CN115761493A - Water body extraction method based on combined water body index frequency - Google Patents
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Abstract
The invention discloses a water body extraction method based on combined water body index frequency, which comprises the following steps: s1, acquiring remote sensing image data and preprocessing the remote sensing image data; s2, constructing a water body index, and constructing an instantaneous water body extraction decision tree model according to the water body index; s3, identifying and extracting instantaneous water body pixels according to the instantaneous water body extraction decision tree model, distinguishing the water body, and preliminarily determining an average water body; and S4, extracting a water line by using a Canny edge detection method, and determining an average water body extraction result according to the output water body pixel value, the edge detection and the boundary elimination result. The method can realize average water body extraction based on the dense long time sequence images, improve the water body extraction precision, improve the effectiveness of water body extraction in the noise environment of construction shadows and terrain shadows, resist the interference of rolling cloud noise in the single-scene images, and enable the large-scale long time sequence water body extraction to have universality.
Description
Technical Field
The invention relates to a remote sensing extraction method, in particular to a water body extraction method based on combined water body index frequency.
Background
Water resources are one of the most important resources on the earth, and are the important material basis for the survival and development of all human beings and organisms, and the extraction of water body information has very important significance for researching the ecological environment of a region and the utilization and protection of water resources. The water body extraction from the remote sensing image is convenient for knowing the general view of the existing water resource, is beneficial to more reasonable planning and treatment of the water resource, improves the utilization efficiency of the water resource, and has great influence on human life and social activities.
Regarding the extraction of the water body, the extraction is mainly performed by methods such as a threshold value method, a filtering method, an elevation and gray level co-occurrence matrix, a model extraction by combining an optical image and a radar image and the like. In recent years, with the continuous emission of a large number of optical satellites such as Landsat series, sentinel-2, high-resolution series and the like, abundant high-resolution satellite multispectral remote sensing images which can be freely acquired are provided, so that the water body extraction of a spectral image by using a threshold method becomes a common method.
The single-band threshold method is used for extracting the water body based on the specific reflection rule of the near infrared band, the short wave infrared band and other bands in the water body, and is simple, easy to implement and convenient to operate. However, the single-band threshold method is limited in application environment, the types of the shelters existing on two sides of the watershed of the water body in the actual research area are complex, and the accuracy of the extraction result of the water body in the research area by using the single-band threshold method is not high.
The inter-spectrum relation method is an extraction method for increasing the land-water contrast by adopting a spectrometer to actually measure the reflectivity of a water body in a research area and surrounding terrains and analyzing the measured reflectivity to obtain the quantity relation between band combinations for distinguishing the water body according to the characteristic that the reflectivities of various ground object types of different spectrum bands under the water body and water body background. However, the method of using the inter-spectrum relationship requires targeted spectral measurement and analysis of the water body and the related typical objects in the research area, and the establishment of the inter-spectrum relationship also has certain difficulty, does not have universality, consumes a lot of time and energy, and is not suitable for large-scale water body extraction and analysis.
The water body index method achieves the purpose of extracting the water body by highlighting the land and water difference through the algorithm combination of adding and subtracting multipliers and the like on wave bands. Different construction modes are processes of performing operation on each wave band to achieve the purpose of highlighting water body information and inhibiting non-water body information so as to obtain the water body information. The water body index method forms a more universal method by utilizing abundant spectral information, so that the water body index method becomes a mainstream water body extraction method.
In the current water body extraction research, a certain water body index can be used for accurately extracting local water bodies, but the type of the global water body is various, and the global water body extraction precision is low by only using one water body index; in addition, single-scene images which mostly depend on a certain period are extracted from the current water body, and the obtained instantaneous water surface images cannot reflect the average state of the water body and are not convenient for reflecting regional climate and environmental changes.
Disclosure of Invention
The invention aims to: in order to overcome the defects in the prior art, the invention aims to provide a water body extraction method based on combined water body index frequency, which is suitable for determining the dense long-time extraction average water body and improving the extraction precision.
The technical scheme is as follows: the invention relates to a water body extraction method based on combined water body index frequency, which comprises the following steps:
s1, acquiring remote sensing image data and preprocessing the remote sensing image data;
s2, constructing a water body index, and constructing an instantaneous water body extraction decision tree model according to the water body index;
s3, according to the instantaneous water body extraction decision tree model, instantaneous water body pixels are identified and extracted, the water body is distinguished, and an average water body is preliminarily determined;
and S4, extracting a water line by using a Canny edge detection method, and determining an average water body extraction result according to the output water body pixel value, the edge detection and the boundary elimination result.
Further, in step S1, the remote sensing image data is dense long-time-series high-resolution remote sensing image data, and the preprocessing specifically includes:
s11, screening the remote sensing image data to obtain transit image data with cloud cover less than 20%;
and S12, converting dimensionless DN values in the screened transit image data into atmospheric top layer reflectivity, converting the atmospheric top layer reflectivity in the screened transit image data into surface reflectivity, and cutting out the image of the research area according to the screened transit image.
Further, in step S2, the specific steps of extracting the decision tree model are as follows:
s21, selecting and constructing five water body indexes of NDWI, MNDWI, RNDWI, AWEInsh, AWEIsh and EVI and one vegetation index;
s22, selecting the optimal threshold of various water body indexes by utilizing an Otsu threshold method;
s23, constructing an instantaneous water body extraction decision tree model according to different reflectivities of main feature interference factors in the test area, wherein the function relation is as follows:
NDWI.gt(0).and(AWEInsh.gt(-0.375).or(AWEIsh.gt(-0.172)).and(RNDWI.gt(0.004))).orMNDWI.gt(-0.002).and(EVI.lt(0.1))。
the NDWI index is greater than 0 and identifies most water body pixels, and the pixels with few NDWIs less than 0 are mistakenly divided into dark buildings and are omitted, and the MNDWI index is introduced on the basis, so that the MNDWI satisfies the condition that the operation value is greater than or equal to-0.002, the building interference is eliminated, and the vegetation interference is eliminated when the enhanced vegetation index operation value of the pixels is less than 0.1, and the water body in the dense area of the building is completely extracted.
The sludge and the water body are distinguished by combining an AWEInsh index and an RNDWI index for extraction, when the NDWI calculation value is more than 0, a large number of shadows and sludge pixels which are judged as the water body can be extracted, and the interference of dark buildings and roads is eliminated by using a threshold value of the AWEInsh at-0.375 as a segmentation point to an area which is less affected by the shadows. And (3) taking an AWEIsh threshold value of-0.172 as a segmentation point to eliminate shadow interference, and screening and extracting the land-water intersection boundary region by using RNDWI on the basis.
And establishing a decision tree model to obtain the final complete water body.
NDWI represents a normalized difference water body index, and the calculation formula is as follows:
MNDWI represents the improved normalized difference water body index, and the calculation formula is as follows:
RNDWI represents a revised normalized water body index, which is calculated by the formula:
the AWEI has two forms, namely AWEIsh and AWEInsh;
wherein, the calculation formula of AWEIsh is as follows:
AWEIsh＝Blue+2.5×Green-1.5×(NIR+SWIR1)-0.25×SWIR2
the calculation formula of AWEInsh is as follows:
AWEInsh＝4×(Green-SWIR1)-(0.25×NIR+2.75×SWIR2)
EVI represents the vegetation enhancement index, which is calculated by the formula:
in the formula, green represents the reflectivity of a Green light band; NIR represents the reflectance of the near infrared band; SWIR1 represents the reflectance of the short infrared band one; red represents the reflectance in the Red wavelength band; blue represents the reflectance of the Blue band; SWIR2 represents the reflectance of the short infrared band two.
Further, step S22 includes the steps of:
s221, calculating an optimal threshold value capable of separating the water body from the non-water body to enable the intra-class variance of the water body and the non-water body to be minimum, and performing analysis statistics on two types of pixels subjected to binarization segmentation according to the determined threshold value;
s222, acquiring a distinguishing value between two types of pixels through a statistical analysis result;
s223, minimizing intra-cluster variation by maximizing inter-cluster variance;
and S224, returning a single intensity value to obtain the optimal threshold for distinguishing the two types of pixels.
Further, in step S3, the specific step of preliminarily determining the average water body is as follows:
s31, counting the water body judgment result;
s32, calculating the water body frequency according to the statistical water body judgment result;
and S33, removing the low-quality extraction result, and primarily determining the average water body by using the water body frequency diagram.
The calculation formula of the water body frequency is as follows:
in the formula, F Water Representing the frequency of the water body; sigma N Water Representing the number of water body pixels in the open earth surface; sigma N Total Representing the total number of observations in one year; sigma N Bad Representing the number of inferior observations within one year; sigma N Total -∑N Bad Indicating a number of good observations.
In step S3, the water body is discriminated according to the following expression:
in the formula, UNION represents an instantaneous water body extraction decision tree model discrimination method provided according to different test point characteristics of a research area; values of 0 and 1 are expressed as non-water and water, respectively; and when the pixel value in the research area simultaneously satisfies that the enhanced vegetation index EVI is less than 0.1 and the UNION characteristic, the pixel can be judged as the water body.
Further, in step S4, the water sideline extraction specifically comprises:
s41, extracting a water line which is less influenced by a background value according to a Canny edge detection method;
and S42, removing the boundary of the artificial water area.
The Canny edge detection method comprises the following steps: removing noise of the image by using a Gaussian filter; solving gradient values and gradient directions; filtering the non-maximal inhibition values; an edge dual threshold is determined.
Has the advantages that: compared with the prior art, the invention has the following remarkable characteristics: the method has the advantages that the average water body extraction under the intensive long time sequence can be realized, the water body extraction precision is improved to 92.75%, the effectiveness of water body extraction under the environment with building shadow and terrain shadow noise is improved, the interference of rolling cloud noise in a single-scene image is resisted, the water body extraction with large scale and long time sequence is more universal, the technical support can be provided for large-scale remote sensing surface water monitoring, and the technical support is provided for reasonable planning, water resource treatment and ecological protection.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the instant Water extraction decision tree model of the present invention;
FIG. 3 is a schematic diagram of an instantaneous water body extraction result using a combined water body index according to the present invention, wherein a is a test point 1, b is a test point 2, and c is a test point 3;
FIG. 4 is a schematic view of the image frequency of the water body in the whole watershed of Jiangjiang Jiangsu section in 2016;
FIG. 5 is a schematic diagram of the mean water level water extraction results of the present invention;
FIG. 6 is a schematic diagram of the result of the waterside extraction according to the present invention.
Detailed Description
As shown in fig. 1, a water body extraction method based on combined water body index frequency specifically includes the following steps:
s1, acquiring remote sensing image data, and preprocessing the remote sensing image data, wherein the method comprises the following steps:
and S11, acquiring dense long-time high-resolution remote sensing image data.
S12, screening the remote sensing image data to obtain transit image data with cloud amount less than 20%.
And S13, carrying out radiometric calibration, atmospheric correction and cutting on the screened transit image data.
S2, constructing a water body index, and constructing an instantaneous water body extraction decision tree model according to the water body index, wherein the method comprises the following steps:
s21, selecting and constructing five water body indexes of NDWI, MNDWI, RNDWI, AWEInsh, AWEIsh and EVI and a vegetation index.
Specifically, NDWI represents a normalized difference water body index, and the calculation formula is as follows:
in the formula, green represents the reflectivity of a Green light band; NIR denotes the reflectance of the near infrared band. The NDWI can enhance the water body in the satellite image, the main purpose is to detect and monitor the tiny change of the water content of the water body, and the index has the defects that the index is sensitive to the building structure and cannot effectively inhibit building noise in urban areas and mountain shadow in mountain areas, which can cause overestimation of the water body.
MNDWI represents the improved normalized difference water body index, and the calculation formula is as follows:
in the formula, green represents the reflectivity of a Green light band; SWIR1 represents the reflectance of the short infrared band. The MNDWI can effectively restrain background noises of buildings, bare land and the like, and the precision of extracting the water body in the town range with dense buildings is higher.
RNDWI represents a revised normalized water body index, which is calculated by the formula:
where SWIR1 represents the reflectance in the short infrared band; red is the reflectance in the Red band. The influence of mountain shadow can be rejected to shortwave infrared wave band and ruddiness wave band, mainly used for the extraction of mire and shallow water.
In particular, the AWEI represents an automatic water body extraction index that consistently improves the accuracy of water body extraction in the presence of various environmental noises, while providing a stable threshold. The AWEI has two forms, namely AWEIsh and AWEInsh;
wherein, the calculation formula of AWEIsh is as follows:
AWEIsh＝Blue+2.5×Green-1.5×(NIR+SWIR1)-0.25×SWIR2
the calculation formula of AWEInsh is as follows:
AWEInsh＝4×(Green-SWIR1)-(0.25×NIR+2.75×SWIR2)
in the formula, green represents the reflectivity of a Green light wave band; BLUE represents the reflectivity of the BLUE band; NIR denotes reflectance in the near infrared band; SWIR1 and SWIR2 respectively indicate the reflectance of the short infrared band one and the short infrared band two.
Specifically, the AWEIsh is designed mainly for removing shadow pixels, and is easy to distinguish water body boundaries; AWEInsh is designed for areas with urban background, and is suitable for shadow areas.
Specifically, EVI represents a vegetation enhancement index, and the calculation formula is:
in the formula, NIR represents the reflectance in the near infrared band; red represents the reflectance of the Red wavelength band; blue represents the reflectance of the Blue band. The EVI has the advantages of a normalized vegetation index (NDVI), improves the problems of high vegetation zone saturation, incomplete atmospheric influence correction, soil background and the like, can improve the sensitivity of vegetation, reduces the influence of the soil background and the atmosphere, has higher sensitivity and superiority in monitoring vegetation change, and is widely applied to researches such as grassland degradation monitoring, grassland resource quantitative analysis and the like.
S22, selecting the optimal threshold of various water body indexes by using an Otsu threshold method, wherein the method comprises the following steps:
s221, traversing all possible threshold values and analyzing and counting the two types of pixels after threshold value segmentation.
S222, obtaining a distinguishing value between the two types of pixels through a statistical analysis result.
S223, minimizing intra-cluster variation by maximizing inter-cluster variance.
And S224, returning a single intensity value to obtain the optimal threshold for distinguishing the two types of pixels.
S23, constructing an instantaneous water body extraction decision tree model according to different reflectivities of main object interference factors in the test area.
S3, identifying and extracting the instantaneous water body pixels according to the instantaneous water body extraction decision tree model, and calculating the water body index frequency, wherein the method specifically comprises the following steps:
s31, identifying and extracting the instantaneous water body pixels according to the instantaneous water body extraction decision tree model, and distinguishing the water body.
The instantaneous water body pixel identification and extraction are carried out according to the instantaneous water body extraction decision tree model, and the water body is distinguished by the following expression:
in the formula, UNION represents an instantaneous water body extraction decision tree model discrimination method proposed according to different test point characteristics of a research area; values of 0 and 1 are respectively expressed as non-water body and water body; when the pixel value in the research area simultaneously meets the conditions that the enhanced vegetation index EVI is less than 0.1 and the UNION characteristic is met, the pixel can be judged as the water body.
And S32, counting the water body judgment result.
S33, calculating the water body frequency according to the statistical water body judgment result, wherein the calculation formula of the water body frequency is as follows:
in the formula, F Water Representing the frequency of the water body; sigma N Water Representing the number of water body pixels in the open surface; sigma N Total Representing the total number of observations in one year; sigma N Bad Representing the number of inferior observations within one year; sigma N Total -∑N Bad Indicating a number of good observations.
And S34, removing the low-quality extraction result, and primarily determining the average water body by using the water body frequency diagram.
S4, extracting water sidelines by using a Canny edge detection method, and determining an average water body extraction result, wherein the method comprises the following steps:
s41, extracting a water line which is less influenced by a background value according to a Canny edge detection method, and specifically comprising the following steps: removing noise of the image by using a Gaussian filter; solving gradient values and gradient directions; filtering the non-maximal inhibition values; an edge dual threshold is determined.
And S42, removing the boundary of the small artificial water area.
S43, determining an average water body extraction result according to the output water body pixel value, the edge detection result and the boundary elimination result.
The method of the embodiment is applied to a Yangtze river Jiangsu section with complex surface environment as a research area, and the Yangtze river shoreline extraction based on the high-precision water body extraction algorithm comprises the following steps:
based on a Google Earth Engine cloud platform, a high-precision water body extraction algorithm with operability in a large-scale environment is provided by combining a long-time-sequence image set in Sentinel-2MSI years with time characteristics of pixels, and the Yangtze river shoreline extraction is realized. The method comprises the steps of obtaining Sentinel-2 satellite remote sensing image data of the Yangtze river Jiangsu section as experimental data and water body true value images as verification data. The Sentinel-2 satellite data is obtained by screening a 2016-2021 year-round image data set by using a GEE platform, and screening a transit image with cloud amount less than 20% in 2016-2021 year as experimental data. The water body true value image is obtained by visually interpreting a 83-scene Sentinel-2 image in 2016-2021, selecting a plurality of scene image data for each month, visually interpreting the Sentinel-2 image, manually outlining a water body image vector diagram and a water body side line vector diagram, and acquiring the water body image under the water body mean water level by using an average value calculation tool as verification data for water body extraction. And preprocessing the image such as radiometric calibration, atmospheric correction, cutting and the like.
The water bodies in the research area are classified according to the geographical environment of the water bodies, and are divided into 3 types of water bodies in the areas with dense buildings and shadow water bodies, water bodies with rich vegetations and sludge water bodies which are not built. Selecting and constructing five water body indexes of NDWI, MNDWI, RNDWI, AWEInsh, AWEIsh and EVI and one vegetation index. And selecting an optimal threshold value for various water body indexes by using an Otsu threshold method, and carrying out sectional partition research in different threshold values. Referring to fig. 2, a transient water body extraction decision tree model is constructed, and the decision tree is constructed according to different reflectivities of main feature interference factors such as shadows, buildings, dark roads, vegetation and the like in a test area. The functional relationship is as follows:
NDWI.gt(0).and(AWEInsh.gt(-0.375).or(AWEIsh.gt(-0.172)).and(RNDWI.gt(0.004))).or MNDWI.gt(-0.002).and(EVI.lt(0.1))。
and (3) identifying most water body pixels with the NDWI index larger than 0, missing a small number of pixels with the NDWI smaller than 0 due to being wrongly divided into dark buildings, introducing the MNDWI index on the basis, eliminating building interference when the MNDWI meets the condition that the operation value is larger than or equal to-0.002, and eliminating vegetation interference when the enhanced vegetation index operation value of the pixels is smaller than 0.1, and completely extracting the water body in the dense area of the building.
The sludge and the water body are distinguished by extracting through combining an AWEInsh index and an RNDWI index, when the NDWI calculation value is larger than 0, a large number of shadows and sludge pixels which are judged as water bodies can be extracted, and the interference of dark buildings and roads is eliminated in the area which is less affected by the shadows by using the AWEInsh threshold value at-0.375 as a partitioning point. And (3) taking an AWEIsh threshold value of-0.172 as a segmentation point to eliminate shadow interference, and screening and extracting the land-water intersection boundary region by using RNDWI on the basis.
And establishing a decision tree model to obtain the final complete water body. Wherein, water represents a water body, and no water represents a non-water body.
As shown in fig. 3, the pixels are subjected to instantaneous water body pixel extraction and identification according to the constructed decision tree extraction rule. a is a test point 1, the edge bank contains a large number of buildings and shadow interference, and the edge bank is an artificial bank section in the building concentration; b is a silt shoreline with a large number of beaches and silt at the test point 2; c, the test points 3 comprise branches of the Yangtze river and vegetation land and are natural bank sections with rich vegetation;
counting the number of water pixels in the dense long-time-sequence image according to the following formula:
calculating the water body frequency according to the following formula:
pixels with mask values greater than 1 are identified as low quality observations, masked or excluded from the study.
The image frequency of the water body in the whole watershed of the Jiangjiang Jiangsu section in 2016 is shown in FIG. 4. And (3) performing a water body extraction experiment on the three test points by using a combined water body index frequency method, and finally determining that when the water body frequency is 0.63, the water body extraction result is closest to the water surface image under the average water level, the extraction precision is 92.75%, and the water body image extraction result is shown in fig. 5.
And extracting a water line which is less influenced by a background value by Canny edge detection according to the average water body of Jiangsu segments of Yangtze river from 2016 to 2021. The python script is used for removing the small culture fishpond boundary, and the specific operation codes are as follows:
as shown in fig. 6, the water line extraction result is obtained by shape screening.
In summary, according to the technical scheme of the invention, the optimal threshold of the water body index can be determined by using the Otsu threshold method, the instantaneous water body extraction decision tree model is constructed by using five water body indexes and one vegetation index, the instantaneous water body pixel identification is performed according to the water body extraction decision tree model, the water body frequency is calculated by counting the dense long-time-sequence image water body discrimination result, the water boundary line is extracted by using a Canny edge detection method, the average water body under the dense long-time sequence is finally determined, and the problems that the current water body extraction mostly depends on a single-scene image in a certain period, the water body extraction precision is low, the research range is small and the like are solved. The method can realize average water body extraction under the intensive long time sequence, improve the water body extraction precision, improve the effectiveness of water body extraction under the environment with construction shadow and terrain shadow noise, resist the interference of rolling cloud noise in a single-scene image, enable the water body extraction with large scale and long time sequence to have universality, provide technical support for large-scale remote sensing surface water monitoring, and provide technical support for reasonable planning and water resource treatment and ecological protection.
Claims (10)
1. A water body extraction method based on combined water body index frequency is characterized by comprising the following steps:
s1, acquiring remote sensing image data and preprocessing the remote sensing image data;
s2, constructing a water body index, and constructing an instantaneous water body extraction decision tree model according to the water body index;
s3, according to the instantaneous water body extraction decision tree model, instantaneous water body pixels are identified and extracted, the water body is distinguished, and an average water body is preliminarily determined;
and S4, extracting a water line by using a Canny edge detection method, and determining an average water body extraction result according to the output water body pixel value, the edge detection result and the boundary elimination result.
2. The water body extraction method based on the combined water body index frequency as claimed in claim 1, wherein: in the step S1, the remote sensing image data is dense long-time-series high-resolution remote sensing image data, and the preprocessing specifically includes:
s11, screening the remote sensing image data to obtain transit image data with cloud cover less than 20%;
and S12, converting dimensionless DN values in the screened transit image data into atmospheric top layer reflectivity, converting the atmospheric top layer reflectivity in the screened transit image data into surface reflectivity, and cutting out the image of the research area according to the screened transit image.
3. The water body extraction method based on the combined water body index frequency as claimed in claim 1, wherein: in step S2, the specific steps of extracting the decision tree model are as follows:
s21, selecting and constructing five water body indexes of NDWI, MNDWI, RNDWI, AWEInsh, AWEIsh and EVI and one vegetation index;
s22, selecting the optimal threshold values of various water body indexes by using an Otsu algorithm;
s23, constructing an instantaneous water body extraction decision tree model according to different reflectivities of main feature interference factors in the test area.
4. The water body extraction method based on the combined water body index frequency as claimed in claim 3, wherein: the NDWI represents a normalized difference water body index, and the calculation formula is as follows:
the MNDWI expresses an improved normalized difference water body index, and the calculation formula is as follows:
the RNDWI represents a revised normalized water body index, and the calculation formula is as follows:
the AWEI has two forms, namely AWEIsh and AWEInsh;
wherein, the calculation formula of AWEIsh is as follows:
AWEIsh＝Blue+2.5×Green-1.5×(NIR+SWIR1)-0.25×SWIR2
the calculation formula of AWEInsh is as follows:
AWEInsh＝4×(Green-SWIR1)-(0.25×NIR+2.75×SWIR2)
the EVI represents a vegetation enhancement index, and the calculation formula is as follows:
in the formula, green represents the reflectivity of a Green light band; NIR represents the reflectance of the near infrared band; SWIR1 represents the reflectance of the short infrared band one; red represents the reflectance of the Red wavelength band; blue represents the reflectance of the Blue band; SWIR2 represents the reflectance of the short infrared band two.
5. The water body extraction method based on the combined water body index frequency as claimed in claim 3, wherein: the S22 includes the steps of:
s221, calculating an optimal threshold value capable of separating the water body from the non-water body to enable the intra-class variance of the water body and the non-water body to be minimum, and performing analysis statistics on two types of pixels subjected to binarization segmentation according to the determined threshold value;
s222, acquiring a distinguishing value between two types of pixels through a statistical analysis result;
s223, minimizing intra-cluster variation by maximizing inter-cluster variance;
and S224, returning a single intensity value to obtain the optimal threshold for distinguishing the two types of pixels.
6. The water body extraction method based on the combined water body index frequency as claimed in claim 1, wherein: in the step S3, the specific step of preliminarily determining the average water body is as follows:
s31, counting the water body judgment result;
s32, calculating the water body frequency according to the statistical water body judgment result;
and S33, removing the low-quality extraction result, and primarily determining the average water body by using the water body frequency diagram.
7. The water body extraction method based on the combined water body index frequency as claimed in claim 6, wherein: the calculation formula of the water body frequency is as follows:
in the formula, F Water Representing the frequency of the water body; sigma N Water Representing the number of water body pixels in the open surface; sigma N Total Representing the total number of observations in one year; sigma N Bad Representing the number of inferior observations within one year; sigma N Total -∑N Bad Indicating a number of good observations.
8. The water body extraction method based on the combined water body index frequency as claimed in claim 1, wherein: in step S3, the expression for distinguishing the water body is as follows:
in the formula, UNION represents an instantaneous water body extraction decision tree model discrimination method proposed according to different test point characteristics of a research area; values of 0 and 1 are expressed as non-water and water, respectively; and when the pixel value in the research area simultaneously satisfies that the enhanced vegetation index EVI is less than 0.1 and the UNION characteristic, the pixel can be judged as the water body.
9. The water body extraction method based on the combined water body index frequency as claimed in claim 1, wherein: in the step S4, the water sideline extraction specifically comprises the following steps:
s41, extracting a water line which is less influenced by a background value according to a Canny edge detection method;
and S42, removing the boundaries of the artificial water area.
10. The water body extraction method based on the combined water body index frequency as claimed in claim 9, wherein: the Canny edge detection method comprises the following steps: removing noise of the image by using a Gaussian filter; solving gradient value and gradient direction; filtering the non-maximal inhibition values; an edge dual threshold is determined.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117315489A (en) * | 2023-11-23 | 2023-12-29 | 云南师范大学 | Water body extraction method and device based on local background characteristic information |
| CN117690041A (en) * | 2024-01-31 | 2024-03-12 | 南方海洋科学与工程广东省实验室(珠海) | Dynamic water body extraction method and system based on static satellite remote sensing data |
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2022
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117315489A (en) * | 2023-11-23 | 2023-12-29 | 云南师范大学 | Water body extraction method and device based on local background characteristic information |
| CN117315489B (en) * | 2023-11-23 | 2024-07-05 | 云南师范大学 | Water body extraction method and device based on local background characteristic information |
| CN117690041A (en) * | 2024-01-31 | 2024-03-12 | 南方海洋科学与工程广东省实验室(珠海) | Dynamic water body extraction method and system based on static satellite remote sensing data |
| CN117690041B (en) * | 2024-01-31 | 2024-04-12 | 南方海洋科学与工程广东省实验室(珠海) | Dynamic water body extraction method and system based on static satellite remote sensing data |
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