Document Type

Conference Paper

Publication Date

2012

DOI

10.1117/12.919162

Publication Title

Visual Information Processing XXI, Proceedings of SPIE 8399

Volume

8399

Pages

83990A (1-9)

Conference Name

SPIE Defense, Security, and Sensing, April 23-27, 2012, Baltimore Maryland

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.

Rights

Copyright 2012 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., Iftekharuddin, K., & Li, J. (2012) Sparse coding for hyperspectral images using random dictionary and soft thresholding. In M.A. Neifeld and A. Ashok (Eds.), Visual Information Processing XXI, Proceedings of SPIE 8399 (83990A). SPIE of Bellingham, WA. https://doi.org/10.1117/12.919162

ORCID

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

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