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10 January 2014 Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition
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Proceedings Volume 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013); 90690R (2014) https://doi.org/10.1117/12.2050229
Event: Fifth International Conference on Graphic and Image Processing, 2013, Hong Kong, China
Abstract
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.
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Xiaoguang Cui, Yuan Tian, Lubin Weng, and Yiping Yang "Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition", Proc. SPIE 9069, Fifth International Conference on Graphic and Image Processing (ICGIP 2013), 90690R (10 January 2014); https://doi.org/10.1117/12.2050229
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