CN107895169A - A kind of method based on ENVISAT ASAR dual polarizations data extraction wetland information - Google Patents
A kind of method based on ENVISAT ASAR dual polarizations data extraction wetland information Download PDFInfo
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Abstract
The invention discloses a kind of method based on ENVISAT ASAR dual polarizations data extraction wetland information, this method includes:1) ENVISAT ASAR dual polarization data are pre-processed, including radiation calibration, mutually filtering, registration, ortho-rectification；2) pretreated ASAR dual polarizations data mining polarization information is based on, constructs ASAR multiband backscattering coefficient collection；3) to the multiband backscattering coefficient set analysis of construction the backscattering coefficient of its corresponding atural object change；4) difference of each wave band backscattering coefficient of different atural objects based on extraction carries out machine learning and classification to atural object, and based on this classification results extraction wetland information.Reflection of the dual polarization information and its institute's implicit information that present invention comprehensive utilization ASAR dual polarization data are included to atural object, region wetland information (including area and locus distribution etc.) is extracted, can be provided for wetland information not by the more efficiently technological means of Influence of cloud.
Description
Technical Field
The invention relates to a wetland information extraction method based on ENVISAT ASAR dual-polarized data, which comprehensively utilizes polarization information contained in the ASAR dual-polarized data and reflection of excavated information on the type of ground object coverage to extract wetland information and belongs to the field of geography and biotechnology.
Background
The wetland is a comprehensive body with rich biological diversity between land and sea, is a general term for coastal beaches, marshlands, peat lands, reservoirs, lakes and the like, and is one of seven large land coverage types in the world. In recent years, wetland systems have just become a research hotspot of scholars at home and abroad, and the wetland monitoring and protecting research becomes a key point of wetland remote sensing research. The wetland information extraction is the basic application of wetland remote sensing research, and is a cornerstone for implementing wetland dynamic monitoring and developing other related research works by accurately and efficiently extracting the spatial distribution and area information of wetland resources.
In order to clearly describe the effect of the wetland on the biodiversity of human beings, the control of the spatial distribution and the area of the wetland is very important, so that people are interested in the wetland information extraction research. Some scholars measure the wetland range directly through a reconnaissance mode, and other researchers research land coverage type classification based on optical remote sensing images to obtain the spatial distribution information of the wetland, or research the reflection of ground object scattering properties through microwave remote sensing backscattering coefficients to extract the wetland information. Since the optical image is subject to atmospheric conditions, it is difficult to obtain an effective image in a cloudy rain area. In addition, the wetland area is generally rare, and the sampling of the real sample points is difficult.
Disclosure of Invention
In order to solve the existing problems, the invention discloses a wetland information extraction method based on ENVISAT ASAR dual-polarized data, which has the following specific technical scheme:
a wetland information extraction method based on ENVISAT ASAR dual-polarized data comprises the following operation steps:
step 1, preprocessing ASAR dual-polarized data, including radiometric calibration, filtering, image registration and orthorectification;
step 2, carrying out polarization information mining based on the ASAR dual-polarization data to construct an ASAR multi-waveband data set;
step 3, analyzing the change characteristics of the backscattering coefficient of different ground objects on the constructed multiband image;
and 4, performing machine learning and classification on the ground objects based on the extracted difference of the backscattering coefficients of different ground objects in each wave band, and extracting wetland information based on the classification result.
The radiometric calibration in the step 1 is realized by the following formula:
the radiometric calibration formula is:
in the formula (I), the compound is shown in the specification,is the backscattering coefficient, DN is the image brightness value, K is the absolute scaling constant, G (θ) mn ) 2 For antenna gain, R mn Is a pitch, R ref For reference, the pitch (800 KM), α mn Is the angle of incidence.
The filtering in the step 1 adopts a Lee self-adaptive filtering algorithm, namely, a vertical mask is adopted to determine the most homogeneous part based on the region statistical sliding window, the algorithm can effectively remove noise and retain edge information, and the core formula is as follows:
k＝var(X)/var<Y>＝(var<Y>-E 2 (<Y>)σ 2 n )/(var<Y>[1+σ 2 n ])
withσ 2 n ＝1/L
wherein, the first and the second end of the pipe are connected with each other,is an estimate of X, nw is a window of size NxN,average of NxN window regions of Y, k is adaptive filteringWave coefficient, var (X) is the regional statistical variance of X, var<Y>, is the statistical variance of the region of Y,<> is the area average operation, σ 2 n The speckle noise variance is the square of the ratio of the standard deviation to the observed mean, and L is the view.
The specific implementation process of the step 2 is as follows:
firstly, ASAR dual-polarized data is subjected to band operation:
B M ＝B HH -B VV
wherein, B M Is the difference between the backscattering coefficients of HH polarization and VV polarization, B D Is the ratio of the backscattering coefficients of HH polarization and VV polarization, B HH Is HH polarization backscattering coefficient, B VV Is the VV polarization backscattering coefficient, B HH And B VV Are the backscatter coefficients after radiometric calibration, filtering, image registration and orthorectification in step 1.
Then, HH polarization backscatter coefficient, VV polarization backscatter coefficient, B M And B D And (5) carrying out wave band synthesis and constructing multi-wave band data.
The step 3 comprises the following steps: and (3) the change rule of the backscattering coefficient of the typical ground object types of the agricultural land, the bare land, the construction land, the water body or the wetland on the multiband image constructed in the step (2).
The multi-band data comprises HH, VV and B M And B D The backscattering coefficients of different polarizations of different types of ground objects can also be different. The concrete expression is as follows:
(1) The tone of the agricultural land is more uniform on an HH polarized image, the tone of the agricultural land is mottled on a VV polarized image, the surface is uneven, and the tone of the agricultural land is B D The image is shown as bright tone, which indicates that the scattering effect of the body is strong. HH polarization reflects most of the leaf information of agricultural crops, the hue is uniform, and the backscattering coefficient distribution is concentrated; the VV polarization penetration capacity is stronger, and the reflection is thatThe crop stem and soil roughness and humidity information have large variation of backscattering coefficient; b is D The image reflects the volume scattering intensity of the target, B for agricultural land D The value of (a) is relatively high.
(2) The bare land is darker in color tone on HH and VV polarization images, lower in backscattering coefficient, slightly higher than that of a water body and lower than that of an agricultural land.
(3) The construction land is bright white on HH polarization and VV polarization images, the backscattering coefficient is large, and B is caused by multiple scattering of the construction land D Relatively high value, compared to the farm land B D Compared to slightly higher.
(4) The water body generally shows mirror reflection on a radar image, the tone of the water body on an HH polarization image is uniform and is obvious with the boundary of surrounding ground objects, the backscattering coefficient is close to 0, the tone of the water body on the VV polarization image is inconsistent, the information is rich and is not obvious with the boundary of the surrounding ground objects, and the backscattering coefficient is larger than the HH polarization image, so that part of underwater or underwater information is reflected.
(5) The wetland has water body information and vegetation information, has uniform tone on an HH polarization image and obvious boundary with surrounding ground objects, has different tone on a VV polarization image, and has a lower HH band backscattering coefficient and a higher water body than those of agricultural land. The backscattering coefficient of the wetland in the VV polarized image is lower than that of the construction land, but higher than that of other land types.
The specific implementation process of the step 4 is as follows:
(1) Establishing a decision model by adopting a tree structure according to the quantitative relation between the surface feature type and the backscattering coefficient in the step 3;
(2) Performing decision analysis on the whole image by adopting a Classification and regression tree algorithm (Classification and regression tree algorithm) to obtain a Classification result graph;
(3) Classifying and post-processing the classification result based on the sample plot measured data, eliminating fragments, merging corresponding classes, and the like;
(4) And extracting wetland information according to the adjusted classification result graph, counting the wetland area, and making a wetland space distribution graph.
The working principle of the invention is as follows:
according to the method, firstly, ENVISAT ASAR dual-polarized data is preprocessed, secondly, information implied by the dual-polarized data is mined, then machine learning is adopted based on the mined information, ground object coverage type classification is carried out on a research area, and finally wetland information is extracted.
The invention has the beneficial effects that:
according to the invention, machine learning and classification are carried out on a research area by adopting ENVISAR ASAR dual-polarized data and implicit information thereof and combining priori knowledge, and further wetland information including spatial distribution and area is extracted.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is further elucidated with reference to the drawings and the detailed description. It should be understood that the following detailed description is illustrative of the invention only and is not intended to limit the scope of the invention.
Fig. 1 is a schematic structural diagram of the present invention, and it can be seen from the accompanying drawings that the wetland information extraction method based on the enviat ASAR dual-polarized data includes the following operation steps:
step 1, preprocessing ASAR dual-polarized data, including radiometric calibration, filtering, image registration and orthorectification; the radiometric calibration formula is:
in the formula (I), the compound is shown in the specification,is the backscattering coefficient, DN is the image brightness value, K is the absolute scaling constant, G (theta) mn ) 2 For antenna gain, R mn Is a pitch, R ref As a reference pitch (800 KM), α mn Is the angle of incidence.
The filtering in the step 1 adopts a Lee self-adaptive filtering algorithm, namely, a vertical mask is adopted to determine the most homogeneous part based on the region statistical sliding window, the algorithm can effectively remove noise and retain edge information, and the core formula is as follows:
k＝var(X)/var<Y>＝(var<Y>-E 2 (<Y>)σ 2 n )/(var<Y>[1+σ 2 n ])
with σ 2 n ＝1/L
where k is the adaptive filter coefficient, var (X), var<Y>, is the statistical variance of the region,<> is the area average operation, σ 2 n Is a priori coherent speckle variant.
Step 2, carrying out polarization information mining based on the ASAR dual-polarized data, and constructing an ASAR multi-waveband data set; the specific implementation process is as follows:
firstly, ASAR dual-polarized data is subjected to band operation:
B M ＝B HH -B VV
wherein, B M Is the difference between the backscattering coefficients of HH polarization and VV polarization, B D Is the ratio of the back scattering coefficients of HH polarization and VV polarization, B HH Is HH polarization backscattering coefficient, B VV Is the VV polarization backscattering coefficient, B HH And B VV Are the backscatter coefficients after radiometric calibration, filtering, image registration and orthorectification by step 1.
Then, HH polarization backscattering coefficient and VV polarization are performedCoefficient of backscattering, B M And B D And (5) carrying out wave band synthesis and constructing multi-wave band data.
Step 3, analyzing the change characteristics of the backscattering coefficient of different ground objects on the constructed multiband image; the change rule of the backscattering coefficient of the typical objects such as agricultural land, bare land, construction land, water body, wetland and the like on the multiband image constructed in the step 2 is included. The specific implementation process is as follows:
the multi-band data comprises HH, VV and B M And B D The backscattering coefficients of different polarizations of different types of ground objects can also be different. The concrete expression is as follows:
(1) The color tone of the agricultural land is uniform on an HH polarized image, the color tone of the agricultural land is mottled on a VV polarized image, the surface of the agricultural land is uneven, and the color tone of the agricultural land is B D The image is shown as bright tone, which indicates that the scattering effect of the body is strong. HH polarization reflects most of the leaf information of agricultural crops, the hue is uniform, and the backscattering coefficient distribution is concentrated; the VV polarization penetration capacity is strong, which reflects crop stem and soil roughness and humidity information, and the backscattering coefficient changes greatly; b D The image reflects the bulk scattering intensity of the target, B for agricultural land D The value of (a) is relatively high.
(2) The bare land is darker in color tone on HH and VV polarization images, lower in backscattering coefficient, slightly higher than that of a water body and lower than that of an agricultural land.
(3) The construction land is bright white on HH polarization images and VV polarization images, the backscattering coefficient is large, and B is large due to multiple scattering of the construction land D Relatively high value, compared to the farm land B D Compared to slightly higher.
(4) The water body generally shows mirror reflection on a radar image, the tone of the water body on an HH polarization image is uniform and is obvious with the boundary of surrounding ground objects, the backscattering coefficient is close to 0, the tone of the water body on the VV polarization image is inconsistent, the information is rich and is not obvious with the boundary of the surrounding ground objects, and the backscattering coefficient is larger than the HH polarization image, so that part of underwater or underwater information is reflected.
(5) The wetland has water body information and vegetation information, has uniform tone on an HH polarization image and obvious boundary with surrounding ground objects, has different tone on a VV polarization image, and has a lower HH band backscattering coefficient and a higher water body than those of agricultural land. The backscattering coefficient of the wetland in the VV polarized image is lower than that of the construction land, but higher than that of other land types.
And 4, performing machine learning and classification on the ground objects based on the difference of the extracted backscattering coefficients of different ground objects in each wave band, and extracting wetland information based on the classification result. The specific implementation process is as follows:
(1) Establishing a decision model by adopting a tree structure according to the quantitative relation between the surface feature type and the backscattering coefficient in the step 3;
(2) Carrying out decision analysis on the whole image by adopting a Classification and regression tree algorithm (Classification and regression tree algorithm) to obtain a Classification result graph;
(3) Classifying and post-processing the classification result based on the sample plot actual measurement data, eliminating fragments, combining corresponding classes and the like;
(4) And extracting wetland information according to the adjusted classification result graph, counting the wetland area, and making a wetland space distribution graph.
The technical means disclosed by the scheme of the invention are not limited to the technical means disclosed by the technical means, and the technical means also comprises the technical scheme formed by any combination of the technical features.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (6)
1. A wetland information extraction method based on ENVISAT ASAR dual-polarized data is characterized by comprising the following operation steps:
step 1, preprocessing ASAR dual-polarized data, including radiometric calibration, filtering, image registration and orthorectification;
step 2, carrying out polarization information mining based on the ASAR dual-polarized data, and constructing an ASAR multi-waveband data set;
step 3, analyzing the change characteristics of the backscattering coefficient of different ground objects on the constructed multiband image;
and 4, performing machine learning and classification on the ground objects based on the difference of the extracted backscattering coefficients of different ground objects in each wave band, and extracting wetland information based on the classification result.
2. The wetland information extraction method based on ENVISAT ASAR dual-polarized data according to claim 1, characterized in that the radiometric calibration in the step 1 is realized by the following formula:
the radiometric calibration formula is:
in the formula (I), the compound is shown in the specification,is the backscattering coefficient, DN is the image brightness value, K is the absolute scaling constant, G (theta) mn ) 2 For antenna gain, R mn Is a pitch, R ref For reference pitch, α mn Is the angle of incidence.
3. The wetland information extraction method based on ENVISAT ASAR dual-polarized data according to claim 1, characterized in that the filtering in step 1 adopts a Lee adaptive filtering algorithm, that is, a vertical mask is adopted to determine the most homogeneous part based on a regional statistical sliding window, the algorithm can effectively remove noise and retain edge information, and the core formula is as follows:
with σ 2 n ＝1/L
wherein, the first and the second end of the pipe are connected with each other,is an estimate of X, N w Is a window of size N x N,the mean of the NxN window region for Y, k the adaptive filter coefficient, var (X) the regional statistical variance of X, var<Y>, is the statistical variance of the region of Y,<> is a regional average operation, σ 2 n The speckle noise variance is the square of the ratio of the standard deviation to the observed mean, and L is the view.
4. The wetland information extraction method based on ENVISAT ASAR dual-polarized data according to claim 1, characterized in that the specific implementation process of the step 2 is as follows:
firstly, ASAR dual-polarized data is subjected to band operation:
B M ＝B HH -B VV
wherein, B M Is the difference between the backscattering coefficients of HH polarization and VV polarization, B D Is the ratio of the backscattering coefficients of HH polarization and VV polarization, B HH Is HH polarization backscattering coefficient, B VV Is the VV polarization backscattering coefficient, B HH And B VV All are backscattering coefficients after radiometric calibration, filtering, image registration and orthorectification in step 1;
then, HH polarization backscatter coefficient, VV polarization backscatter coefficient, B M And B D And (5) carrying out wave band synthesis and constructing multi-wave band data.
5. The wetland information extraction method based on ENVISAT ASAR dual-polarized data according to claim 1, characterized in that the step 3 comprises: the change rule of the backscattering coefficient of typical land features of agricultural land, bare land, construction land, water body and wetland on the multiband image constructed in the step 2;
the multi-band data comprises HH, VV and B M And B D The backscattering coefficients of different polarizations of different types of ground objects can also be different.
6. The wetland information extraction method based on ENVISAT ASAR dual-polarized data according to claim 1, characterized in that the specific implementation process of the step 4 is as follows:
(1) Establishing a decision model by adopting a tree structure according to the quantitative relation between the ground object type and the backscattering coefficient in the step 3;
(2) Carrying out decision analysis on the whole image by adopting a classification and regression tree algorithm to obtain a classification result graph;
(3) Classifying and post-processing the classification result based on the measured data of the sample plot, eliminating fragments and combining corresponding classes;
(4) And extracting wetland information according to the adjusted classification result graph, counting the wetland area, and making a wetland space distribution graph.
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