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Proceedings Paper

Sparse coding for hyperspectral images using random dictionary and soft thresholding
Author(s): Ender Oguslu; Khan Iftekharuddin; Jiang Li
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Paper Abstract

Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). Sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. Sparse coding consists of a dictionary learning step and an encoding step. In the learning step, we compared two different methods, L1-penalized sparse coding and random selection for the dictionary learning. In the encoding step, we utilized a soft threshold activation function to obtain feature representations for HSI. We applied the proposed algorithm to a HSI dataset collected at the Kennedy Space Center (KSC) and compared our results with those obtained by a recently proposed method, supervised locally linear embedding weighted k-nearest-neighbor (SLLE-WkNN) classifier. We have achieved better performances on this dataset in terms of the overall accuracy with a random dictionary. We conclude that this simple feature extraction framework might lead to more efficient HSI classification systems.

Paper Details

Date Published: 7 May 2012
PDF: 9 pages
Proc. SPIE 8399, Visual Information Processing XXI, 83990A (7 May 2012); doi: 10.1117/12.919162
Show Author Affiliations
Ender Oguslu, Old Dominion Univ. (United States)
Khan Iftekharuddin, Old Dominion Univ. (United States)
Jiang Li, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 8399:
Visual Information Processing XXI
Mark Allen Neifeld; Amit Ashok, Editor(s)

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