Spatial information-enhanced hyperspectral imagery classification based on joint spatial-aware collaborative representation

To address the insufficiency of texture information-based classification features to classify samples, we proposed two methods for spatial information-enhanced hyperspectral imagery classification based on joint spatial-aware collaborative representation (JSaCR). First, we introduce a texture regularized-based joint spatial-aware collaborative representation (TRJSaCR) method, in which prior texture is regarded as a regularization term to constrain the coefficient of the objection function of JSaCR and the closed-form solution is obtained to reconstruct the test sample. Second is a spatial information-assisted discrimination rules (SIDR) method coupled with TRJSaCR (TRJSaCR-SIDR) for classification. More precisely, the label information of the test samples and their corresponding neighborhoods are specified by TRJSaCR-SIDR, and the final labels are determined by considering their neighborhood label distribution. Our work aims to broaden the knowledge of the utilization of spatial information in hyperspectral classification. Experimental results on two benchmark hyperspectral datasets, Indian Pines and Pavia University, indicate that the proposed algorithms are superior to other state-of-the-art classifiers.