Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation | |
Ma, Dandan1,2![]() ![]() ![]() | |
刊名 | REMOTE SENSING
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2018-05-01 | |
卷号 | 10期号:5 |
关键词 | Anomaly Detection Hyperspectral Image Sparse Representation Multiple Dictionaries Feature Extraction Clustering |
ISSN号 | 2072-4292 |
DOI | 10.3390/rs10050745 |
产权排序 | 1 |
文献子类 | Article |
英文摘要 | 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. |
WOS关键词 | LOW-RANK REPRESENTATION ; TARGET DETECTION ; JOINT SPARSE ; BAND SELECTION ; IMAGERY ; CLASSIFICATION ; CONSTRAINT |
WOS研究方向 | Remote Sensing |
语种 | 英语 |
WOS记录号 | WOS:000435198400087 |
资助机构 | 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 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/30323] ![]() |
专题 | 西安光学精密机械研究所_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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