Vegetation Index (NDVI) and the proportion of algal bloom area is established as follows: In the formula, k i The parameter k obtained in step (5) i (i = 0, 1, 2, ..., n), x is P algae The g corresponding to pixel(i,j) NDVI The value, t1, is obtained by visual interpretation in step (2) for g. NDVI The algal bloom segmentation threshold, t2 is the equation The solution x.

5. The method according to claim 1, characterized in that, In step (7), the formula for accumulating the proportion of water bloom pixels, S(r), is as follows: In the formula, S is the area of ​​algal bloom after GI correction; n is the total number of pixels in the study area from the GI image; r i The relationship model r(x) between the Normalized Difference Vegetation Index (NDVI) and the proportion of algal bloom area obtained in step (6) is used to calculate the proportion of algal bloom area in the i-th pixel, 1≤i≤n; c is the coverage area of ​​a single pixel in the image GI.

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