Document Type

Conference Paper

Publication Date




Publication Title

Image and Signal Processing for Remote Sensing XIX, Proceedings of SPIE 8892




88920R (1-6)

Conference Name

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.


Copyright 2013 Society of Photo‑Optical Instrumentation Engineers (SPIE).

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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.


0000-0003-0091-6986 (Li)