阅读说明：本技术 一种改进型的高光谱遥感影像波段选择方法 (Improved hyperspectral remote sensing image band selection method ) 是由 李臣明 朱磊 高红民 花再军 曹雪莹 杨耀 王家伟 于 2019-09-25 设计创作，主要内容包括：本发明公开了一种改进型的高光谱遥感影像波段选择方法,包括如下步骤：基于SMSE(光谱最小信息熵)算法计算各波段的信息熵,选出信息熵最大的一组波段组合作为第一个初始波段；基于K-L散度定义具有对称性的相似度矩阵,并与K-AP算法结合,对第二个初始波段进行选择；通过LP算法进行后续波段选择。本发明从高光谱图像的维度出发,在考虑波段信息量的同时充分考虑各个不同波段之间的相关性,利用SMSE、K-L散度、K-AP算法、LP算法等多个技术手段,使得最后选择出的波段集合可以有效地平衡波段信息量和波段之间相关性的关系,从而很好地降低了原始高光谱数据的维度,减少了冗余信息。(The invention discloses an improved hyperspectral remote sensing image band selection method, which comprises the following steps: calculating the information entropy of each wave band based on an SMSE (minimum information entropy spectrum) algorithm, and selecting a group of wave band combinations with the maximum information entropy as a first initial wave band; defining a similarity matrix with symmetry based on the K-L divergence, and combining with a K-AP algorithm to select a second initial waveband; and carrying out subsequent band selection through an LP algorithm. According to the method, based on the dimensionality of the hyperspectral image, the correlation among different wave bands is fully considered while the wave band information quantity is considered, and the relation between the wave band information quantity and the correlation among the wave bands can be effectively balanced by the finally selected wave band set by utilizing a plurality of technical means such as SMSE (simple mode noise enhancement), K-L divergence, a K-AP (adaptive Path) algorithm, an LP (Linear predictive Path) algorithm and the like, so that the dimensionality of original hyperspectral data is well reduced, and redundant information is reduced.)

1. An improved hyperspectral remote sensing image band selection method is characterized by comprising the following steps: the method comprises the following steps:

s1: calculating the information entropy of each wave band based on an SMSE algorithm, and selecting a group of wave band combinations with the maximum information entropy as a first initial wave band;

s2: defining a similarity matrix with symmetry based on the K-L divergence, and combining with a K-AP algorithm to select a second initial waveband;

s3: and carrying out subsequent band selection through an LP algorithm.

2. The improved hyperspectral remote sensing image band selection method according to claim 1, characterized by comprising the following steps: the specific process of selecting the first initial band based on the SMSE algorithm in step S1 is as follows:

s1-1: inputting an image, and utilizing a Gaussian distribution probability function to invert Gaussian distribution to obtain the probability of a hyperspectral image pixel spectrum;

s1-2: calculating the information entropy of a spectrum curve according to the probability of a hyperspectral image pixel spectrum;

s1-3: setting A as the minimum information entropy threshold of the end member spectrum, comparing the obtained information entropy of the spectrum curve with A, and if the information entropy is smaller than A, calculating and overlapping the probability of the end member spectrum curve;

s1-4: setting B as the threshold value of the spectrum of the end member of the same type, if the probability difference of the end member spectrum curve is larger than B, extracting the end member, otherwise, screening and extracting the end member by utilizing the probability and the information entropy of the end member spectrum curve.

3. The improved hyperspectral remote sensing image band selection method according to claim 2, characterized by comprising the following steps: the calculation formula of the SMSE algorithm in step S1 is as follows:

wherein the content of the first and second substances,

in the formula, x_iIs the gray value of (j, k) pixel in the ith wave band, mu_iSigma of_iThe mean value and variance of the gray value of the ith waveband are shown, P (j, k) is the probability of the spectral curve, and a is a constant.

4. The improved hyperspectral remote sensing image band selection method according to claim 1, characterized by comprising the following steps: the specific process of defining the similarity matrix with symmetry based on the K-L divergence in step S2 is as follows:

for two discrete probability distributions P, Q, the original K-L divergence from P to Q is defined as:

it can be seen from the formula (2) that the original K-L divergence itself is asymmetric, i.e. KL (P | | Q) ≠ KL (Q | | | P), so it is not suitable for being directly used in K-AP algorithm as a measurement method of inter-band similarity, so a new measurement method of inter-band similarity based on K-L divergence is defined, defining the inter-band similarity as:

the hyperspectral image is a three-dimensional cubic data set R^M*N*LWhere M and N represent the length and width of the spatial dimension and L represents the total number of spectral bands of the spectral dimension, the ith layer of spectral data I_i∈R^M*N*LThe pixel matrix is expressed as a two-dimensional image, namely, each layer of spectral band image can be regarded as an image pixel matrix; s_ijRepresenting the similarity between the ith and jth layers of bands, the similarity matrix is defined as:

wherein x represents the gray value, i (x) and j (x) represent the probability distribution of the gray value of the spectral image of the ith layer and the jth layer respectively.

5. The improved hyperspectral remote sensing image band selection method according to claim 1, characterized by comprising the following steps: the specific process of step S3 is as follows:

s3-1: the previously selected initial band pair B₁And B₂As a basis, the set of bands Φ ═ B is formed₁，B₂}；

S3-2: b in the band set phi is selected in the rest bands based on an LP algorithm₁，B₂Most dissimilar band B₃At this time, the band set is converted to phi ∩ { B ═ phi₃}；

S3-3: continuously and iteratively executing the step S3-2 until the number of the wave bands in the selected set phi reaches the set number;

s3-4: and selecting the subsequent wave bands.

6. The improved hyperspectral remote sensing image band selection method according to claim 5, wherein the method comprises the following steps: in the step S3-4, a linear prediction algorithm is used to select a subsequent band, which specifically includes:

suppose band B₁，B₂Is a band in the set phi, it is possible to select the band B that is the least similar to the two bands among the remaining bands by using B₁And B₂To estimate band B:

a₀+a₁B₁+a₂B₂＝B′(5)

wherein the content of the first and second substances,b' is a radical of a compound of formula B₁And B₂Prediction of band B, a₀，a₁And a₂It is the parameter that minimizes the linear prediction error, which is expressed as:

e_min=||pβ′-B′p|| (6)

parameter vector a ═ a₀,a₁,a₂)^TDetermined by the least squares solution:

a＝(X^TX)^-1X^TY (7)

where X is a matrix of Lx3, the values of the elements in the first column of the matrix are all 1, and the second column includes the band B_iAll pixels in the third column are in band B₂Y is a vector of Lx1 formed by all pixels in the band B;

band B that will get the maximum value after calculating the linear prediction error of all the remaining bands₃And merging the set phi, and repeating the process to continue to select the set phi until the number in the set phi meets the set target number.

Technical Field

The invention belongs to the technical field of hyperspectral remote sensing image processing, relates to a wave band selection method of a hyperspectral image, and particularly relates to an improved hyperspectral remote sensing image wave band selection method.

Background

A large amount of spectrum wave band data of hyperspectral remote sensing provide abundant information for people to know the ground features, and the method is very beneficial to subsequent ground feature classification and target identification. However, the hyperspectral image has a large number of wave bands, a large data volume and high information redundancy, which results in a large space required for data storage and a long processing time. Because the number of the wave bands of the hyperspectral image is large, dimension disaster phenomenon is easy to occur, namely, the classification precision is reduced, and therefore, dimension reduction processing for reducing the data volume and saving resources is very necessary.

Feature extraction and wave band selection are two main dimension reduction methods of the hyperspectral image. The method has the advantages that the dimension reduction is carried out by utilizing the feature extraction, the algorithm is complex, the calculated amount is large, the purpose of dimension reduction is realized through certain transformation, the physical significance of original data is changed, the data translation is not facilitated, in contrast, the wave band selection is a wave band subset which plays a main role in selecting from all wave bands of the hyperspectral image, the data dimension of the hyperspectral image can be greatly reduced, useful information can be completely reserved, and the method has special significance.

The traditional hyperspectral band selection method comprises the following steps: the extraction is directly carried out according to the height of OIF (optimal index factor) indexes, the method requires that the standard deviation between selected bands is as large as possible, and the correlation coefficient is as small as possible, but in reality, the two methods are difficult to be optimal. A segmented OIF index method has been proposed later, which can effectively remove the correlation, but needs to divide all bands into several band subsets in advance, and the whole process is relatively complicated. In addition, serpic et al propose a local extremum constrained discrete binary space search method, and an s-bands concept by averaging several adjacent bands according to a continuous band selection method with optimized classification accuracy. Although the method achieves higher classification accuracy, the method is a mixed algorithm of feature extraction and band selection, and the number of related bands is far larger than that of a common band selection method. Guo et al propose a rapid greedy optimization strategy for band selection, but are not ideal from the perspective of the final classification results.

Although a plurality of hyperspectral waveband selection methods exist at present, the number of original wavebands is generally hundreds, the conventional waveband selection method based on a search algorithm usually only keeps one group of optimal waveband combination, and other unselected wavebands are discarded, so that the dimensionality and redundant information of data are effectively reduced, but a large amount of identification information for classification is lost, and therefore, on the premise of keeping effective information, the reduction of the data dimensionality and redundant information of a hyperspectral image has research value and research significance.

Disclosure of Invention

The purpose of the invention is as follows: in order to overcome the defects in the prior art, an improved hyperspectral remote sensing image waveband selection method is provided, the selected waveband set can effectively balance the relation between the waveband information quantity and the correlation between wavebands, and a more efficient technical method is provided for reducing the data dimensionality and redundant information of a hyperspectral image.

The technical scheme is as follows: in order to achieve the above object, the present invention provides an improved method for selecting a waveband of a hyperspectral remote sensing image, comprising the following steps:

s1: calculating the information entropy of each wave band based on an SMSE (minimum information entropy spectrum) algorithm, and selecting a group of wave band combinations with the maximum information entropy as a first initial wave band;

s2: defining a similarity matrix with symmetry based on the K-L divergence, and combining with a K-AP algorithm to select a second initial waveband;

s3: and carrying out subsequent band selection through an LP algorithm.

Further, the specific process of selecting the first initial band based on the SMSE algorithm in step S1 is as follows:

s1-1: inputting an image, and utilizing a Gaussian distribution probability function to invert Gaussian distribution to obtain the probability of a hyperspectral image pixel spectrum;

s1-2: calculating the information entropy of a spectrum curve according to the probability of a hyperspectral image pixel spectrum;

s1-3: setting A as the minimum information entropy threshold of the end member spectrum, comparing the obtained information entropy of the spectrum curve with A, and if the information entropy is smaller than A, calculating and overlapping the probability of the end member spectrum curve;

s1-4: setting B as the threshold value of the spectrum of the end member of the same type, if the probability difference of the end member spectrum curve is larger than B, extracting the end member, otherwise, screening and extracting the end member by utilizing the probability and the information entropy of the end member spectrum curve.

Further, the calculation formula of the SMSE algorithm in step S1 is as follows:

wherein the content of the first and second substances,

in the formula, x_iIs the gray value of (j, k) pixel in the ith wave band, mu_iSigma of_iThe mean value and variance of the gray value of the ith waveband are shown, P (j, k) is the probability of the spectral curve, and a is a constant.

Further, the specific process of defining the similarity matrix with symmetry based on the K-L divergence in step S2 is as follows:

for two discrete probability distributions P, Q, the original K-L divergence from P to Q is defined as:

it can be seen from the formula (2) that the original K-L divergence itself is asymmetric, i.e. KL (P | | Q) ≠ KL (Q | | | P), so it is not suitable for being directly used in K-AP algorithm as a measurement method of inter-band similarity, so a new measurement method of inter-band similarity based on K-L divergence is defined, defining the inter-band similarity as:

the hyperspectral image is a three-dimensional cubic data set R^M*N*LWhere M and N represent the length and width of the spatial dimension and L represents the total number of spectral bands of the spectral dimension, the ith layer of spectral data I_i∈R^M*N*LThe pixel matrix is expressed as a two-dimensional image, namely, each layer of spectral band image can be regarded as an image pixel matrix; s_ijRepresenting the similarity between the ith and jth layers of bands, the similarity matrix is defined as:

wherein x represents the gray value, i (x) and j (x) represent the probability distribution of the gray value of the spectral image of the ith layer and the jth layer respectively.

Further, the specific process of step S3 is as follows:

s3-1: the previously selected initial band pair B₁And B₂As a basis, the set of bands Φ ═ B is formed₁，B₂}；

S3-2: b in the band set phi is selected in the rest bands based on an LP algorithm₁，B₂Most dissimilar band B₃At this time, the band set is converted to phi ∩ { B ═ phi₃}；

S3-3: continuously and iteratively executing the step S3-2 until the number of the wave bands in the selected set phi reaches the set number;

s3-4: and selecting the subsequent wave bands.

Further, in the step S3-4, a linear prediction algorithm is used to select a subsequent band, which specifically includes:

suppose band B₁，B₂Is a band in the set phi, it is possible to select the band B that is the least similar to the two bands among the remaining bands by using B₁And B₂To estimate band B:

a₀+a₁B₁+a₂B₂＝B′ (5)

wherein B' is B₁And B₂Prediction of band B, a₀，a₁And a₂It is the parameter that minimizes the linear prediction error, which is expressed as:

e_min＝||pB′-B′p|| (6)

parameter vector a ═ a₀,a₁,a₂)^TDetermined by the least squares solution:

a＝(X^TX)^-1X^TY (7)

where X is a matrix of Lx3, the values of the elements in the first column of the matrix are all 1, and the second column includes the band B_iAll pixels in the third column are in band B₂Y is a vector of Lx1 formed by all pixels in the band B;

the greater the linear prediction error, the more dissimilar the bands, so the band B that will obtain the maximum value after calculating the linear prediction errors for all the remaining bands₃And merging the set phi, and repeating the process to continue to select the set phi until the number in the set phi meets the set target number.

The method starts from the dimensionality of a hyperspectral image, considers the correlation among different wave bands while considering the information quantity of the wave bands, firstly selects a first initial wave band with the largest information entropy based on an SMSE algorithm, then defines a similarity matrix with symmetry based on K-L divergence, combines the similarity matrix with a K-AP algorithm, selects a second initial wave band, and finally selects a subsequent wave band through LP.

Has the advantages that: compared with the prior art, the method provided by the invention starts from the dimensionality of the hyperspectral image, fully considers the correlation among different wave bands while considering the wave band information content, and utilizes a plurality of technical means such as SMSE, K-L divergence, K-AP algorithm, LP algorithm and the like to enable the finally selected wave band set to effectively balance the relation between the wave band information content and the correlation among the wave bands, thereby well reducing the dimensionality of original hyperspectral data and reducing redundant information.

Drawings

FIG. 1 is a schematic flow diagram of the process of the present invention;

FIG. 2 is a schematic flow chart of the SMSE algorithm;

FIG. 3 is a schematic view of the K-L divergence calculation process.

Detailed Description

The invention is further elucidated with reference to the drawings and the embodiments.

As shown in fig. 1 and 2, the present invention provides an improved method for selecting a hyperspectral remote sensing image band, comprising the following steps:

s1: calculating the information entropy of each wave band based on an SMSE (minimum information entropy spectrum) algorithm, and selecting a group of wave band combinations with the maximum information entropy as a first initial wave band, wherein the specific process is as follows:

s1-1: inputting an image, and utilizing a Gaussian distribution probability function to invert Gaussian distribution to obtain the probability of a hyperspectral image pixel spectrum;

s1-2: calculating the information entropy of a spectrum curve according to the probability of a hyperspectral image pixel spectrum;

s1-3: setting A as the minimum information entropy threshold of the end member spectrum, comparing the obtained information entropy of the spectrum curve with A, and if the information entropy is smaller than A, calculating and overlapping the probability of the end member spectrum curve;

s1-4: setting B as the threshold value of the spectrum of the end member of the same type, if the probability difference of the end member spectrum curve is larger than B, extracting the end member, otherwise, screening and extracting the end member by utilizing the probability and the information entropy of the end member spectrum curve.

The calculation formula of the SMSE algorithm in this step is as follows:

wherein the content of the first and second substances,

in the formula, x_iIs the gray value of (j, k) pixel in the ith wave band, mu_iSigma of_iThe mean value and variance of the gray value of the ith waveband are shown, P (j, k) is the probability of the spectral curve, and a is a constant.

S2: defining a similarity matrix with symmetry based on K-L divergence, and the specific process is as follows: for two discrete probability distributions P, Q, the original K-L divergence from P to Q is defined as:

it can be seen from the formula (2) that the original K-L divergence itself is asymmetric, i.e. KL (P | | Q) ≠ KL (Q | | | P), so it is not suitable for being directly used in K-AP algorithm as a measurement method of inter-band similarity, so a new measurement method of inter-band similarity based on K-L divergence is defined, defining the inter-band similarity as:

the hyperspectral image is a three-dimensional cubic data set R^M*N*LWhere M and N represent the length and width of the spatial dimension and L represents the total number of spectral bands of the spectral dimension, the ith layer of spectral data I_i∈R^M*N*LThe pixel matrix is expressed as a two-dimensional image, namely, each layer of spectral band image can be regarded as an image pixel matrix; s_ijRepresenting the similarity between the ith and jth layers of bands, the similarity matrix is defined as:

wherein x represents gray value, i (x) and j (x) represent probability distribution of gray value of the spectral image of the ith layer and the jth layer respectively;

the similarity matrix is combined with a K-AP algorithm to select a second initial waveband.

S3: and (3) selecting a subsequent wave band through an LP algorithm, wherein the specific process is as follows:

s3-1: the previously selected initial band pair B₁And B₂As a basis for the determination of the position of the object,set of constituent bands phi ═ B₁,B₂}；

S3-2: b in the band set phi is selected in the rest bands based on an LP algorithm₁，B₂Most dissimilar band B₃At this time, the band set is converted to phi ∩ { B ═ phi₃}；

S3-3: continuously and iteratively executing the step S3-2 until the number of the wave bands in the selected set phi reaches the set number;

s3-4: selecting a subsequent wave band by using a linear prediction algorithm, which specifically comprises the following steps:

suppose band B₁，B₂Is a band in the set phi, it is possible to select the band B that is the least similar to the two bands among the remaining bands by using B₁And B₂To estimate band B:

a₀+a₁B₁+a₂B₂＝B′ (5)

wherein B' is B₁And B₂Prediction of band B, a₀，a₁And a₂It is the parameter that minimizes the linear prediction error, which is expressed as:

e_min＝||pB′-B′p|| (6)

parameter vector a ═ a₀,a₁,a₂)^TDetermined by the least squares solution:

a＝(X^TX)^-1X^TY (7)

where X is a matrix of Lx3, the values of the elements in the first column of the matrix are all 1, and the second column includes the band B_iAll pixels in the third column are in band B₂Y is a vector of Lx1 formed by all pixels in the band B;

the greater the linear prediction error, the more dissimilar the bands, so the band B that will obtain the maximum value after calculating the linear prediction errors for all the remaining bands₃And merging the set phi, and repeating the process to continue to select the set phi until the number in the set phi meets the set target number.

As shown in fig. 3, the K-L divergence in the above step S2 is calculated as follows in the present embodiment:

1) calculating a probability distribution of the signal;

2) solving the K-L distance;

3) substituting the obtained K-L distance into a divergence definition formula:

4) the K-L divergence is obtained.