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Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition
Cui, Xiaoguang; Tian, Yuan; Weng, Lubin; Yang, Yiping
2013
Conference NameFifth International Conference on Graphic and Image Processing, ICGIP
Source PublicationFifth International Conference on Graphic and Image Processing, ICGIP 2013
Conference Date2013
Conference PlaceHong Kong
AbstractThis paper presents a novel low-rank and sparse decomposition (LSD) based model for anomaly detection in hyperspectral images. In our model, a local image region is represented as a low-rank matrix plus spares noises in the spectral space, where the background can be explained by the low-rank matrix, and the anomalies are indicated
by the sparse noises. The detection of anomalies in local image regions is formulated as a constrained LSD problem, which can be solved efficiently and robustly with a modified "Go Decomposition" (GoDec) method. To enhance the validity of this model, we adapts a "simple linear iterative clustering" (SLIC) superpixel algorithm to efficiently generate
homogeneous local image regions i.e. superpixels in hyperspectral imagery, thus ensures that the background in local image regions satisfies the condition of low-rank. Experimental results on real hyperspectral data demonstrate that, compared with several known local detectors including RX detector, kernel RX detector, and SVDD detector,
the proposed model can comfortably achieves better performance in satisfactory computation time.
KeywordAnomaly Detection Hyper-spectral Imageries Low-rank And Sparse Decompositions Superpixels
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12413
Collection空天信息研究中心
Corresponding AuthorCui, Xiaoguang
Affiliation1.Institute of Automation, Chinese Academy of Sciences Sciences
2.Institute of Automation, Chinese Academy of Sciences Sciences
3.Institute of Automation, Chinese Academy of Sciences Sciences
4.Institute of Automation, Chinese Academy of Sciences Sciences
Recommended Citation
GB/T 7714
Cui, Xiaoguang,Tian, Yuan,Weng, Lubin,et al. Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition[C],2013.
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