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17 October 2013 Hyperspectral image classification using a spectral-spatial sparse coding model
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We present a sparse coding based spectral-spatial classification model for hyperspectral image (HSI) datasets. The proposed method consists of an efficient sparse coding method in which the l1/lq regularized multi-class logistic regression technique was utilized to achieve a compact representation of hyperspectral image pixels for land cover classification. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center and compared our algorithm to a recently proposed method, Gaussian process maximum likelihood (GP-ML) classifier. Experimental results show that the proposed method can achieve significantly better performances than the GP-ML classifier when training data is limited with a compact pixel representation, leading to more efficient HSI classification systems.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ender Oguslu, Guoqing Zhou, and Jiang Li "Hyperspectral image classification using a spectral-spatial sparse coding model", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920R (17 October 2013);

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