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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
会议名称Fifth International Conference on Graphic and Image Processing, ICGIP
会议录名称Fifth International Conference on Graphic and Image Processing, ICGIP 2013
会议日期2013
会议地点Hong Kong
摘要This 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.
关键词Anomaly Detection Hyper-spectral Imageries Low-rank And Sparse Decompositions Superpixels
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12413
专题空天信息研究中心
通讯作者Cui, Xiaoguang
作者单位1.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
推荐引用方式
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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