Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition
文献类型：会议论文
| 作者 | Cui, Xiaoguang ; Tian, Yuan; Weng, Lubin; Yang, Yiping
|
| 出版日期 | 2013 |
| 会议日期 | 2013 |
| 会议地点 | Hong Kong |
| 关键词 | Anomaly Detection Hyper-spectral Imageries Low-rank And Sparse Decompositions Superpixels |
| 英文摘要 | 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. |
| 会议录 | Fifth International Conference on Graphic and Image Processing, ICGIP 2013
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12413]  |
| 专题 | 自动化研究所_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]. 见:. Hong Kong. 2013. |
入库方式： OAI收割
来源：自动化研究所
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