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Sparse coding based dense feature representation model for hyperspectral image classification

[+] Author Affiliations
Ender Oguslu

Old Dominion University, Batten College of Engineering and Technology, Department of Electrical and Computer Engineering, 231 Kaufman Hall, Norfolk, Virginia 23529, United States

Turkish Air Force NCO Vocational Collage, Department of Electronics, Ataturk Cd. Gaziemir, Izmir 35410, Turkey

Guoqing Zhou

Guilin University of Technology, Guangxi Key Laboratory for Spatial Information and Geomatics Engineering, Guilin, Guangxi 541004, China

Zezhong Zheng

University of Electronic Science and Technology of China, School of Resources and Environment, North Jianshe Road, Sichuan, Chengdu 610054, China

Khan Iftekharuddin, Jiang Li

Old Dominion University, Batten College of Engineering and Technology, Department of Electrical and Computer Engineering, 231 Kaufman Hall, Norfolk, Virginia 23529, United States

J. Electron. Imaging. 24(6), 063009 (Nov 30, 2015). doi:10.1117/1.JEI.24.6.063009
History: Received March 7, 2015; Accepted October 26, 2015
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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.

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Citation

Ender Oguslu ; Guoqing Zhou ; Zezhong Zheng ; Khan Iftekharuddin and Jiang Li
"Sparse coding based dense feature representation model for hyperspectral image classification", J. Electron. Imaging. 24(6), 063009 (Nov 30, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.6.063009


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