CN113177979A - Water pollution area identification method and system based on multispectral image - Google Patents
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Abstract
The invention discloses a water pollution area identification method and a system based on multispectral images, which are used for acquiring spectral images of a target water area and acquiring spectral difference images representing the spectral change degree of each grid unit through a spectral difference function; based on the spectrum difference image, a pollution probability distribution map is obtained by using a difference classification function; and setting a threshold value, and extracting a pollution pattern spot from the pollution probability distribution map. The identification method provided by the invention directly detects the polluted area according to the change characteristics of the spectrum on the space without prior knowledge, ground calibration is not required, atmospheric correction is not required, a large number of samples are not required for supporting, the result is less influenced by the atmosphere, the utilization rate of the multispectral image is improved, and the application scene is wide.
Description
Technical Field
The invention belongs to the field of pollution monitoring, and particularly relates to a water pollution area identification method and system based on a multispectral image.
Background
The traditional water pollution monitoring adopts a point monitoring method, namely, a plurality of point locations are selected in a flow field, and a water quality automatic monitoring station is arranged or a manual sampling and testing method is adopted to periodically evaluate the water quality of the point locations. The method only reflects the water quality condition of the sampling point, but cannot reflect the pollution distribution condition in the whole drainage basin.
With the development of remote sensing technology, multispectral and hyperspectral satellite remote sensing is used for identifying polluted areas of drainage basins. The method belongs to surface monitoring, the whole drainage basin is divided into grid units with continuous space, a water quality parameter value of each unit grid is calculated by using a water quality parameter inversion model, and finally, a polluted area is identified through grid statistics or comparison with a standard value. The method can comprehensively reflect the pollution distribution condition of the whole watershed, but the used water quality parameter inversion model needs enough sample support with reliable quality, and the historical image at any moment is difficult to be used for pollution area analysis under the condition of insufficient experimental accumulation.
The water quality parameter quantitative inversion model is the key for identifying the polluted area, the model construction uses a data pair of 'water quality parameter-multispectral reflectivity' as a support, the number of sample points is required to be enough, and the quality of a water quality parameter test result and a multispectral reflectivity calculation result is reliable, but the following problems are always existed in the actual work:
(1) the atmospheric correction process for calculation of multispectral reflectance products does not have a method suitable for all areas at present, and the commonly used 6S, MODTRAN atmospheric correction model requires a large number of atmospheric parameters as input, such as CO2Concentration, aerosol thickness and the like, which are difficult to obtain in practical application, are often replaced by model default values, so that local and current atmospheric conditions cannot be completely simulated, and thus a multispectral reflectivity calculation result has certain errors;
(2) the water quality parameters can be obtained through an automatic water quality monitoring station or a manual assay mode, samples for modeling need to be matched with satellite images in time and space, and sampling data belong to the same type, such as the data of the automatic water quality monitoring station or the detection assay results of the same mechanism. Under general conditions, the number of samples meeting the use condition in a basin is less than 10, and the construction of a water quality parameter inversion model is limited.
Disclosure of Invention
The invention aims to provide a water pollution area identification method and system based on multispectral images, aiming at the defects of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme: a water pollution area identification method based on multispectral images is characterized by comprising the following steps:
step 1: acquiring a spectral image of a target water area through a spectral difference function D (lambda)1,λ2,λ3,...,λn) Converting the spectral image into a spectral difference image D;
step 2: based on the spectrum difference image, acquiring a pollution probability distribution map P by using a difference classification function F;
and step 3: setting a threshold value, and extracting a target pollution pattern spot from the pollution probability distribution map P.
Further, in step 1, the spectral difference function D (λ)1,λ2,λ3,...,λn) By extracting from n kinds of spectral data of the spectral image, wherein n ≧ 1.
Further, in step 1, the spectral difference function D (λ)1,λ2,λ3,...,λn) Spectral difference image D (lambda) of the ith wavebandi) And obtaining by calculating the difference of the spectral data of each grid unit.
Further, in step 2, the difference classification function F eliminates interference factors of the spectral difference image by the image features of the target.
A water contamination region identification system based on multispectral images, comprising:
a spectrum difference analysis module for acquiring a spectrum image of the target water area by a spectrum difference function D (lambda)1,λ2,λ3,...,λn) Converting the spectral image into a spectral difference image D;
the difference classification module is used for acquiring a pollution probability distribution map P by using a difference classification function F based on the spectrum difference image; and the pollution area extraction module is used for setting a threshold value and extracting a target pollution pattern spot from the pollution probability distribution map P.
Further, a spectral difference function D (λ) in the spectral difference analysis module1,λ2,λ3,...,λn) By extracting from n kinds of spectral data of the spectral image, wherein n ≧ 1.
Further, a spectrum difference function D (lambda) in the spectrum difference analysis module1,λ2,λ3,...,λn) Spectral difference image D (lambda) of the ith wavebandi) And obtaining by calculating the difference of the spectral data of each grid unit.
Further, the difference classification function F in the difference classification module eliminates interference factors of the spectral difference image through the image characteristics of the target.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of the preceding claims.
The water pollution area identification method provided by the invention directly identifies the area possibly polluted through the change characteristics of the spectrum in the space, avoids the problem of water quality parameter inversion model construction caused by incomplete atmospheric correction and insufficient sample quantity, and improves the utilization rate of multispectral images.
And abnormal noise is eliminated through the spectrum and morphological characteristics of different objects, and the accuracy of the judgment of the polluted area is improved.
The water pollution area identification method and the water pollution area identification system have the beneficial effects that: under the condition of no prior knowledge, the pollution area is directly detected according to the change characteristics of the spectrum in the space, a large number of samples are not needed for supporting, the influence of the atmosphere on the result is small, and the utilization rate of the multispectral image is improved.
Drawings
Fig. 1 is a flowchart of an embodiment of a water pollution area identification method based on a multispectral image.
Fig. 2 is a satellite image according to example 1.
FIG. 3 is a geometry corrected multi-spectral reflectance product of example 1.
Fig. 4 is a multispectral image of the water area range of example 1.
FIG. 5 is a multispectral image of the contaminated area identified in example 1.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of this application and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A method for identifying a water pollution area based on a multispectral image, with a flow shown in fig. 1, includes the following steps:
step 1: acquiring a spectral image of a target water area through a spectral difference function D (lambda)1,λ2,λ3,...,λn) Acquiring a spectrum difference image D representing the spectrum change degree of each grid unit;
step 2: based on the spectrum difference image, utilizing a difference classification function F (I, A, C, D) to obtain a pollution probability distribution map P;
and step 3: and setting a threshold value, and extracting a pollution pattern spot from the pollution probability distribution graph.
In the step 1, the spectrum difference function D is used for acquiring the change degree of the spectrum of each grid unit;
in the step 1, a spectral difference function D is extracted from n kinds of spectral data of a multispectral image, wherein n is more than or equal to 1;
spectral difference image D (lambda) of the ith wavelength bandi) Calculating the difference of the spectral data of each grid unit through basic function operation to obtain the spectral data;
the difference acquisition mode of the spectrum data of each grid unit is to calculate the difference between the spectrum of each grid and m grids around the grid, wherein m is more than or equal to 2; the difference acquisition is obtained by a basis function operation, and the calculation method is not limited to ratio, difference, square root, arithmetic square root, and the like.
And (3) in the step 2, a difference classification function F aims to eliminate interference factors through the characteristic difference of the image.
The exclusion method in step 2 is not limited to morphological features, optical features, etc. based on the target contamination pattern.
And 3, setting a threshold value according to the results of the step 1 and the step 2 and actual application setting, and finally extracting a pollution pattern spot from the pollution probability distribution map.
Spectral difference function D (lambda) in step 11,λ2,λ3,...,λn) One of the specific expression forms of (a) is as follows:
D＝max(D(λ1),D(λ2),D(λ3),...,D(λn)) (1)
wherein D is the final spectral difference image, D (lambda)i) For the spectral difference image of the ith wavelength band, D (λ i) is calculated as follows:
D(λi)＝D0(λi)+D1(λi)+D2(λi)+D3(λi)+D4(λi) (2)
D0(λi)＝(R(λi)x,y-μ(λi))2 (3)
D1(λi)＝(R(λi)x+1,y-1-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y+1) (4)
D2(λi)＝(R(λi)x+1,y-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y) (5)
D3(λi)＝(R(λi)x+1,y+1-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y-1) (6)
D4(λi)＝(R(λi)x,y+1-R(λi)x,y)+(R(λi)x,y-R(λi)x,y-1) (7)
wherein, R (lambda)i)x,yFor spectral images lambdaiSpectral value of the picture element (x, y) in the band, mu (lambda)i) Is the spectral average of the ith band. D is set to 0 less than the threshold.
The difference classification function in step 2 is F (I, a, C, D), one expression form of which is that I is a component characterizing the reflection intensity, and A, C is the area and the perimeter, respectively, for statistical morphological features.
Step 2.1: setting the pixel with the spectral difference value larger than 0 as 1, performing open operation on the image, eliminating the isolated pixel and communicating adjacent image spots;
step 2.2: calculating the area A and the perimeter C of the image spot, and calculating a shape index S which is A/C;
step 2.3: calculating an intensity component I, wherein the intensity component I can be near-infrared reflectivity or a brightness component;
step 2.5: and calculating a pollution probability distribution graph P (D) S (1-I).
The image features of step 3 include, but are not limited to, morphological features of the image, brightness distribution features of the image after color conversion, or some other common image feature recognition methods. The interference factors can be eliminated and the communication pattern spots can be further processed by one or a plurality of methods.
The step 3 is realized by the following steps:
(1) calculating a shape index C of the Unicom image spots, wherein A is the area of the image spots, L is the perimeter of the image spots, the image element of the image spots with the C larger than C1 is set to be 0 and used for eliminating water surface noise, and the value range of C1 is 0-0.05;
(2) carrying out RGB-HSV conversion on the Unicom graphic spot, wherein H is a hue component, S is a saturation component, V is a brightness component, pixels of the saturation component S > S1 and the brightness component V > V1 are set to be 0, and the value ranges of S1 and V1 are 0-0.4;
(3) the non-0 pixel is the identification result of the polluted area.
A water contamination region identification system based on multispectral images, comprising:
the spectrum difference analysis module is used for acquiring a spectrum image of a target water area and converting the spectrum image into a spectrum difference image D through a spectrum difference function;
the difference classification module is used for acquiring a pollution probability distribution map P by using a difference classification function F based on the spectrum difference image;
and the pollution area extraction module is used for setting a threshold value and extracting a target pollution pattern spot from the pollution probability distribution map P.
Spectral difference function D (λ)1,λ2,λ3,...,λn) One of the specific expression forms of (a) is as follows:
D＝max(D(λ1),D(λ2),D(λ3),...,D(λn)) (1)
wherein D is the final spectral difference image, D (lambda)i) For the spectral difference image of the ith wavelength band, D (λ i) is calculated as follows:
D(λi)＝D0(λi)+D1(λi)+D2(λi)+D3(λi)+D4(λi) (2)
D0(λi)＝(R(λi)x,y-μ(λi))2 (3)
D1(λi)＝(R(λi)x+1,y-1-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y+1) (4)
D2(λi)＝(R(λi)x+1,y-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y) (5)
D3(λi)＝(R(λi)x+1,y+1-R(λi)x,y)+(R(λi)x,y-R(λi)x-1,y-1) (6)
D4(λi)＝(R(λi)x,y+1-R(λi)x,y)+(R(λi)x,y-R(λi)x,y-1) (7)
wherein, R (lambda)i)x,yFor spectral images lambdaiSpectral value of the picture element (x, y) in the band, mu (lambda)i) Is the spectral average of the ith band. D is set to 0 less than the threshold.
The difference classification function in the difference classification module is F (I, A, C, D) D.S (1-I), wherein I is a component for representing the reflection intensity, A, C is the area and the perimeter respectively, D is a spectrum difference image, a pollution probability distribution map P sets a pixel with a spectrum difference value larger than 0 in the spectrum difference image as 1, the image is operated, an isolated pixel is eliminated, and adjacent image spots are communicated; calculating the area A and the perimeter C of the unicom image spot, and calculating a morphology index S which is A/C; calculating intensity components I of the connected pattern spots, wherein the I is near infrared reflectivity or brightness components; and calculating a pollution probability distribution graph through P ═ D · S · (1-I).
The contaminated area extraction module includes: the morphological analysis submodule is used for calculating a morphological index C of the pollution probability distribution diagram connected with the image spots, wherein C is A/L, A is the area of the image spots, L is the perimeter of the image spots, the image element of the image spot with C larger than C1 is set to be 0 and used for eliminating water surface noise, and the value range of C1 is 0-0.05; the color analysis submodule is used for carrying out RGB-HSV conversion on the communicated pattern spot, wherein H is a hue component, S is a saturation component, V is a brightness component, pixels of the saturation component S > S1 and the brightness component V > V1 are set to be 0, and the value ranges of S1 and V1 are 0-0.4; and the identification submodule is used for identifying the non-0 pixel as the target pollution pattern spot.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above.
Example 1
(1) Acquiring a 9-month high-resolution first satellite image (as shown in figure 2) of a certain region 2020;
(2) carrying out radiation correction on the satellite image by using a radiation calibration coefficient, carrying out atmospheric correction on the satellite image by using a FLAASH atmospheric correction method, carrying out orthotropic correction on the satellite image by using RPC parameters, manually selecting a control point to carry out geometric fine correction on the image, and obtaining a multispectral reflectivity product (shown in figure 3) subjected to geometric correction;
(3) extracting a water area range by using the NDWI water body index, and extracting a multispectral image of the water area range by using the water area range as a mask (as shown in figure 4);
(4) calculating the variance of each pixel, and setting the pixel with the variance less than 50 as 0;
(5) after carrying out Unicom processing on the non-0 pixels, calculating the shape index of each image spot, and setting the pixels with the shape index larger than 0.018 (the numerical values of different areas are determined according to empirical values) as 0;
(6) performing RGB-HSV conversion on the pixels which are not 0, and setting the pixels with saturation (S) larger than 0.18 and brightness (V) larger than 0.2 as 0;
(7) outputting non-0 image spots, namely identified contaminated areas (regions) (as shown in figure 5).
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A water pollution area identification method based on multispectral images is characterized by comprising the following steps:
step 1: acquiring a spectral image of a target water area through a spectral difference function D (lambda)1,λ2,λ3,...,λn) Converting the spectral image into a spectral difference image D;
step 2: based on the spectrum difference image, acquiring a pollution probability distribution map P by using a difference classification function F;
and step 3: setting a threshold value, and extracting a target pollution pattern spot from the pollution probability distribution map P.
2. The method for identifying a water-polluted region based on a multispectral image according to claim 1, wherein: said step (c) is1 spectral difference function D (lambda)1,λ2,λ3,...,λn) By extracting from n kinds of spectral data of the spectral image, wherein n ≧ 1.
3. The method for identifying a water-polluted region based on a multispectral image according to claim 1, wherein: spectral difference function D (lambda) in said step 11,λ2,λ3,...,λn) Spectral difference image D (lambda) of the ith wavebandi) And obtaining by calculating the difference of the spectral data of each grid unit.
4. The method for identifying a water-polluted region based on a multispectral image according to claim 1, wherein: and the difference classification function F in the step 2 eliminates interference factors of the spectrum difference image through the image characteristics of the target.
5. A water pollution area identification system based on a multispectral image, comprising:
a spectrum difference analysis module for acquiring a spectrum image of the target water area by a spectrum difference function D (lambda)1,λ2,λ3,...,λn) Converting the spectral image into a spectral difference image D;
the difference classification module is used for acquiring a pollution probability distribution map P by using a difference classification function F based on the spectrum difference image;
and the pollution area extraction module is used for setting a threshold value and extracting a target pollution pattern spot from the pollution probability distribution map P.
6. The multispectral image-based water contamination region identification system according to claim 5, wherein: a spectral difference function D (λ) in the spectral difference analysis module1,λ2,λ3,...,λn) By extracting from n kinds of spectral data of the spectral image, wherein n ≧ 1.
7. The multiple light based of claim 5Spectral image's water pollution regional identification system, its characterized in that: a spectral difference function D (lambda) in the spectral difference analysis module1,λ2,λ3,...,λn) Spectral difference image D (lambda) of the ith wavebandi) And obtaining by calculating the difference of the spectral data of each grid unit.
8. The multispectral image-based water contamination region identification system according to claim 5, wherein: and the difference classification function F in the difference classification module excludes interference factors of the spectrum difference image through the image characteristics of the target.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program, when executed by a processor, implementing the steps of the method of any one of claims 1 to 4.
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| CN114993965B (en) * | 2022-05-13 | 2023-04-18 | 中煤嘉沣(湖南)环保科技有限责任公司 | Automatic pollution source identification method and system |
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