CN109191443A - Hyperspectral image band selection method based on sequence information Yu wave band quality - Google Patents
Hyperspectral image band selection method based on sequence information Yu wave band quality Download PDFInfo
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Abstract
A kind of hyperspectral image band selection method based on sequence information Yu wave band quality provided by the invention, comprising the following steps: the sequence sub-block with sequence relation is divided into high spectrum image space plane；Each layer of wave band of each sequence sub-block is expressed as a column vector, column vector is standardized；Construct spectral band quality evaluation search criteria；Each sequence sub-block is merged with search criteria, constructs sub-block metric matrix；Sub-block metric matrix is merged with determinant point process, selects that redundancy is low, the good spectral band subset of separability；The index for the spectral band subset that output selection obtains.A kind of hyperspectral image band selection method based on sequence information Yu wave band quality provided by the invention improves measurement accuracy from the redundancy of different spaces domain measurement spectral band；By constructing search criteria, the quality of effective evaluation spectral band improves the quality of selection spectral band.
Description
Technical Field
The invention relates to the technical field of hyperspectral images, in particular to a hyperspectral image waveband selection method based on sequence information and waveband quality.
Background
The hyperspectral image is data of a three-dimensional structure, and compared with the traditional image, the hyperspectral image can provide abundant spectral information through the spectral dimension, and is widely applied to agriculture, geology and atmospheric research. However, abundant spectral bands also bring a series of problems, firstly, data redundancy exists, excessive bands cause a huge data set, the computational complexity is high, and a common processor is difficult to effectively process. Secondly, in the excessive spectral bands, there are bands with poor quality, which also affects the classification accuracy.
The traditional spectral band selection method generally comprises two steps, firstly, designing a search criterion, namely designing a search criterion for selecting a spectral band meeting the criterion; the second is a search method for determining how to select spectral bands in the raw data set. Common methods include forward-based search methods. In each search, a band is added to the existing search subset, so that the bands of the subset meet the search criteria. Two disadvantages exist in this method, one is that the method is a traversal method, and has high computational complexity and long time. Secondly, this method lacks consideration of the whole hyperspectral image, but the measure of redundancy is not accurate from the consideration of each selected partial spectral band.
The common spectral band selection method also comprises a sorting-based method, wherein the correlation between each band and the whole data is calculated, sorting is carried out according to the magnitude of the correlation, and the spectral band in the front sorting is selected.
Disclosure of Invention
The invention provides a hyperspectral image band selection method based on sequence information and band quality, aiming at overcoming the technical defects that the redundancy of spectral bands is measured inaccurately and the quality of the spectral bands cannot be evaluated in the existing spectral band selection method.
In order to solve the technical problems, the technical scheme of the invention is as follows:
the hyperspectral image band selection method based on sequence information and band quality comprises the following steps:
s1: dividing a hyperspectral image space plane into sequence sub-blocks with sequence relation;
s2: representing each layer wave band of each sequence sub-block as a column vector, and carrying out standardization processing on the column vector;
s3: constructing a spectral band quality evaluation search criterion;
s4: fusing each sequence sub-block with a search criterion by using a Gaussian radial basis function to construct different sub-block measurement matrixes;
s5: fusing the obtained sub-block measurement matrix with a determinant point process, and selecting a spectrum band subset with low redundancy and good separability;
s6: and outputting the index of the selected spectral band subset.
In step S1, the raw hyperspectral data represents:
B＝{b1,b2,b3,…,bl}∈Rn×l,bi∈Rn×1；
wherein n is the total number of pixel points of each layer of spectral band, and l is the total number of spectral bands; bi(1 ≦ i ≦ l) for the ith layer spectral band;
the mth sequence subblock is represented as:
wherein, each subblock contains r pixel points.
In step S2, each layer band of each sequence sub-block is expressed as a one-dimensional column vector, specifically:
the calculation formula for normalizing the column vectors is:
wherein, the step S3 specifically includes:
s31: estimating the information entropy of each layer of spectral band, wherein the calculation formula is as follows:
wherein p isiRepresenting the pixel point of the ith layer; p (P)i) Probability distribution estimation values representing pixel points, estimated using a histogram or estimated using a Parzen window, HiInformation entropy representing the ith layer;
s32: collecting sample pixel points and calculating mutual information of the category labels, wherein the collected sample pixel points are expressed as:
whereinPixel sample points representing acquisitions of the ith layer of optical tape, n representing the number of acquisitions, the corresponding labels being expressed as: c ═ C1,c2,c3,…,cn]T；
Therefore, the formula for calculating the mutual information is as follows:
wherein,representing a sample setα 1 th sample cα2α 2 th label representing labelset C;
s33: establishing a spectral band quality evaluation search criterion according to the information entropy and the mutual information, wherein the maximization formula is as follows:
max Qi＝Hi+Mi；
wherein Q isiRepresenting the quality of the spectral band, QiThe larger the value, the better the spectral band quality.
In step S4, the similarity matrix constructed by each sequence of sub-blocks is represented as:
wherein,indicating the correlation of the ith and jth layer wave bands calculated according to the mth and sequence information; the correlation relationship between every two of all the bands can be expressed as a matrix LmL is the total number of wave bands;
sigma is an adjusting parameter and is set to be between 0 and 1;
fusing similar matrixes constructed by different sub-blocks to construct a measurement matrix S with weight, wherein the calculation formula of the measurement matrix is as follows:
wherein, αmAs weight value, representing the importance of the sub-block, take
Wherein, the step S5 specifically includes:
s51: performing characteristic decomposition on the measurement matrix S, and selecting k characteristic values and corresponding characteristic vectors from the measurement matrix S;
s52: from the selected feature vectors, indices of k spectral bands are selected.
Wherein, the step S51 specifically includes:
s511: performing characteristic decomposition on the measurement matrix S to obtain characteristic values and corresponding characteristic vectors
S512: computing a characteristic polynomial
Wherein the characteristic polynomial is expressed as
S513: let h be k, n be l;
s514: let n be n-1, judge whether u is less thanIf yes, go to step S515; if not, repeatedly executing the step S514;
s515: storing the collected characteristic values and indexes of corresponding characteristic vectors into a set S, and assigning a parameter h-1 to a parameter h;
s516: judging whether the parameter h is 0, if so, outputting an index set S of the set characteristic vectors; if not, go to step S514.
Wherein, the step S52 specifically includes:
s521: calculating { V | Vn}n∈SInitializing a spectrum band index set Y;
s522: judging whether the | V | is greater than 0; if yes, go to step S53; if not; step S55 is executed;
s523: judging whether the following formula is satisfied:
pr (i) is the probability of the ith layer spectrum being selected; u is a random variable uniformly distributed from [0,1 ];
if yes, go to step S54; if not, the step S53 is executed repeatedly;
s524: the band index i is stored in the spectral band index set Y, and the orthogonal and standardized V is assigned to
S525: and outputting a spectral band index set Y.
Wherein the step S6 specifically includes: and (4) storing the indexes of the spectral band sets selected in the step (S5) to obtain the spectral band subsets with low redundancy and high quality.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
according to the hyperspectral image band selection method based on sequence information and band quality, provided by the invention, the spatial sequence information of a hyperspectral image is effectively utilized, the redundancy of spectral bands is measured from different spatial domains, and the measurement accuracy is improved; by constructing a search criterion, the quality of the spectral band is effectively evaluated, and the quality of the selected spectral band is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a flow chart of an algorithm for selecting k eigenvectors.
Fig. 3 is a flow chart of an algorithm for selecting k spectral bands.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the hyperspectral image band selection method based on sequence information and band quality includes the following steps:
s1: dividing a hyperspectral image space plane into sequence sub-blocks with sequence relation;
s2: representing each layer wave band of each sequence sub-block as a column vector, and carrying out standardization processing on the column vector;
s3: constructing a spectral band quality evaluation search criterion;
s4: fusing each sequence sub-block with a search criterion by using a Gaussian radial basis function to construct different sub-block measurement matrixes;
s5: fusing the obtained sub-block measurement matrix with a determinant point process, and selecting a spectrum band subset with low redundancy and good separability;
s6: and outputting the index of the selected spectral band subset.
More specifically, in step S1, the raw hyperspectral data represents:
B＝{b1,b2,b3,…,bl}∈Rn×l,bi∈Rn×1；
wherein n is the total number of pixel points of each layer of spectral band, and l is the total number of spectral bands; bi(1 ≦ i ≦ l) for the ith layer spectral band;
the mth sequence subblock is represented as:
wherein, each subblock contains r pixel points.
More specifically, in step S2, each layer band of each sequence sub-block is represented as a one-dimensional column vector, specifically:
the calculation formula for normalizing the column vectors is:
more specifically, the step S3 specifically includes:
s31: estimating the information entropy of each layer of spectral band, wherein the calculation formula is as follows:
wherein p isiRepresenting the pixel point of the ith layer; p (P)i) Probability distribution estimation values representing pixel points, estimated using a histogram or estimated using a Parzen window, HiInformation entropy representing the ith layer;
s32: collecting sample pixel points and calculating mutual information of the category labels, wherein the collected sample pixel points are expressed as:
whereinPixel sample points representing acquisitions of the ith layer of optical tape, n representing the number of acquisitions, the corresponding labels being expressed as: c ═ C1,c2,c3,…,cn]T；
Therefore, the formula for calculating the mutual information is as follows:
wherein,representing a sample setα 1 th sample cα2α 2 th label representing labelset C;
s33: establishing a spectral band quality evaluation search criterion according to the information entropy and the mutual information, wherein the maximization formula is as follows:
max Qi＝Hi+Mi；
wherein Q isiRepresenting the quality of the spectral band, QiThe larger the value, the better the spectral band quality.
More specifically, in step S4, the similarity matrix constructed by each sequence of sub-blocks is represented as:
wherein,indicating the correlation of the ith and jth layer wave bands calculated according to the mth and sequence information; the correlation relationship between every two of all the bands can be expressed as a matrix LmL is the total number of wave bands;
sigma is an adjusting parameter and is set to be between 0 and 1;
fusing similar matrixes constructed by different sub-blocks to construct a measurement matrix S with weight, wherein the calculation formula of the measurement matrix is as follows:
wherein, αmAs weight value, representing the importance of the sub-block, take
More specifically, the step S5 specifically includes:
s51: performing characteristic decomposition on the measurement matrix S, and selecting k characteristic values and corresponding characteristic vectors from the measurement matrix S;
s52: from the selected feature vectors, indices of k spectral bands are selected.
More specifically, the step S51 specifically includes:
s511: performing characteristic decomposition on the measurement matrix S to obtain characteristic values and corresponding characteristic vectors
S512: computing a characteristic polynomial
Wherein the characteristic polynomial is expressed as
S513: let h be k, n be l;
s514: let n be n-1, judge whether u is less thanIf yes, go to step S515; if not, repeatedly executing the step S514;
s515: storing the collected characteristic values and indexes of corresponding characteristic vectors into a set S, and assigning a parameter h-1 to a parameter h;
s516: judging whether the parameter h is 0, if so, outputting an index set S of the set characteristic vectors; if not, go to step S514.
More specifically, the step S52 specifically includes:
s521: calculating { V | Vn}n∈SInitializing a spectrum band index set Y;
s522: judging whether the | V | is greater than 0; if yes, go to step S53; if not; step S55 is executed;
s523: judging whether the following formula is satisfied:
pr (i) is the probability of the ith layer spectrum being selected; u is a random variable uniformly distributed from [0,1 ];
if yes, go to step S54; if not, the step S53 is executed repeatedly;
s524: the band index i is stored in the spectral band index set Y, and the orthogonal and standardized V is assigned to
S525: and outputting a spectral band index set Y.
More specifically, the step S6 specifically includes: and (4) storing the indexes of the spectral band sets selected in the step (S5) to obtain the spectral band subsets with low redundancy and high quality.
In a specific implementation process, the hyperspectral image band selection method based on sequence information and band quality effectively utilizes spatial sequence information of a hyperspectral image to measure the redundancy of spectral bands from different spatial domains, and improves the measurement accuracy; by constructing a search criterion, the quality of the spectral band is effectively evaluated, and the quality of the selected spectral band is improved.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. The hyperspectral image band selection method based on sequence information and band quality is characterized by comprising the following steps of:
s1: dividing a hyperspectral image space plane into sequence sub-blocks with sequence relation;
s2: representing each layer wave band of each sequence sub-block as a column vector, and carrying out standardization processing on the column vector;
s3: constructing a spectral band quality evaluation search criterion;
s4: fusing each sequence sub-block with a search criterion by using a Gaussian radial basis function to construct different sub-block measurement matrixes;
s5: fusing the obtained sub-block measurement matrix with a determinant point process, and selecting a spectrum band subset with low redundancy and good separability;
s6: and outputting the index of the selected spectral band subset.
2. The hyperspectral image band selection method based on sequence information and band quality according to claim 1, wherein in step S1, the raw hyperspectral data represents:
B＝{b1,b2,b3,…,bl}∈Rn×l,bi∈Rn×1；
wherein n is the total number of pixel points of each layer of spectral band, and l is the total number of spectral bands; bi(1 ≦ i ≦ l) for the ith layer spectral band;
the mth sequence subblock is represented as:
wherein, each subblock contains r pixel points.
3. The hyperspectral image band selection method based on sequence information and band quality according to claim 2, wherein in the step S2, each layer band of each sequence sub-block is represented as a one-dimensional column vector, specifically:
the calculation formula for normalizing the column vectors is:
4. the hyperspectral image band selection method based on sequence information and band quality according to claim 3, wherein the step S3 specifically comprises:
s31: estimating the information entropy of each layer of spectral band, wherein the calculation formula is as follows:
wherein p isiRepresenting the pixel point of the ith layer; p (P)i) Probability distribution estimation values representing pixel points, estimated using a histogram or estimated using a Parzen window, HiInformation entropy representing the ith layer;
s32: collecting sample pixel points and calculating mutual information of the category labels, wherein the collected sample pixel points are expressed as:
whereinPixel sample points representing acquisitions of the ith layer of optical tape, n representing the number of acquisitions, the corresponding labels being expressed as: c ═ C1,c2,c3,…,cn]T；
Therefore, the formula for calculating the mutual information is as follows:
wherein,representing a sample setα 1 th sample cα2α 2 th label representing labelset C;
s33: establishing a spectral band quality evaluation search criterion according to the information entropy and the mutual information, wherein the maximization formula is as follows:
max Qi＝Hi+Mi；
wherein Q isiRepresenting the quality of the spectral band, QiThe larger the value, the better the spectral band quality.
5. The hyperspectral image waveband selection method based on sequence information and waveband quality of claim 4, wherein in the step S4, the similarity matrix constructed by each sequence sub-block is represented as:
wherein,indicating the correlation of the ith and jth layer wave bands calculated according to the mth and sequence information; the correlation relationship between every two of all the bands can be expressed as a matrix LmL is the total number of wave bands;
sigma is an adjusting parameter and is set to be between 0 and 1;
fusing similar matrixes constructed by different sub-blocks to construct a measurement matrix S with weight, wherein the calculation formula of the measurement matrix is as follows:
wherein, αmAs weight value, representing the importance of the sub-block, take
6. The hyperspectral image band selection method based on sequence information and band quality according to claim 5, wherein the step S5 specifically comprises:
s51: performing characteristic decomposition on the measurement matrix S, and selecting k characteristic values and corresponding characteristic vectors from the measurement matrix S;
s52: from the selected feature vectors, indices of k spectral bands are selected.
7. The hyperspectral image band selection method based on sequence information and band quality according to claim 6, wherein the step S51 specifically comprises:
s511: performing characteristic decomposition on the measurement matrix S to obtain characteristic values and corresponding characteristic vectors
S512: computing a characteristic polynomial
Wherein the characteristic polynomial is expressed as
S513: let h be k, n be l;
s514: let n be n-1, judge whether u is less thanIf yes, go to step S515; if not, repeatedly executing the step S514;
s515: storing the collected characteristic values and indexes of corresponding characteristic vectors into a set S, and assigning a parameter h-1 to a parameter h;
s516: judging whether the parameter h is 0, if so, outputting an index set S of the set characteristic vectors; if not, go to step S514.
8. The hyperspectral image band selection method based on sequence information and band quality according to claim 7, wherein the step S52 specifically is:
s521: calculating { V | Vn}n∈SInitializing a spectrum band index set Y;
s522: judging whether the | V | is greater than 0; if yes, go to step S53; if not; step S55 is executed;
s523: judging whether the following formula is satisfied:
pr (i) is the probability of the ith layer spectrum being selected; u is a random variable uniformly distributed from [0,1 ];
if yes, go to step S54; if not, the step S53 is executed repeatedly;
s524: the band index i is stored in the spectral band index set Y, and the orthogonal and standardized V is assigned to
S525: and outputting a spectral band index set Y.
9. The hyperspectral image band selection method based on sequence information and band quality according to claim 8, wherein the step S6 is specifically as follows: and (4) storing the indexes of the spectral band sets selected in the step (S5) to obtain the spectral band subsets with low redundancy and high quality.
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| CN111031390A (en) * | 2019-12-17 | 2020-04-17 | 南京航空航天大学 | Dynamic programming-based method for summarizing video of determinant point process with fixed output size |
| CN111031390B (en) * | 2019-12-17 | 2022-10-21 | 南京航空航天大学 | Method for summarizing process video of outputting determinant point with fixed size |
| CN113505846A (en) * | 2021-07-26 | 2021-10-15 | 云南电网有限责任公司电力科学研究院 | Hyperspectral band selection method based on mutual information |
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