Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation

doi:10.3964/j.issn.1000-0593(2018)11-3507-09

	Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation
	Wang Jie-chao1,2,3 ; Sun Da-peng1,2,3 ; Zhang Chang-zing 1; Xie Feng 1; Wang Jian-yu1
	2018-11
发表期刊	光谱学与光谱分析 (IF:0.7[JCR-2023],0.6[5-Year])
ISSN	1000-0593
卷号	38 期号:11 页码:3507-3515
发表状态	已发表
DOI	10.3964/j.issn.1000-0593(2018)11-3507-09
摘要	With the widespread use of hyperspectral images, hyperspectral image technology has made considerable progress, of which hyperspectral image anomaly detection technology has received more and more attention. In order to solve the problem of poor practicability and poor detection effect of traditional hyperspectral image anomaly detection techniques, this paper presents a novel low rank representation detection algorithm. For hyperspectral images, most of the background pixels can be approximated by a small number of major background pixel combinations, and their representation coefficients will be located in a low-rank space. While the remaining anomalous pixels in the sparse part that can not be represented by the main background pixels can be extracted by the detection algorithm. In low-rank representations, the construction of the background pixel dictionary will affect the representation of the background pixels in the hyperspectral image. When extracting the background pixels directly from the existing hyperspectral image to construct the dictionary, this process will lead to the contamination of the background pixel dictionary by the abnormal pixels. So in this paper, the background pixel dictionary is constructed by using the observed data on the hyperspectral image to be detected and the potential unobserved data that can be synthesized by the principle of spectral composition, and the main features of the background pixels are extracted, helping to better separate the sparse anomalous pixel Information. Hyperspectral image data is characterized by high-dimensional geometry. In this paper, we introduce a Laplacian matrix to constrain the representation of locally similar pixels in the space to be detected, and get a closer representation of the true representation coefficients. The experimental results are validated respectively on the simulation data and the real data, showing that the proposed method reduces the false detection rate by effectively highlighting the abnormal pixels and improves the detection rate by suppressing the background pixels.
关键词	Hyperspectral image Laplasse Low rank representation Anomaly detection
收录类别	SCI ; SCIE ; 北大核心 ; EI
语种	中文
WOS研究方向	Spectroscopy
WOS类目	Spectroscopy
WOS记录号	WOS:000452247200030
出版者	OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS
EI入藏号	20190806521497
EI主题词	Anomaly detection ; Hyperspectral imaging ; Matrix algebra ; Signal detection ; Spectroscopy
EI分类号	Information Theory and Signal Processing:716.1 ; Algebra:921.1
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29180
专题	信息科学与技术学院_硕士生信息科学与技术学院_特聘教授组_王建宇组
通讯作者	Wang Jian-yu
作者单位	1.Chinese Acad Sci, Key Lab Space Act Optoelect Technol, Shanghai Inst Tech Phys, Shanghai 200083, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.ShanghaiTech Univ, Shanghai 200120, Peoples R China
第一作者单位	上海科技大学
推荐引用方式 GB/T 7714	Wang Jie-chao,Sun Da-peng,Zhang Chang-zing,et al. Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation[J]. 光谱学与光谱分析,2018,38(11):3507-3515.
APA	Wang Jie-chao,Sun Da-peng,Zhang Chang-zing,Xie Feng,&Wang Jian-yu.(2018).Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation.光谱学与光谱分析,38(11),3507-3515.
MLA	Wang Jie-chao,et al."Hyperspectral Image Anomaly Detection Based on Laplasse Constrained Low Rank Representation".光谱学与光谱分析 38.11(2018):3507-3515.