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Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation
Ma, Dandan1,2; Yuan, Yuan1; Wang, Qi3,4,5; Wang, Qi (crabwq@gmail.com)
Department光学影像学习与分析中心
2018-05-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:5
Contribution Rank1
Abstract

Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance unsatisfactory. Therefore, an effective feature extraction technique is necessary. To this end, this paper proposes a novel anomaly detection method via discriminative feature learning with multiple-dictionary sparse representation. Firstly, a new spectral feature selection framework based on sparse presentation is designed, which is closely guided by the anomaly detection task. Then, the representative spectra which can significantly enlarge anomaly's deviation from background are picked out by minimizing residues between background spectrum reconstruction error and anomaly spectrum recovery error. Finally, through comprehensively considering the virtues of different groups of representative features selected from multiple dictionaries, a global multiple-view detection strategy is presented to improve the detection accuracy. The proposed method is compared with ten state-of-the-art methods including LRX, SRD, CRD, LSMAD, RSAD, BACON, BACON-target, GRX, GKRX, and PCA-GRX on three real-world hyperspectral images. Corresponding to each competitor, it has the average detection performance improvement of about respectively. Extensive experiments demonstrate its superior performance in effectiveness and efficiency.

SubtypeArticle
KeywordAnomaly Detection Hyperspectral Image Sparse Representation Multiple Dictionaries Feature Extraction Clustering
WOS HeadingsScience & Technology ; Technology
DOI10.3390/rs10050745
Indexed BySCI ; EI
WOS KeywordLOW-RANK REPRESENTATION ; TARGET DETECTION ; JOINT SPARSE ; BAND SELECTION ; IMAGERY ; CLASSIFICATION ; CONSTRAINT
Language英语
WOS Research AreaRemote Sensing
Funding OrganizationNational Key R&D Program of China(2017YFB1002202) ; State Key Program of National Natural Science of China(60632018) ; National Natural Science Foundation of China(61773316) ; Fundamental Research Funds for the Central Universities(3102017AX010) ; Open Research Fund of Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences
WOS SubjectRemote Sensing
WOS IDWOS:000435198400087
EI Accession Number20182205256446
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30323
Collection光学影像学习与分析中心
Corresponding AuthorWang, Qi (crabwq@gmail.com)
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
4.Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
5.Northwestern Polytech Univ, Unmanned Syst Res Inst USRI, Xian 710072, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Ma, Dandan,Yuan, Yuan,Wang, Qi,et al. Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation[J]. REMOTE SENSING,2018,10(5).
APA Ma, Dandan,Yuan, Yuan,Wang, Qi,&Wang, Qi .(2018).Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation.REMOTE SENSING,10(5).
MLA Ma, Dandan,et al."Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation".REMOTE SENSING 10.5(2018).
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