IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Spatial-spectral Classification of Hyperspectral Images using Discriminative Dictionary Designed by Learning Vector Quantization

In this paper, a novel discriminative dictionary learning method is proposed for Sparse Representation-based Classification (SRC) to label high-dimensional Hyperspectral Imagery (HSI). In SRC, a dictionary is conventionally constructed using all the training pixels, which is not only inefficient due to the large size of typical HSI images, but is also ineffective in capturing class-discriminative information crucial for classification. We address the dictionary design problem with the inspiration from the Learning Vector Quantization (LVQ) technique and propose a hinge loss function that is directly related to the classification task as the objective function for dictionary learning. The resulting online learning procedure systematically “pulls ” and “pushes ” dictionary atoms so that they become better adapted to distinguish between different classes. In addition, the spatial context for a test pixel within its local neighborhood is modeled using a Bayesian graph model and incorporated with the sparse representation of a single test pixel in a unified probabilistic framework, which enables further refinement of our dictionary to capture the spatial class dependency that complements the spectral information. Experiments on different HSI images demonstrate that the dictionaries optimized using our method can achieve higher classification accuracy with substantially reduced dictionary size than using the whol