Document Type


Publication Date




Publication Title

Journal of Electronic Imaging






063009 (1-12)


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.


Copyright 2015 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., Guoqing, Z., Zezhong, Z., Iftekharuddin, K., & Jiang, L. (2015). Sparse coding based dense feature representation model for hyperspectral image classification. Journal of Electronic Imaging, 24(6), 1-12, Article 063009.


0000-0001-8316-4163 (Iftekharuddin), 0000-0003-0091-6986 (Li)