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Journal of Electronic Imaging

Sparse coding based dense feature representation model for hyperspectral image classification
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Paper Abstract

We present a sparse coding based dense feature representation model (a preliminary version of the paper was presented at the SPIE Remote Sensing Conference, Dresden, Germany, 2013) for hyperspectral image (HSI) classification. The proposed method learns a new representation for each pixel in HSI through the following four steps: sub-band construction, dictionary learning, encoding, and feature selection. The new representation usually has a very high dimensionality requiring a large amount of computational resources. We applied the l1/lq regularized multiclass logistic regression technique to reduce the size of the new representation. We integrated the method with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) to discriminate different types of land cover. We evaluated the proposed algorithm on three well-known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit, and image fusion and recursive filtering. Experimental results show that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification.

Paper Details

Date Published: 30 November 2015
PDF: 12 pages
J. Electron. Imaging. 24(6) 063009 doi: 10.1117/1.JEI.24.6.063009
Published in: Journal of Electronic Imaging Volume 24, Issue 6
Show Author Affiliations
Ender Oguslu, Old Dominion Univ. (United States)
Turkish Air Force NCO Vocational Collage (Turkey)
Guoqing Zhou, Guilin Univ. of Technology (China)
Zezhong Zheng, Univ. of Electronic Science and Technology of China (China)
Khan Iftekharuddin, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)


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