Document Type

Conference Paper

Publication Date

2013

DOI

10.1117/12.2030261

Publication Title

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

Volume

8892

Pages

88920R (1-6)

Conference Name

SPIE Remote Sensing, September 23-26, 2013, Dresden, Germany

Abstract

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.

Rights

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

One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

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

ORCID

0000-0003-0091-6986 (Li)

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