Paper
7 May 2012 Sparse coding for hyperspectral images using random dictionary and soft thresholding
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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.
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Ender Oguslu, Khan Iftekharuddin, and Jiang Li "Sparse coding for hyperspectral images using random dictionary and soft thresholding", Proc. SPIE 8399, Visual Information Processing XXI, 83990A (7 May 2012); https://doi.org/10.1117/12.919162
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Associative arrays

Computer programming

Feature extraction

Data modeling

Hyperspectral imaging

Image classification

Image compression

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