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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)
作者部门光学影像学习与分析中心
2018-05-01
发表期刊REMOTE SENSING
ISSN2072-4292
卷号10期号:5
产权排序1
摘要

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.

文章类型Article
关键词Anomaly Detection Hyperspectral Image Sparse Representation Multiple Dictionaries Feature Extraction Clustering
WOS标题词Science & Technology ; Technology
DOI10.3390/rs10050745
收录类别SCI ; EI
关键词[WOS]LOW-RANK REPRESENTATION ; TARGET DETECTION ; JOINT SPARSE ; BAND SELECTION ; IMAGERY ; CLASSIFICATION ; CONSTRAINT
语种英语
WOS研究方向Remote Sensing
项目资助者National 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类目Remote Sensing
WOS记录号WOS:000435198400087
引用统计
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/30323
专题光学影像学习与分析中心
通讯作者Wang, Qi (crabwq@gmail.com)
作者单位1.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
推荐引用方式
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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