Sparse coding based dense feature representation model for hyperspectral image classification

Ender Oguslu; Guoqing Zhou; Zezhong Zheng; Khan Iftekharuddin; Jiang Li

doi:10.1117/1.JEI.24.6.063009

30 November 2015 Sparse coding based dense feature representation model for hyperspectral image classification

Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, Jiang Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063009 (November 2015). https://doi.org/10.1117/1.JEI.24.6.063009

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/l_q 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.

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Ender Oguslu, Guoqing Zhou, Zezhong Zheng, Khan Iftekharuddin, and Jiang Li "Sparse coding based dense feature representation model for hyperspectral image classification," Journal of Electronic Imaging 24(6), 063009 (30 November 2015). https://doi.org/10.1117/1.JEI.24.6.063009

Published: 30 November 2015

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