Automatic tidal creek extraction method based on color spatial component water index replacement fusion

Abstract

The invention relates to an automatic tidal creek extraction method based on color space component water index replacement fusion. The method comprises the following steps: acquiring a target remote sensing image; calculating a normalized water body index NDWI, an improved normalized water body index MNDWI and a multispectral water body index MuWI; performing color synthesis to obtain a color image; carrying out HIS transformation, and taking the obtained I component as a new water body index NWI; performing gray level co-occurrence matrix GLCM calculation on the NWI image to obtain an average feature; the average features are processed through optimized top-hat transformation; carrying out statistics to determine an optimal threshold value for extracting the tidal creek, and generating a binary image; and performing connected component analysis and morphological operation on the binarized image to obtain a final result of tidal creek extraction. The method has the beneficial effects that the influence of background heterogeneity on tidal creek extraction is reduced, the linear features of the tidal creek are effectively enhanced, the tidal creek and the background can be better distinguished, and the precision of tidal creek extraction is improved.

Description

Technical Field

[0001] The invention relates to the technical field of remote sensing image classification and extraction, and more specifically, to a tidal gully automatic extraction method based on water index replacement and fusion of color space components. Background Art

[0002] Tidal creeks are linear depressions in tidal-dominated coastal landforms, usually composed of complex bifurcated waterway systems, presenting one of the most striking geometric forms in the natural environment. Tidal creeks are not only important channels for the exchange of sediments, nutrients and pollutants, but also maintain fragile ecological habitats and maintain a delicate balance between sedimentation processes and hydrodynamics. However, with the overdevelopment of coastal areas and the intensification of human activities, the morphology of tidal creeks has changed significantly. As the morphology of tidal creeks continues to change, traditional tidal creek detection methods have gradually revealed their limitations. Traditional field measurements are difficult to be widely used in tidal creek network monitoring over large areas due to their short tidal exposure time and limited coverage. At the same time, aerial image analysis methods that rely on manual visual interpretation are complex and subjective, making it difficult to conduct large-scale repeated observations. In contrast, water body index is widely used in water body detection due to its high accuracy and low-cost implementation. However, in areas with significant differences in tidal creek width and complex backgrounds, water body index is often affected by shadows and noise, and traditional water body index methods cannot effectively distinguish narrow tidal creeks from adjacent objects.

[0003] The essence of the index method is to explore the commonality of the target object, adapt to target recognition in different scenarios, and extract the target object more flexibly than hard classification. The water body index is obtained by mathematically calculating multiple optical bands in optical satellite images, aiming to enhance the spectral signal distinction of the target water body. At present, a variety of indices have been successfully developed and applied in various target water body extraction studies. For tidal creek monitoring, traditional water body index methods such as NDWI and MNDWI have good performance in large-scale water body detection, but in areas where the width of the tidal creek varies greatly, especially in areas where the spectral difference between narrow tidal creeks and the surrounding complex background (such as vegetation, mudflats) is small, resulting in unsatisfactory extraction of these indices. In addition, affected by shadows, clouds and other noise, a single water body index is difficult to effectively distinguish between narrow tidal creeks and tidal flat areas. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and to propose an automatic tidal gully extraction method based on replacement and fusion of water indexes of color space components.

[0005] In the first aspect, a method for automatically extracting tidal gullies based on water index replacement and fusion of color space components is provided, comprising:

[0006] Step 1: Acquire a target remote sensing image and preprocess the target remote sensing image;

[0007] Step 2, calculating the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image;

[0008] Step 3: Perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; perform HIS transformation on the color image, and use the obtained I component as the new water index NWI;

[0009] Step 4: Calculate the gray level co-occurrence matrix GLCM of the NWI image to obtain the average feature;

[0010] Step 5: Processing the average feature by optimized top hat transformation;

[0011] Step 6: Statistically determine the optimal threshold for extracting tidal gullies based on the results of processing with the optimized top-hat transform, and generate a binary image based on the optimal threshold;

[0012] Step 7: Perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

[0013] Preferably, in step 2, the calculation formulas of NDWI, MNDWI and MuWI are:

[0014]

[0015] MuWI=-4ND(2,3)+2ND(3,8)+2ND(3,12)-ND(3,11)

[0016] Among them, Green is the green light band value, NIR is the near infrared band value, SWIR1 is the first band value of shortwave infrared, and ND(i,j) represents the normalized difference between band i and band j.

[0017] Preferably, in step 3, MNDWI is used as the red component, NDWI is used as the green component, and MuWI is used as the blue component for color synthesis; the calculation formula of HIS transformation is:

[0018]

[0019] H=60H t (if H t <0,H t =H t +6)

[0020] Among them, R, G and B are the red component, green component and blue component respectively, max and min are the maximum and minimum values ​​of R, G and B of each pixel respectively, I represents the pixel brightness (brightness), S represents the color saturation (brightness) of the pixel, Ht is an intermediate variable used to calculate the hue H; I represents the pixel brightness, and S represents the color saturation of the pixel.

[0021] Preferably, in step 4, the calculation formula is:

[0022] Gray=(0.3×B)+(0.59×R)+(0.11×GR)

[0023]

[0024] Where Gray is the gray value, i is the gray value of the first pixel, j is the gray value of the second pixel adjacent to the first pixel, f(x,y) is the pixel value of the image at position (x,y), (Δx,Δy) is the position offset of adjacent pixels calculated based on the distance d and the angle θ, and p(i,j) is the joint probability of two pixel pairs with gray values ​​i and j in GLCM.

[0025] Preferably, in step 5, the optimized top hat change T o The calculation formula is:

[0026]

[0027] Among them, S o and S c Opening operation The structural element of the closed operation ·, I C is the complementary image of image I and is defined as follows:

[0028]

[0029] Where U is the full set of possible values ​​for each pixel; the complementary image I of image I C The operation of (x,y) is:

[0030] I C (x,y)=max(U)-I(x,y)

[0031] Where max(U) is the maximum possible pixel value and I(x,y) is the pixel value at position (x,y) of the original image.

[0032] Preferably, in step 6, the expression to be satisfied for tidal gully extraction is:

[0033]

[0034] Among them, T o (x,y) is the T of position (x,y) o value, T o The threshold value.

[0035] In a second aspect, a tidal gully automatic extraction system based on water index replacement fusion of color space components is provided, which is used to execute any method described in the first aspect, including:

[0036] A first acquisition module is used to acquire a target remote sensing image and pre-process the target remote sensing image;

[0037] The first calculation module is used to calculate the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image;

[0038] A synthesis module is used to perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; and perform HIS transformation on the color image, and use the obtained I component as a new water index NWI;

[0039] The second calculation module is used to calculate the gray level co-occurrence matrix GLCM of the NWI image to obtain the average feature;

[0040] A processing module, used for processing the average feature by optimized top-hat transformation;

[0041] A determination module, used for statistically determining an optimal threshold for extracting tidal gullies according to a result of processing by optimized top-hat transformation, and generating a binary image according to the optimal threshold;

[0042] The second acquisition module is used to perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

[0043] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0044] In a fourth aspect, an electronic device is provided, including:

[0045] Memory, used to store computer programs;

[0046] A processor is used to execute the computer program to implement any method as described in the first aspect.

[0047] The beneficial effects of the present invention are as follows: the present invention first replaces and fuses color space components by combining three water body indices (NDWI, MNDWI, MuWI), thereby enhancing the spectral discrimination of different land objects, and can more clearly extract tidal gullies of different widths, especially under complex backgrounds, thereby overcoming the deficiency of traditional single index in extracting narrow tidal gullies. Afterwards, by introducing gray-level co-occurrence matrix (GLCM) and optimized top-hat transformation technology, the spatial and texture features of the image are fully utilized, the morphological features of the tidal gullies are effectively highlighted, the interference of complex backgrounds such as vegetation and mudflats is reduced, and the extraction results are clearer and more accurate. Finally, based on morphological features and statistical analysis, the optimal threshold is determined for binarization processing, the automatic extraction of tidal gullies is realized, the efficiency and consistency of tidal gully extraction are improved, and the subjectivity and errors caused by human intervention are avoided. The present invention can adapt to complex coastal tidal flat areas through the fusion processing of spectral and spatial features, realize the monitoring and analysis of large-scale tidal gully networks, overcome the limitations of traditional field measurements and manual interpretation, and provide data support and reference for subsequent tidal gully morphological analysis and evolution characteristics. Therefore, the method proposed in the present invention has important practical application significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of the automatic tidal gully extraction method based on the replacement and fusion of color space component water index;

[0049] Figure 2 Schematic diagram of the process of constructing a new water index;

[0050] Figure 3 This is a schematic diagram of the gray-level symbiosis matrix tidal gully extraction effect;

[0051] Figure 4 Schematic diagram of the optimized top-hat transformation tidal gully extraction effect;

[0052] Figure 5a is a schematic diagram of the original image;

[0053] Figure 5b Schematic diagram of tidal gully extraction effect. DETAILED DESCRIPTION

[0054] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0055] Embodiment 1:

[0056] Although traditional water body indices such as NDWI and MNDWI perform well in a wide range of water body detection, their extraction accuracy is obviously insufficient when the width of tidal creeks varies greatly, especially when the spectral difference between narrow tidal creeks and complex backgrounds (such as vegetation and mudflats) is small.

[0057] In order to solve the problems of the prior art, Example 1 of the present application provides an automatic tidal creek extraction method based on the replacement and fusion of color space component water indices. First, the Sentinel-2 remote sensing images are screened and preprocessed, and the NDWI, MNDWI and MuWI are calculated. Then, these index images are synthesized into color images, and a new water body index NWI is constructed through HIS transformation. Subsequently, the gray-level co-occurrence matrix (GLCM) analysis and optimized top-hat transformation technology are applied to further enhance the linear characteristics of the tidal creek and reduce background interference. Finally, according to the morphological characteristics of the tidal creek area, the appropriate threshold is determined through statistical analysis, and accurate tidal creek extraction is achieved through connected component analysis and morphological operations. This method effectively improves the distinction between the tidal creek and the background, especially in complex tidal flat environments, it can achieve more accurate automatic extraction of tidal creeks.

[0058] Specifically, Figure 1 As shown, the method provided by the present invention comprises:

[0059] Step 1: Acquire a target remote sensing image and preprocess the target remote sensing image.

[0060] For example, the target remote sensing image is a Sentinel-2 remote sensing image available in a target area and a target month, and the preprocessing includes: atmospheric correction and cloud removal.

[0061] Step 2: Calculate the normalized water index NDWI, improved normalized water index MNDWI and multispectral water index MuWI corresponding to the target remote sensing image.

[0062] In step 2, the calculation formulas for NDWI, MNDWI and MuWI are:

[0063]

[0064] MuWI=-4ND(2,3)+2ND(3,8)+2ND(3,12)-ND(3,11)

[0065] Among them, Green is the green light band value, NIR is the near infrared band value, SWIR1 is the first band value of shortwave infrared, and ND(i,j) represents the normalized difference between band i and band j.

[0066] In this example, NIR, Red, Green, and SWIR1 are B8, B4, B3, and B11 of the Sentinel-2 data, respectively.

[0067] Step 3: Figure 2 As shown, according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image, color synthesis is performed to obtain a color image; and the color image is subjected to HIS (hue, intensity and saturation combination) transformation, and the obtained I component is used as the new water index NWI.

[0068] In step 3, MNDWI is used as the red component, NDWI as the green component, and MuWI as the blue component for color synthesis; the calculation formula of HIS transformation is:

[0069]

[0070] H=60H t (if H t <0,H t =Ht+6)

[0071] Among them, R, G and B are the red component, green component and blue component respectively, max and min are the maximum and minimum values ​​of R, G and B of each pixel respectively, Ht is an intermediate variable used to calculate the hue H; I represents the pixel brightness (brightness), and S represents the color saturation (brightness) of the pixel.

[0072] Step 4: Figure 3 As shown, the gray level co-occurrence matrix GLCM is calculated for the NWI image to obtain the average (SumAverage) feature.

[0073] Step 5: Figure 4 As shown, the Sum Average feature is processed by optimized top hat transformation to reduce the heterogeneity of tidal flat background and enhance the linear characteristics of tidal gully.

[0074] In step 5, the present application uses morphological operations to highlight the structural features of the tidal creek area and reduce background interference.

[0075] Step 6: Statistically determine the optimal threshold for extracting tidal gullies based on the results of processing with the optimized top-hat transform, and generate a binary image based on the optimal threshold.

[0076] Step 7: Figure 5a and Figure 5b As shown, connected component analysis and morphological operations are performed on the binary image to obtain the final result of tidal gully extraction.

[0077] Therefore, this application constructs a multi-feature water index NWI that combines multiple water index replacement fusions to enhance the distinction between tidal gullies and other landforms. In the index construction process, spectral features and spatial texture features are combined to achieve simple and fast extraction of tidal gullies through thresholds (as shown in Figure 5).

[0078] Embodiment 2:

[0079] Based on Example 1, Example 2 of the present application provides a more specific method for automatic extraction of tidal gullies based on replacement and fusion of water index of color space components, including:

[0080] Step 1: Acquire a target remote sensing image and preprocess the target remote sensing image.

[0081] Step 2: Calculate the normalized water index NDWI, improved normalized water index MNDWI and multispectral water index MuWI corresponding to the target remote sensing image.

[0082] Step 3: Perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; and perform HIS transformation on the color image, and use the obtained I component as the new water index NWI.

[0083] Step 4: Calculate the gray level co-occurrence matrix GLCM of the NWI image to obtain the average features.

[0084] In step 4, the calculation formula is:

[0085] Gray=(0.3×B)+(0.59×R)+(0.11×GR)

[0086]

[0087] Where Gray is the gray value, i is the gray value of the first pixel, j is the gray value of the second pixel adjacent to the first pixel, f(x,y) is the pixel value of the image at position (x,y), (Δx,Δy) is the position offset of adjacent pixels calculated based on the distance d and the angle θ, and p(i,j) is the joint probability of two pixel pairs with gray values ​​i and j in GLCM.

[0088] Step 5: Process the average feature through optimized top-hat transformation.

[0089] In step 5, the optimized top hat variation T o The calculation formula is:

[0090]

[0091] Among them, So and S c Opening operation The structural element of the closed operation ·, I C is the complementary image of image I and is defined as follows:

[0092]

[0093] Where U is the full set of possible values ​​for each pixel; the complementary image I of image I C The operation of (x,y) is:

[0094] I C (x,y)=max(U)-I(x,y)

[0095] Where max(U) is the maximum possible pixel value and I(x,y) is the pixel value at position (x,y) of the original image.

[0096] Step 6: Statistically determine the optimal threshold for extracting tidal gullies based on the results of processing with the optimized top-hat transform, and generate a binary image based on the optimal threshold.

[0097] In step 6, the expression that needs to be satisfied for tidal gully extraction is:

[0098]

[0099] Among them, T o (x,y) is the T of position (x,y) o value, T o In this example, is -0.02.

[0100] Step 7: Perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

[0101] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0102] Embodiment 3:

[0103] Based on Examples 1 and 2, Example 3 of the present application provides an automatic tidal gully extraction system based on color space component water index replacement fusion, including:

[0104] A first acquisition module is used to acquire a target remote sensing image and pre-process the target remote sensing image;

[0105] The first calculation module is used to calculate the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image;

[0106] A synthesis module is used to perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; and perform HIS transformation on the color image, and use the obtained I component as a new water index NWI;

[0107] The second calculation module is used to calculate the gray level co-occurrence matrix GLCM of the NWI image to obtain the average feature;

[0108] A processing module, used for processing the average feature by optimized top-hat transformation;

[0109] A determination module, used for statistically determining an optimal threshold for extracting tidal gullies according to a result of processing by optimized top-hat transformation, and generating a binary image according to the optimal threshold;

[0110] The second acquisition module is used to perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

[0111] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiment 1. Therefore, the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be repeated in this application.

[0112] In summary, the present invention combines multiple water body images to synthesize a new color image, applies HIS transformation to the color image, and uses the I component as the NWI image. Then, the gray level co-occurrence matrix and the optimized top hat transformation method are used to reduce the influence of background heterogeneity on tidal gully extraction, and effectively enhance the linear characteristics of tidal gullies, which can better distinguish tidal gullies from the background, improve the accuracy of tidal gully extraction, especially in salt marsh tidal flats where wide and narrow tidal gullies coexist, and provide data support and reference for the automatic extraction of tidal gullies.

Claims

1. An automatic tidal gully extraction method based on water index replacement and fusion of color space components, characterized in that: include: Step 1: Acquire a target remote sensing image and preprocess the target remote sensing image; Step 2, calculating the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image; Step 3: Perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; perform HIS transformation on the color image, and use the obtained I component as the new water index NWI; Step 4: Calculate the gray level co-occurrence matrix GLCM of the NWI image, obtain and average features; Step 5: Processing the average feature by optimized top hat transformation; Step 6: Statistically determine the optimal threshold for extracting tidal gullies based on the results of processing with the optimized top-hat transform, and generate a binary image based on the optimal threshold; Step 7: Perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

2. The automatic tidal creek extraction method based on color space component water index replacement and fusion according to claim 1 is characterized in that: In step 2, the calculation formulas for NDWI, MNDWI and MuWI are: MuWI=-4ND(2,3)+2ND(3,8)+2ND(3,12)-ND(3,11) 3. Among them, Green is the green light band value, NIR is the near infrared band value, SWIR1 is the first shortwave infrared band value, and ND(i,j) represents the normalized difference between band i and band j. The automatic tidal gully extraction method based on color space component water index replacement fusion according to claim 2 is characterized in that: In step 3, MNDWI is used as the red component, NDWI as the green component, and MuWI as the blue component for color synthesis; the calculation formula of HIS transformation is: H=60H t (if H t <0,H t =Ht+6) Among them, R, G and B are the red component, green component and blue component respectively, max and min are the maximum and minimum values ​​of R, G and B of each pixel respectively, I represents the pixel brightness, S represents the color saturation of the pixel, and Ht is an intermediate variable used to calculate the hue H.

4. The automatic tidal gully extraction method based on color space component water index replacement and fusion according to claim 3 is characterized in that: In step 4, the calculation formula is: Gray=(0.3×B)+(0.59×R)+(0.11×GR) Where Gray is the gray value, i is the gray value of the first pixel, j is the gray value of the second pixel adjacent to the first pixel, f(x,y) is the pixel value of the image at position (x,y), (Δx,Δy) is the position offset of adjacent pixels calculated based on the distance d and the angle θ, and p(i,j) is the joint probability of two pixel pairs with gray values ​​i and j in GLCM.

5. The automatic tidal creek extraction method based on color space component water index replacement and fusion according to claim 4 is characterized in that: In step 5, the optimized top hat variation T o The calculation formula is: Among them, S o and S c Opening operation The structural element of the closed operation ·, I C is the complementary image of image I and is defined as follows: Where U is the full set of possible values ​​for each pixel; the complementary image I of image I C The operation of (x,y) is: I C (x,y)=max(U)-I(x,y) Where max(U) is the maximum possible pixel value and I(x,y) is the pixel value at position (x,y) of the original image.

6. The method for automatic extraction of tidal gullies based on replacement and fusion of color space component water index according to claim 5 is characterized in that: In step 6, the expression that needs to be satisfied for tidal gully extraction is: Among them, T o (x,y) is the T of position (x,y) o value, T o The threshold value.

7. An automatic tidal gully extraction system based on water index replacement and fusion of color space components, characterized in that: Used to perform the method according to any one of claims 1 to 6, comprising: A first acquisition module is used to acquire a target remote sensing image and pre-process the target remote sensing image; The first calculation module is used to calculate the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image; A synthesis module is used to perform color synthesis according to the normalized water index NDWI, the improved normalized water index MNDWI and the multispectral water index MuWI corresponding to the target remote sensing image to obtain a color image; and perform HIS transformation on the color image, and use the obtained I component as a new water index NWI; The second calculation module is used to calculate the gray level co-occurrence matrix GLCM of the NWI image to obtain the average feature; A processing module, used for processing the average feature by optimized top-hat transformation; A determination module, used for statistically determining an optimal threshold for extracting tidal gullies according to a result of processing by optimized top-hat transformation, and generating a binary image according to the optimal threshold; The second acquisition module is used to perform connected component analysis and morphological operations on the binary image to obtain the final result of tidal gully extraction.

8. A computer storage medium, characterized in that: The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 6.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 6.

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