CN113537150A - Hyperspectral image target anomaly detection method, system, terminal and storage medium - Google Patents

Hyperspectral image target anomaly detection method, system, terminal and storage medium Download PDF

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CN113537150A
CN113537150A CN202110921317.9A CN202110921317A CN113537150A CN 113537150 A CN113537150 A CN 113537150A CN 202110921317 A CN202110921317 A CN 202110921317A CN 113537150 A CN113537150 A CN 113537150A
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郭宇
纪炎涛
姜沛林
张雪涛
王飞
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Xian Jiaotong University
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Abstract

A hyperspectral image target anomaly detection method, a hyperspectral image target anomaly detection system, a terminal and a storage medium are disclosed, wherein the detection method comprises the following steps: giving each wave band of the hyperspectral image a proper weight through maximum cross-correlation entropy cooperation representation; and for the hyperspectral image with each wave band having a proper weight, obtaining a pixel abnormal score through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value. According to the hyperspectral image detection method, different weights are given to different wave band self-adaptations of the hyperspectral image, so that the influence of wave band noise and abnormal pixel point segments on the detection accuracy can be effectively avoided, and the robustness of the algorithm is improved. The objective function is constructed as a joint function, the wave band weight and the neighborhood pixel point weight are solved simultaneously, an iterative solution algorithm is used for efficient solution, and the problem of suboptimal solution possibly caused by independent learning of one of the wave band weight and the neighborhood pixel point weight is solved. The invention can effectively improve the accuracy.

Description

Hyperspectral image target anomaly detection method, system, terminal and storage medium
Technical Field
The invention belongs to the field of remote sensing image processing, and particularly relates to a hyperspectral image target anomaly detection method, a hyperspectral image target anomaly detection system, a hyperspectral image target anomaly detection terminal and a storage medium.
Background
The hyperspectral image has a plurality of spectral domains, can provide a large amount of spectral information and spatial information of the earth surface, and is widely applied to the fields of scene classification, mixing, clustering, change detection, target detection and the like due to the good detection performance of the hyperspectral image. One of applications of hyperspectral images, hyperspectral anomaly detection, is one of the hotspots of current research, and currently, hyperspectral anomaly detection is widely applied to military and civil fields and achieves good effects. Hyperspectral anomaly detection is an unsupervised binary problem, and does not require prior knowledge. In the hyperspectral anomaly detection, an object whose spectrum is significantly different from its surrounding spectrum is regarded as an anomaly, and an object other than the anomaly in the entire image is defined as a background.
Many existing methods can complete hyperspectral anomaly detection. The Reed-Xiaoli (RX) algorithm is based on the Mahalanobis distance between pixels and the background and assumes that multivariate Gaussian background distributions are used to identify anomalies, detailed in turn into GRX using the entire image to model the background and LRX using local dual-window modeling. Later, another scholarly proposed a non-linear version of rx (KRX) that was extended to a feature space associated with the original input space by a non-linear mapping function. Due to the problem that KRX has a target pollution background, and the improvement of an algorithm based on the problem is carried out by scholars, a robust nonlinear anomaly detection algorithm (RNAD) using robust regression analysis in a kernel space is provided. Collaborative representation based detectors (CRDs) were proposed later and widely used. CRD assumes that every pixel in the background can be approximated using its neighborhood pixels, while outlier pixels cannot. It is assumed that the background pixel is a linear combination of neighboring pixels and is minimized by
Figure BDA0003207512290000011
Norm ofEnhancing the collaborative representation. Currently available RX-based detectors assume that the background is uniform and are modeled using a multivariate gaussian distribution. However, in practice, this assumption is often not satisfied, which results in a decrease in accuracy.
In addition, noise and abnormal interference cannot be completely shielded when the hyperspectral image is acquired, so that noise and abnormal pixel points may exist in a certain waveband in the hyperspectral image. CRD and its derivatives assume that each band has the same importance, and such assumption will be poor in robustness to noise bands, thereby reducing the effect of anomaly detection.
The related documents are:
【1】Reed I S,Yu X.Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1990,38(10):1760-1770.
【2】Molero J M,Garzón E M,García I,et al.Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2013,6(2):801-814.
【3】Kwon H,Nasrabadi N M.Kernel RX-algorithm:Anonlinear anomaly detector for hyperspectral imagery[J].IEEE transactions on Geoscience and Remote Sensing,2005,43(2):388-397.
【4】Zhao R,Du B,Zhang L.Arobust nonlinear hyperspectral anomaly detection approach[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(4):1227-1234.
【5】Li W,Du Q.Collaborative representation for hyperspectral anomaly detection[J].IEEE Transactions on Geoscience and Remote Sensing,2014,53(3):1463-1474.
disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a hyperspectral image target abnormality detection method, a hyperspectral image target abnormality detection system, a hyperspectral image target abnormality detection terminal and a hyperspectral image target abnormality detection storage medium, which can effectively avoid the influence of noise and abnormal wave bands on hyperspectral abnormality detection.
In order to achieve the purpose, the invention has the following technical scheme:
a hyperspectral image target anomaly detection method comprises the following steps:
giving each wave band of the hyperspectral image a proper weight through maximum cross-correlation entropy cooperation representation;
and for the hyperspectral image with each wave band having a proper weight, obtaining a pixel abnormal score through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value.
As a preferred scheme of the target abnormality detection method of the hyperspectral image, the hyperspectral image is defined as
Figure BDA0003207512290000021
Representing a hyperspectral image with n pixels, the dimension being d dimension; local approximation using sliding dual windows, the dimensions of the outer and inner windows being w, respectivelyoutAnd winIs expressed, therefore, the pixels in the neighborhood are used
Figure BDA0003207512290000022
Figure BDA0003207512290000031
Wherein s ═ wout×wout-win×win。
As a preferred scheme of the target abnormality detection method for the hyperspectral image, the maximum cross-correlation entropy cooperation representation target function of the hyperspectral image is as follows:
Figure BDA0003207512290000032
wherein the content of the first and second substances,
Figure BDA0003207512290000033
indicates each one usedThe weight of the neighborhood pixels, lambda is a regularization parameter;
in the formula (I), the compound is shown in the specification,
Figure BDA0003207512290000034
the larger the representation of the difference in collaborative representation, the larger the difference
Figure BDA0003207512290000035
The smaller.
As an optimal scheme of the hyperspectral image target abnormality detection method, the maximum cross-correlation entropy cooperation representation target function is reconstructed through a semi-quadratic technology to obtain the following expression:
Figure BDA0003207512290000036
in the formula (I), the compound is shown in the specification,
Figure BDA0003207512290000037
is a diagonal matrix, each element on the diagonal
Figure BDA0003207512290000038
A weight representing the band;
wkis defined as follows:
Figure BDA0003207512290000039
as an optimal scheme of the hyperspectral image target abnormality detection method, a coefficient corresponding to a pixel point with larger difference with a central pixel is smaller, and a diagonal regularization matrix is utilized to solve
Figure BDA00032075122900000310
Wherein gamma isiFor the Tikhonov regularization matrix, the expression is as follows:
Figure BDA00032075122900000311
as an optimal scheme of the hyperspectral image target anomaly detection method, the proposed target function is optimized in an iterative optimization mode, and unsupervised hyperspectral image target anomaly detection is completed.
The invention also provides a hyperspectral image target anomaly detection system, which comprises:
the spectrum band weighting module is used for giving a proper weight to each band of the hyperspectral image through maximum cross-correlation entropy cooperation representation;
and the abnormal pixel screening module is used for obtaining a pixel abnormal score for the hyperspectral image with a proper weight for each wave band through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value.
The invention also provides terminal equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the hyperspectral image target abnormality detection method when executing the computer program.
The invention further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the hyperspectral image target abnormality detection method.
Compared with the prior art, the invention at least has the following beneficial effects:
in the hyperspectral image target anomaly detection process, different weights are self-adaptively given to different wave bands of a hyperspectral image, a hyperspectral image with a proper weight for each wave band is obtained, pixel anomaly scores are obtained through maximum cross-correlation entropy cooperation expression, an abnormal pixel is selected through setting a proper threshold value, wave band noise and the influence of abnormal pixel point segments on detection accuracy can be effectively avoided, and the robustness of an algorithm is improved. Compared with the current mainstream hyperspectral image target anomaly detection method, the method provided by the invention has obvious advantages, and the accuracy rate on the public data set greatly leads other methods.
Furthermore, the objective function is constructed as a joint function, the wave band weight and the neighborhood pixel point weight are solved simultaneously, an iterative solution algorithm is used for efficient solution, and the problem of suboptimal solution possibly caused by independent learning of one of the wave band weight and the neighborhood pixel point weight is solved.
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FIG. 1 is a graph comparing ROC curves on the AVIRIS-I dataset for the present invention with other methods;
FIG. 2 is a graph comparing ROC curves on ABU-airport-1 data set according to the present invention and other methods;
FIG. 3 AVIRIS-I dataset pseudo-color map;
FIG. 4 is an AVIRIS-I dataset tag map;
FIG. 5 is a graph of the detection results of the AVIRIS-I data set CRD;
FIG. 6 is a graph of the AVIRIS-I data set of the present invention;
FIG. 7 AVIRIS-II dataset pseudo-color map;
FIG. 8 AVIRIS-II dataset tag map;
FIG. 9 is a graph of the results of the detection of the AVIRIS-II data set CRD;
FIG. 10 is a graph of the AVIRIS-II data set of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Suppose a hyperspectral image is defined as
Figure BDA0003207512290000051
Representing a hyperspectral image with n pixels, the dimension being d dimension; local approximation using sliding dual windows, the dimensions of the outer and inner windows being w, respectivelyoutAnd winIs expressed, therefore, the pixels in the neighborhood are used
Figure BDA0003207512290000052
Wherein s ═ wout×wout-win×win。
S1, giving each wave band of the hyperspectral image a proper weight through maximum cross-correlation entropy cooperation;
and S2, obtaining pixel abnormal scores by the cooperation of the maximum cross-correlation entropy of the hyperspectral images with each wave band and proper weight, and selecting abnormal pixels by setting proper threshold values.
The hyperspectral image target abnormality detection method provided by the invention constructs a detector based on a maximum entropy theory (MCC) provided in Information Theory Learning (ITL). ITL provides a unified approach to improve machine learning methods for outlier robustness. The MCC may obtain more information from the data to adjust and therefore may have a greater advantage in processing non-gaussian signals. Entropy introduces a new metric that equals to two points when they are close together
Figure BDA00032075122900000510
And (4) norm. As the two points get farther apart, the metric will approach
Figure BDA00032075122900000512
Norm, finally approaching
Figure BDA00032075122900000511
And (4) norm. Thus, MCC is strongly robust against outliers.
The maximum cross-correlation entropy cooperation of the hyperspectral image represents an objective function as follows:
Figure BDA0003207512290000053
wherein the content of the first and second substances,
Figure BDA0003207512290000054
representing the weight of each neighborhood pixel used, λ being the regularization parameter;
in the formula (I), the compound is shown in the specification,
Figure BDA0003207512290000055
the larger the representation of the difference in collaborative representation, the larger the difference
Figure BDA0003207512290000056
The smaller.
Reconstructing the maximum cross-correlation entropy cooperation expression target function through a semi-quadratic technology to obtain the following expression:
Figure BDA0003207512290000061
in the formula (I), the compound is shown in the specification,
Figure BDA0003207512290000062
is a diagonal matrix, each element on the diagonal
Figure BDA0003207512290000063
A weight representing the band;
wkis defined as follows:
Figure BDA0003207512290000064
corresponding to a small coefficient with a pixel point with a large difference with a central pixel, and solving by using a diagonal regularization matrix
Figure BDA0003207512290000065
Figure BDA0003207512290000066
Wherein gamma isiFor the Tikhonov regularization matrix, the expression is as follows:
Figure BDA0003207512290000067
and optimizing the proposed objective function by adopting an iterative optimization mode to complete unsupervised hyperspectral image target anomaly detection.
The hyperspectral image target anomaly detection method provided by the invention can effectively avoid the influence of noise and abnormal wave bands on hyperspectral anomaly detection. The noise band is given a sufficiently small weight by the method of the invention so as not to affect the inference result, while the contributing band is given more weight so that it can provide more effective information.
The invention also provides a hyperspectral image target anomaly detection system, which comprises:
the spectrum band weighting module is used for giving a proper weight to each band of the hyperspectral image through maximum cross-correlation entropy cooperation representation;
and the abnormal pixel screening module is used for obtaining a pixel abnormal score for the hyperspectral image with a proper weight for each wave band through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value.
The invention also provides terminal equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the steps of the hyperspectral image target abnormality detection method when executing the computer program.
The invention further provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the hyperspectral image target abnormality detection method.
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor, to complete the hyperspectral image target abnormality detection method of the invention.
The terminal can be a desktop computer, a notebook, a palm computer, a cloud server and other computing equipment, and can also be a processor and a memory. The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the hyperspectral image target abnormality detection system by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory.
Examples
A hyperspectral image target anomaly detection method comprises the following steps:
the method comprises the following steps: inputting a hyperspectral image
Figure BDA0003207512290000071
Regularization term parameters λ, σ and sliding window size woutAnd win。
Step two: solving the objective function, fixing Wi(giving initial value I) update of αiThe objective function will be converted into:
Figure BDA0003207512290000072
for the above formula, let it pair alphaiTaking the derivative of the collocated derivative to 0, one can obtain:
Figure BDA0003207512290000073
according to the formula can be used for alphaiAnd (6) updating.
Step three: fixed alphaiUpdating W by a formulai。
Step four: and repeating the second step and the third step until convergence.
Step five: and moving the sliding window to process the next pixel. And carrying out the operations of the second step, the third step and the fourth step.
Step six: by passing
Figure BDA0003207512290000081
And obtaining hyperspectral full-pixel abnormal scores, and setting a threshold value to obtain abnormal pixels.
Table 1 shows that the collaborative representation method based on the maximum entropy criterion compares the high spectrum abnormality detection on the public data set with the AUC value of the current mainstream method, wherein the larger the AUC value is, the better the AUC value is.
TABLE 1
Dataset GRX LRX CRD LSMAD SWCRD ERCRD Ours
AVIRIS-I 0.9111 0.8584 0.9911 0.9758 0.9936 0.9678 0.9985
AVIRIS-II 0.9403 0.8466 0.9696 0.9703 0.9798 0.9614 0.9855
AVIRIS-III 0.8771 0.8411 0.9828 0.9318 0.9837 0.9349 0.9904
ABU-airport -1 0.8221 0.8886 0.923 0.8806 0.9273 0.897 0.9631
ABU-airport-2 0.8404 0.9706 0.9735 0.9315 0.9692 0.9693 0.9879
Salinas 0.8872 0.9172 0.9606 0.9351 0.951 0.9447 0.9643
FIGS. 1 and 2 are graphs comparing ROC curves on AVIRIS-I and ABU-airport-1 data sets for the current mainstream method, with the curves in the graphs being closer to the upper left corner and more effective. Fig. 3, fig. 4, fig. 5 and fig. 6 are respectively a pseudo-color chart, a label chart, a detection effect chart of CRD and a detection effect chart of the present invention of the AVIRIS-I data set. Fig. 6, 7, 8 and 9 are respectively a pseudo-color map, a label map, a CRD detection effect map and a detection effect map of the present invention of the AVIRIS-II data set, in which the brighter the pixel, the higher the possibility that the pixel is an abnormal point. Compared with the current mainstream hyperspectral image target abnormality detection method, the method provided by the invention has obvious advantages, and the accuracy on public data sets (AVIRIS-I data sets and AVIRIS-II data sets) is greatly superior to that of other methods.
The above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the technical solution of the present invention, and it should be understood by those skilled in the art that the technical solution can be modified and replaced by a plurality of simple modifications and replacements without departing from the spirit and principle of the present invention, and the modifications and replacements also fall into the protection scope covered by the claims.

Claims (9)

1. A hyperspectral image target anomaly detection method is characterized by comprising the following steps:
giving each wave band of the hyperspectral image a proper weight through maximum cross-correlation entropy cooperation representation;
and for the hyperspectral image with each wave band having a proper weight, obtaining a pixel abnormal score through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value.
2. The hyperspectral image target abnormality detection method according to claim 1, characterized in that: the hyperspectral image is defined as
Figure FDA0003207512280000011
Representing a hyperspectral image with n pixels, the dimension being d dimension; local approximation using sliding dual windows, the dimensions of the outer and inner windows being w, respectivelyoutAnd winIs expressed, therefore, the pixels in the neighborhood are used
Figure FDA0003207512280000012
Wherein s ═ wout×wout-win×win。
3. The hyperspectral image target abnormality detection method according to claim 1, wherein the maximum cross-correlation entropy cooperation representation target function of the hyperspectral image is as follows:
Figure FDA0003207512280000013
wherein the content of the first and second substances,
Figure FDA0003207512280000014
representing the weight of each neighborhood pixel used, λ being the regularization parameter;
in the formula (I), the compound is shown in the specification,
Figure FDA0003207512280000015
the larger the representation of the difference in collaborative representation, the larger the difference
Figure FDA0003207512280000016
The smaller.
4. The hyperspectral image target abnormality detection method according to claim 3, characterized in that the maximum cross-correlation entropy cooperation representation target function is reconstructed by a semi-quadratic technique to obtain the following expression:
Figure FDA0003207512280000017
in the formula (I), the compound is shown in the specification,
Figure FDA0003207512280000018
is a diagonal matrix, each element on the diagonal
Figure FDA0003207512280000019
A weight representing the band;
wkis defined as follows:
Figure FDA00032075122800000110
5. the hyperspectral image target abnormality detection method according to claim 4, characterized in that the pixel points with larger difference from the center pixel correspond to smaller coefficients, and the diagonal regularization matrix is used for solving
Figure FDA0003207512280000021
Wherein riFor the Tikhonov regularization matrix, the expression is as follows:
Figure FDA0003207512280000022
6. the hyperspectral image target abnormality detection method according to claim 5, characterized in that: and optimizing the proposed objective function by adopting an iterative optimization mode to complete unsupervised hyperspectral image target anomaly detection.
7. A hyperspectral image target anomaly detection system is characterized by comprising:
the spectrum band weighting module is used for giving a proper weight to each band of the hyperspectral image through maximum cross-correlation entropy cooperation representation;
and the abnormal pixel screening module is used for obtaining a pixel abnormal score for the hyperspectral image with a proper weight for each wave band through maximum cross-correlation entropy cooperation representation, and selecting an abnormal pixel through setting a proper threshold value.
8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: the processor, when executing the computer program, implements the steps of the hyperspectral image target abnormality detection method according to any of claims 1 to 6.
9. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when being executed by a processor, realizes the steps of the hyperspectral image target abnormality detection method according to any of claims 1 to 6.
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