Discriminative and compact dictionary design for Hyperspectral Image classification using learning VQ framework
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.