By Topic

Hyperspectral image classification via kernel sparse representation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yi Chen ; Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA ; Nasrabadi, N.M. ; Tran, T.D.

In this paper, a new technique for hyperspectral image classification is proposed. Our approach relies on the sparse representation of a test sample with respect to all training samples in a feature space induced by a kernel function. Projecting the samples into the feature space and kernelizing the sparse representation improves the separability of the data and thus yields higher classification accuracy compared to the more conventional linear sparsity-based classification algorithm. Moreover, the spatial coherence across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are sparsely represented in the feature space by selecting a few common training samples. Two greedy algorithms are also provided in this paper to solve the kernel versions of the pixel-wise and jointly sparse recovery problems. Experimental results show that the proposed technique outperforms the linear sparsity-based classification technique and the classical Support Vector Machine classifiers.

Published in:

Image Processing (ICIP), 2011 18th IEEE International Conference on

Date of Conference:

11-14 Sept. 2011