Hyperspectral Image Classification Based on Regularized Sparse Representation

Abstract

Sparsity-based models have been widely applied to hyperspectral image (HSI) classification. The class label of the test sample is determined by the minimum residual error based on the sparse vector, which is viewed as a pattern of original sample in the sparsity-based model. From the aspect of pattern classification, similar samples in the same class should have similar patterns. However, due to the independent sparse reconstruction process, the similarity among the sparse vectors of these similar samples is lost. To enforce such similarity information, a regularized sparse representation (RSR) model is proposed. First, a centralized quadratic constraint as the regularization term is incorporated into the objective function of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm sparse representation model. Second, RSR can be effectively solved by the feature-sign search algorithm. Experimental results demonstrate that RSR can achieve excellent classification performance.