Image and Signal Processing for Remote Sensing XIX, Proceedings of SPIE 8892
SPIE Remote Sensing, September 23-26, 2013, Dresden, Germany
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.
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Original Publication Citation
Oguslu, E., Zhou, G., & Li, J. (2013) Hyperspectral image classification using a spectral-spatial sparse coding model. In L. Bruzzone (Ed.), Image and Signal Processing for Remote Sensing XIX, Proceedings of SPIE Vol. 8892 (88920R). SPIE of Bellingham, WA. https://doi.org/10.1117/12.2030261
Oguslu, Ender; Zhou, Guoqing; Li, Jiang; and Bruzzone, Lorenzo (Ed.), "Hyperspectral Image Classification Using a Spectral-Spatial Sparse Coding Model" (2013). Electrical & Computer Engineering Faculty Publications. 391.