Hyperspectral Image Classification via Kernel Sparse Representation

• Published in: IEEE transactions on geoscience and remote sensing Vol. 51; no. 1; pp. 217 - 231
• Main Authors: Yi Chen, Nasrabadi, N. M., Tran, T. D.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2013; Institute of Electrical and Electronics Engineers
• Subjects: Applied geophysics; Classification; Correlation; Dictionaries; Earth sciences; Earth, ocean, space; Exact sciences and technology; hyperspectral imagery; Indexes; Internal geophysics; joint sparsity model; Kernel; kernel methods; sparse representation; Support vector machines; Training; Vectors; algorithms; models; hyperspectral imagery; accuracy; remote sensing; recovery; classification; optimization; regression; imagery; joint sparsity model; kernel methods; Pixel; sparse representation
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2012.2201730

In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is proposed. Our approach relies on sparsely representing a test sample in terms of all of the training samples in a feature space induced by a kernel function. For each test pixel in the feature space, a sparse representation vector is obtained by decomposing the test pixel over a training dictionary, also in the same feature space, by using a kernel-based greedy pursuit algorithm. The recovered sparse representation vector is then used directly to determine the class label of the test pixel. Projecting the samples into a high-dimensional feature space and kernelizing the sparse representation improve the data separability between different classes, providing a higher classification accuracy compared to the more conventional linear sparsity-based classification algorithms. Moreover, the spatial coherency across neighboring pixels is also incorporated through a kernelized joint sparsity model, where all of the pixels within a small neighborhood are jointly represented in the feature space by selecting a few common training samples. Kernel greedy optimization algorithms are suggested in this paper to solve the kernel versions of the single-pixel and multi-pixel joint sparsity-based recovery problems. Experimental results on several HSIs show that the proposed technique outperforms the linear sparsity-based classification technique, as well as the classical support vector machines and sparse kernel logistic regression classifiers.