Non-local Spectral-spatial Centralized Sparse Representation for hyperspectral image classification

摘要

This paper presents a unified Non-local Spectral-spatial Centralized Sparse Representation (NL-CSR) model for the hyper-spectral image classification. The proposed model integrates local sparsity and non-local mean centralized induced sparsity. To achieve rich spectral-spatial information, the centralized sparsity enforces the sparse coding vector towards its non-local structural self-similar mean which is obtained via Image Patch Distance (IPD). Experiments validated that our NL-CSR model achieves convincing improvement over conventional sparsity based methods.