Remote sensing image water body rapid extraction method based on multi-scale GLCM feature fusion

Abstract

The invention discloses a remote sensing image water body rapid extraction method based on multi-scale GLCM feature fusion, and relates to the technical field of water body remote sensing monitoring, and the method comprises the following steps: S1, collecting receipts; s2, calculating according to the collected data to obtain a water body index matrix W; s3, multi-scale GLCM texture feature extraction is carried out; s4, performing weighted fusion on the water body index matrix W and the multi-scale texture features to generate a comprehensive feature matrix; s5, parameter space definition; s6, carrying out automatic threshold segmentation through an Otsu method; s7, generating a binary water body matrix; and S8, iteration is carried out. The method has the beneficial effects that a Monte Carlo sampling and iterative optimization mechanism is introduced, global search is carried out in a wide parameter space, an optimal parameter set is found, fine weighted fusion of different scale features is realized, weighted parameters of texture features are automatically optimized, the water body recognition precision and efficiency in a complex environment are greatly improved, and the method is suitable for popularization and application. And meanwhile, the requirement for manual parameter adjustment is reduced, and higher applicability is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of water body remote sensing monitoring, and in particular to a method for rapidly extracting water bodies from remote sensing images by fusion of multi-scale GLCM features. Background Art

[0002] With the advancement of remote sensing technology and big data processing capabilities, remote sensing images are increasingly used in water resource monitoring, environmental management, and disaster emergency response. However, water area extraction technology faces many challenges, such as accuracy and adaptability to complex environments. Existing water extraction technologies are mainly divided into spectral feature-based methods and machine learning-based methods, and both have their own advantages and disadvantages in multi-scale environments. Traditional spectral feature methods mainly rely on constructing water body indexes, such as the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI), which calculate the reflectance of specific spectral combinations such as near infrared (NIR), green band (Green) and short wave infrared (SWIR), thereby achieving water body and non-water body segmentation. This type of method works well under specific conditions, and is particularly suitable for single water environment or large-scale water areas. However, NDWI and MNDWI have significant limitations in complex terrain and multi-scale environments, such as low recognition accuracy for small water bodies, poor extraction effect at complex boundaries, and susceptibility to interference from high-reflection areas such as vegetation and buildings.

[0003] Water body extraction methods based on machine learning have developed rapidly in recent years. Through algorithms such as convolutional neural networks (CNN) and support vector machines (SVM), water body areas are identified, and they have stronger learning and generalization capabilities. Such methods usually rely on a large amount of labeled data for training, which can improve the ability to identify details in water body extraction to a certain extent. However, the adaptability of machine learning methods in multi-scale applications is limited, especially in cross-regional and cross-temporal water body extraction, the recognition effect of different water body types may be significantly reduced. In addition, deep learning models usually have high requirements for computing resources, resulting in high computing costs when applied to large-scale remote sensing data.

[0004] In order to overcome the shortcomings of spectral and machine learning methods, some researchers have proposed multi-scale water body extraction methods based on texture features. This type of method uses the Gray-Level Co-occurrence Matrix (GLCM) to extract the texture features of the image, including inertia, angular second moment (ASM) and entropy, thereby providing texture information of the water body to assist in identification. GLCM features have certain advantages in complex terrain and multi-scale environments because they can capture the spatial structural characteristics of the image. However, a single texture feature shows insufficient adaptability at different scales and still has limitations in processing detailed edges. Summary of the invention

[0005] To solve the above problems, the present invention provides a method for rapid water body extraction from remote sensing images by fusion of multi-scale GLCM features, which is achieved through the following technical solutions.

[0006] A method for rapid water body extraction from remote sensing images by fusion of multi-scale GLCM features, the method comprising the following steps:

[0007] S1, data collection, remote sensing image data including green light, near infrared and short wave infrared bands are obtained from the radar image data set of Sentinel-2 satellite. The original data matrices of remote sensing images of the three bands are I Green ,I NIR and I SWIR , preprocess the original data matrix to obtain I' Green , I' NIR and I' SWIR ;

[0008] S2, through I' Green , I' NIR and I' SWIR The water index matrix W is calculated;

[0009] S3, multi-scale GLCM texture feature extraction, GLCM is the gray level co-occurrence matrix, and the elements of GLCM are defined as:

[0010] P(i,j|d,θ)

[0011] Where i, j represents the gray level, d is the pixel distance, and θ is the direction;

[0012] The following three texture features are extracted from GLCM:

[0013] Moment of Inertia:

[0014]

[0015] Angular second moment:

[0016]

[0017] entropy:

[0018]

[0019] The above three texture features are calculated by kernels of different scales to obtain the multi-scale texture feature matrix T Inertia 、T ASM and T Entropy ;

[0020] S4, the water index matrix W and the multi-scale texture feature T Inertia 、T ASM 、T Entropy Weighted fusion generates a comprehensive feature matrix S(x,y):

[0021]

[0022] Where α is the water index weight coefficient; β, γ, and δ are the weight coefficients of each texture feature; w k is the comprehensive weight coefficient of multi-scale texture features;

[0023] S5, parameter space definition, define α, β, γ, δ and w k The range of values ​​of is used to construct the parameter space, and the Monte Carlo method is used to randomly sample and generate the parameter combination η in the parameter space. The comprehensive feature matrix S(x,y) is calculated based on the parameter combination η;

[0024] S6, Otsu method automatic threshold segmentation, apply Otsu method on the comprehensive feature matrix S of the determined parameter combination η, and automatically determine the optimal segmentation threshold of binarization. The formula is as follows:

[0025]

[0026] in represents the between-class variance;

[0027] S7, generate a binary water body matrix, binarize the comprehensive feature matrix S according to the threshold τ, and generate a water body mask matrix M(x,y):

[0028]

[0029] S8, iteration, set the number of iterations N, repeat steps S5 to S7 until the number of iterations is reached. In each iteration, the error E is calculated based on the generated water mask matrix. η :

[0030]

[0031] Among them, A true is the reference water area, A η is the water area calculated using the parameter combination η based on the water mask matrix M(x,y).

[0032] Preferably, in step S1, the preprocessing operation of the original data matrix is: processing the original data matrix I by median filtering or Gaussian filtering Green ,I NIR and I SWIR , remove noise and outliers, and then perform atmospheric and radiation corrections to obtain I' Green , I' NIR and I′ SWIR .

[0033] Preferably, in step S2, the calculation formula of the water index matrix W is as follows:

[0034] W(x,y) represents the water index value of the pixel.

[0035] Preferably, in step S2, the calculation formula of the water index matrix W is as follows:

[0036] W(x,y) represents the water index value of the pixel.

[0037] Preferably, in step S3, the scales of the kernels used in the GLCM calculation are 3×3, 5×5 and 7×7 respectively.

[0038] Preferably, in step S5, α, β, γ, δ and w k The value range is [0.5,3.5], and the step size is 0.5.

[0039] Preferably, in step S5, α, β, γ, δ and w k The value range is [1,5], and the step size is 1.

[0040] Preferably, in step S5, α, β, γ, δ and w k The value range is [0.1,1], and the step size is 0.1.

[0041] Preferably, in step S8, in each iteration, A η is recorded and statistically analyzed in all iterations. Based on the maximum likelihood estimation principle, the calculated value A of the parameter combination η with the highest frequency is η Best reflects the area of ​​the water body.

[0042] The beneficial effects of the present invention are as follows:

[0043] 1. The present invention enhances the recognition ability of small-scale water bodies and complex boundary areas by fusing multi-scale GLCM texture features and automatically optimizing weights, avoiding the ambiguity and information loss problems of traditional NDWI and MNDWI methods in boundary details, thereby achieving high-precision water body shape extraction.

[0044] 2. The present invention introduces multi-scale texture features, extracts the spatial structure information of the image through the GLCM method, and combines it with Monte Carlo iterative optimization to achieve effective distinction of high-reflection areas such as vegetation and buildings, thereby reducing the false detection rate in the water body extraction process, and is particularly suitable for water body identification in diverse and complex backgrounds.

[0045] 3. By iteratively optimizing the weights of multi-scale texture features through the Monte Carlo algorithm, the present invention can automatically adjust the water segmentation threshold in different environments, enhance the adaptability to complex terrain and multi-scale environments, and overcome the limitation of the traditional Otsu method that performs poorly in complex environments.

[0046] 4. The present invention realizes automatic parameter selection and weight adjustment through Monte Carlo iterative optimization, avoiding the inefficient process of manual repeated parameter adjustment, making the water extraction process more efficient, and improving the application convenience and stability of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the description of the specific implementation methods will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 :A flowchart of a method for rapid water body extraction from remote sensing images by fusion of multi-scale GLCM features according to the present invention;

[0049] Figure 2 : Schematic diagram of the present invention in extracting large lakes and filtering small water bodies;

[0050] Figure 3 : Comparison results of extracting small water bodies using this method and the traditional method based on water index;

[0051] Figure 4 : Results of 100 Monte Carlo iterations. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] like Figure 1-4 As shown, a method for rapid water body extraction from remote sensing images by fusion of multi-scale GLCM features is provided, and the method comprises the following steps:

[0054] S1, data collection, remote sensing image data including green light, near infrared and short wave infrared bands are obtained from the radar image data set of Sentinel-2 satellite. The original data matrices of remote sensing images of the three bands are I Green ,I NIR and I SWIR , preprocess the original data matrix to obtain I' Green , I' NIR and I' SWIR .

[0055] The preprocessing operation of the original data matrix is: the original data matrix I is processed by median filtering or Gaussian filtering Green ,I NIR and I SWIR , remove noise and outliers, then perform atmospheric and radiation correction to make it consistent with the physical spectral reflectance characteristics, and remove clouds and shadows. After correction, I' is obtained Green , I' NIR and I' SWIR .

[0056] S2, through I' Green , I' NIR and I' SWIR The water index matrix W is calculated.

[0057] Select the appropriate water index according to the application scenario. The commonly used ones are NDWI and MNDWI. The formulas are as follows:

[0058]

[0059] W(x,y) represents the water index value of the pixel.

[0060] S3, multi-scale GLCM texture feature extraction, GLCM is the gray level co-occurrence matrix, and the elements of GLCM are defined as:

[0061] P(i,j|d,θ)

[0062] Where i, j represents the gray level, d is the pixel distance, and θ is the direction;

[0063] The following three texture features are extracted from GLCM:

[0064] Moment of Inertia:

[0065]

[0066] Angular second moment:

[0067]

[0068] entropy:

[0069]

[0070] The above three texture features are calculated by kernels of different scales. The scales of the kernels used in GLCM calculation are 3×3, 5×5 and 7×7 respectively, and the multi-scale texture feature matrix T is obtained. Inertia 、T ASM and T Entropy .

[0071] S4, the water index matrix W and the multi-scale texture feature T Inertia 、T ASM 、T Entropy Weighted fusion generates a comprehensive feature matrix S(x,y):

[0072]

[0073] Where α is the water index weight coefficient; β, γ, and δ are the weight coefficients of each texture feature; w k is the comprehensive weight coefficient of multi-scale texture features.

[0074] S5, parameter space definition, define α, β, γ, δ and w k The parameter space is constructed based on the value range of , and the Monte Carlo method is used to randomly sample and generate parameter combinations η in the parameter space. The comprehensive feature matrix S(x,y) is calculated based on the parameter combination η.

[0075] Set the value range of the parameter space so that the best feature weight combination can be automatically found through Monte Carlo optimization. The specific parameter space can be adjusted according to needs. In general, α, β, γ, δ and w k The value range is [0.5, 3.5], and the step size is 0.5. This set of parameter space can be applied to water extraction for general needs. Through Monte Carlo iterative optimization, random sampling weight combination, Monte Carlo sampling statistical characteristics and large sample theory are used to return the optimal parameter combination and feature matrix.

[0076] For specific application scenarios, when we extract large lakes based on remote sensing images, we hope to filter out small water bodies (such as small reservoirs and small tributaries). The traditional Otsu-based method does not have room for adjustment, but automatically sets the maximum inter-class variance as the threshold. Therefore, when faced with small water bodies that need to be filtered, it has obvious limitations. By setting the value of the parameter space in a larger range, α, β, γ, δ and w k The value range of is [1,5], and the step length is 1, which can filter out small water bodies. For the setting of parameters, the Monte Carlo method is used to perform random exploration in the parameter space without giving specific parameter values, such as Figure 2 The water body extraction results of 4 selected random parameters with obvious characteristics in this parameter space are shown. The method can effectively filter small water bodies.

[0077] When we need to capture small water bodies, we set the parameter space range to a smaller space and reduce the step size, α, β, γ, δ and w at the same time. k The value range is [0.1, 1], and the step size is 0.1. When the parameter space and step size are set small, it is suitable for extracting small water bodies and improving the performance in detail processing and boundary recognition, such as Figure 3 The results of small water body extraction are compared with the results of traditional water body extraction method based on water body index.

[0078] S6, Otsu method automatic threshold segmentation, apply Otsu method on the comprehensive feature matrix S of the determined parameter combination η, and automatically determine the optimal segmentation threshold of binarization. The formula is as follows:

[0079]

[0080] in represents the between-class variance.

[0081] By adjusting the parameter combination, the optimal segmentation threshold can be automatically adjusted.

[0082] S7, generate a binary water body matrix, binarize the comprehensive feature matrix S according to the threshold τ, and generate a water body mask matrix M(x,y):

[0083]

[0084] S8, iteration, set the number of iterations N, repeat steps S5 to S7 until the number of iterations is reached. In each iteration, the error E is calculated based on the generated water mask matrix. η :

[0085]

[0086] Among them, Atrue is the reference water area, A η is the water area calculated using the parameter combination η based on the water mask matrix M(x,y).

[0087] At each iteration, A η is recorded and statistically analyzed in all iterations. Based on the maximum likelihood estimation principle, the calculated value A of the parameter combination η with the highest frequency is η Best reflects the area of ​​the water body.

[0088] When we need to accurately extract the area of ​​a water body, we can increase the number of Monte Carlo iterations. Figure 4 The changes in the water area calculation results and their probability density estimates after 100 Monte Carlo parameter iterations are shown. The results show that in 100 iterations, the water area calculation values ​​generally fluctuate around the reference value. Through random parameter adjustment, the algorithm can stably concentrate around the reference value. Based on the principle of maximum likelihood estimation, the calculated value with the highest frequency is selected as the result that best reflects the actual water area. The statistical characteristics of Monte Carlo sampling and the large sample theory ensure the applicability and reliability of the parameter optimization process. These results provide a theoretical basis and data support for further application of this method for large-scale water area monitoring.

[0089] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for rapid water extraction from remote sensing images by fusion of multi-scale GLCM features, characterized in that: The method comprises the following steps: S1, data collection, remote sensing image data including green light, near infrared and short wave infrared bands are obtained from the radar image data set of Sentinel-2 satellite. The original data matrices of remote sensing images of the three bands are I Green ,I NIR and I SWIR , preprocess the original data matrix to obtain I' Green , I' NIR and I' SWIR ; S2, through I' Green , I' NIR and I' SWIR The water index matrix W is calculated; S3, multi-scale GLCM texture feature extraction, GLCM is the gray level co-occurrence matrix, and the elements of GLCM are defined as: P(i,j|d,θ) Where i, j represents the gray level, d is the pixel distance, and θ is the direction; The following three texture features are extracted from GLCM: Moment of Inertia: Angular second moment: entropy: The above three texture features are calculated by kernels of different scales to obtain the multi-scale texture feature matrix T Inertia 、T ASM and T Entropy ; S4, the water index matrix W and the multi-scale texture feature T Inertia 、T ASM 、T Entropy Weighted fusion generates a comprehensive feature matrix S(x,y): Where α is the water index weight coefficient; β, γ, and δ are the weight coefficients of each texture feature; w k is the comprehensive weight coefficient of multi-scale texture features; S5, parameter space definition, define α, β, γ, δ and w k The range of values ​​of is used to construct the parameter space, and the Monte Carlo method is used to randomly sample and generate the parameter combination η in the parameter space. The comprehensive feature matrix S(x,y) is calculated based on the parameter combination η; S6, Otsu method automatic threshold segmentation, apply Otsu method on the comprehensive feature matrix S of the determined parameter combination η, and automatically determine the optimal segmentation threshold of binarization. The formula is as follows: in represents the between-class variance; S7, generate a binary water body matrix, binarize the comprehensive feature matrix S according to the threshold τ, and generate a water body mask matrix M(x,y): S8, iteration, set the number of iterations N, repeat steps S5 to S7 until the number of iterations is reached. In each iteration, the error E is calculated based on the generated water mask matrix. η : Among them, A true is the reference water area, A η is the water area calculated using the parameter combination η based on the water mask matrix M(x,y).

2. According to the method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 1, it is characterized in that: In step S1, the preprocessing operation of the original data matrix is: the original data matrix I is processed by median filtering or Gaussian filtering. Green ,I NIR and I SWIR , remove noise and outliers, and then perform atmospheric and radiation corrections to obtain I' Green , I' NIR and I' SWIR .

3. The method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 2 is characterized in that: In step S2, the calculation formula of the water index matrix W is as follows: W(x,y) represents the water index value of the pixel.

4. The method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 2 is characterized in that: In step S2, the calculation formula of the water index matrix W is as follows: W(x,y) represents the water index value of the pixel.

5. A method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to any one of claims 3 or 4, characterized in that: In step S3, the kernel sizes used in GLCM calculation are 3×3, 5×5 and 7×7 respectively.

6. The method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 5 is characterized in that: In step S5, α, β, γ, δ and w k The value range is [0.5,3.5], and the step size is 0.

5.

7. The method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 5 is characterized in that: In step S5, α, β, γ, δ and w k The value range is [1,5], and the step size is 1.

8. The method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to claim 5 is characterized in that: In step S5, α, β, γ, δ and w k The value range is [0.1,1], and the step size is 0.

1.

9. A method for rapid water body extraction from remote sensing images by multi-scale GLCM feature fusion according to any one of claims 6 to 8, characterized in that: In step S8, A η is recorded and statistically analyzed in all iterations. Based on the maximum likelihood estimation principle, the calculated value A of the parameter combination η with the highest frequency is η Best reflects the area of ​​the water body.

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