Discriminative and Compact Dictionary Design for Hyperspectral Image Classification using Learning VQ Framework
RICE UNIV HOUSTON TX
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Sparse representation provides an efficient description for high-dimensional Hyperspectral Imagery HSI and also encodes discriminative information useful for classification. However, due to the large size of typical HSI images, the naive way to construct a dictionary with all training pixels is neither efficient nor practical. In this paper, a novel approach is proposed to design compact dictionary for Sparse Representation-based Classification SRC. Inspired by Learning Vector Quantization LVQ techniques, we use a hinge loss function directly related to classification task as our objective function, and optimize the dictionary by exploiting the differentiable parts of sparse codes. The resultant dictionary updating procedure adapts the push and pull actions in LVQ to SRC, which is therefore named as Learning Sparse Representation-based Classification LSRC. Experiments on different HSI images demonstrate that our LSRC approach can achieve higher classification accuracy with substantially smaller dictionary size than using the whole training set, and also outperforms existing dictionary learning methods.
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