NDLI: Hyperspectral Image Classification Using Dictionary-Based Sparse Representation

Content Provider	IEEE Xplore Digital Library
Author	Yi Chen Nasrabadi, N.M. Tran, T.D.
Copyright Year	1980
Abstract	A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse representation of an unknown pixel is expressed as a sparse vector whose nonzero entries correspond to the weights of the selected training samples. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Two different approaches are proposed to incorporate the contextual information into the sparse recovery optimization problem in order to improve the classification performance. In the first approach, an explicit smoothing constraint is imposed on the problem formulation by forcing the vector Laplacian of the reconstructed image to become zero. In this approach, the reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. Experimental results show that our algorithm outperforms the classical supervised classifier support vector machines in most cases.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3973
Ending Page	3985
Page Count	13
File Size	1792831
File Format	PDF
ISSN	01962892
Volume Number	49
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Training Dictionaries Hyperspectral imaging Matching pursuit algorithms Image reconstruction Approximation algorithms spatial correlation Classification hyperspectral imagery joint sparsity model simultaneous sparse recovery sparse representation
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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