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2017 | OriginalPaper | Buchkapitel

Classification of Hyperspectral Imagery Based on Dictionary Learning and Extended Multi-attribute Profiles

verfasst von : Qishuo Gao, Samsung Lim, Xiuping Jia

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

In recent years, sparse representation has shown its competitiveness in the field of image processing, and attribute profiles have also demonstrated their reliable performance in utilizing spatial information in hyperspectral image classification. In order to fully integrate spatial information, we propose a novel framework which integrates the above-mentioned methods for hyperspectral image classification. Specifically, sparse representation is used to learn a posteriori probability with extended attribute profiles as input features. A classification error term is added to the sparse representation-based classifier model and is solved by the k-singular value decomposition algorithm. The spatial correlation of neighboring pixels is incorporated by a maximum a posteriori scheme to obtain the final classification results. Experimental results on two benchmark hyperspectral images suggest that the proposed approach outperforms the related sparsity-based methods and support vector machine-based classifiers.

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Metadaten
Titel
Classification of Hyperspectral Imagery Based on Dictionary Learning and Extended Multi-attribute Profiles
verfasst von
Qishuo Gao
Samsung Lim
Xiuping Jia
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-71598-8_32