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...Show MoreMetadata
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 ℓ1-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.
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 7, Issue: 6, June 2014)