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Block diagonalization and low-rank representation-based hyperspectral camouflaged target detection method

A technology of camouflage target and low-rank representation, which is applied in the field of hyperspectral camouflage target detection, can solve the problem of low target detection efficiency, achieve accurate description and improve detection efficiency

Active Publication Date: 2018-03-13
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0006] In order to overcome the shortcomings of low target detection efficiency of existing hyperspectral camouflage target detection methods, the present invention provides a hyperspectral camouflage target detection method based on block diagonal and low-rank representation

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Embodiment Construction

[0020] The specific steps of the hyperspectral camouflage target detection method based on block diagonal and low-rank representation in the present invention are as follows:

[0021] Suppose the input hyperspectral image is a 3D data cube containing n b bands, each band is a picture of n row row and n col The column size of the image. For the convenience of calculation, each band is stretched into a row vector, and all row vectors form a two-dimensional matrix X, Among them, each column of X represents the spectrum corresponding to each pixel, and this direction is the spectral dimension; each row of X corresponds to all pixel values ​​of a band (ie n p =n row ×n col ), which is the spatial dimension.

[0022] 1. Use the k-means algorithm to cluster the image.

[0023] For the input hyperspectral image X, set the value of the number of clusters k (according to the different values ​​of different images, the value of k ranges from 30 to 50), and then perform the follow...

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Abstract

The invention discloses a block diagonalization and low-rank representation-based hyperspectral camouflaged target detection method, which is used for solving the technical problem of low target detection efficiency of an existing hyperspectral camouflaged target detection method. According to the technical scheme, the method comprises the steps of firstly dividing a background into different types by utilizing a k-means clustering algorithm; secondly according to a clustering result, sorting original data; thirdly obtaining a dictionary of each type by utilizing a PCA dictionary learning algorithm, and then obtaining a global background dictionary; fourthly according to a low-rank and sparse representation theory, building a block diagonalization and low-rank detection model; fifthly after model solving, dividing the original data into a background part and a sparse part containing a camouflaged target; and finally extracting the camouflaged target from the sparse part. Under a framework of the low-rank and sparse representation theory, the background is subjected to refined description by utilizing the clustering algorithm, so that the background description is more accurate andthe camouflaged target detection efficiency is improved.

Description

technical field [0001] The invention relates to a hyperspectral camouflage target detection method, in particular to a hyperspectral camouflage target detection method based on block diagonal and low-rank representation. Background technique [0002] With the widespread application of camouflage technology in modern warfare, camouflaged target detection and its technology development have become a research hotspot. Although modern camouflage means include anti-radar, anti-infrared, anti-visible light and other camouflage means, but because hyperspectral images can effectively reflect the reflectivity characteristics of different substances in the visible light, infrared and even wider spectral ranges, these camouflage means cannot be used globally. The band is the range for camouflage, so the use of hyperspectral images for camouflage target detection has received more and more attention, and it has strong practicability in practical applications. [0003] When using hypers...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/66
CPCG06V30/194G06F18/28G06F18/2135G06F18/23213
Inventor 张秀伟李飞张艳宁张磊陈妍佳蒋冬梅
Owner NORTHWESTERN POLYTECHNICAL UNIV
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