Hyperspectral Image Classification Based on Adjacent Constraint Representation

Sparse representation (SR)-based models have shown to be a powerful category of frameworks for hyperspectral image classification (HSIC). However, current residual-driven methods mainly focus on the sparsity of the coefficient, which is generally used in conjunction with the dictionary. In fact, the discriminant information hidden behind the value of sparse coefficient is not fully exploited. In this letter, we analyze the SR-based framework from the perspective of sparse coefficient, develop the participation degree (PD)-driven decision mechanism, and establish a concise model called constraint representation (CR). Based on CR, an improved version called adjacent CR (ACR) is further proposed, with consideration of spatial coherence via adjacent constraint. Experimental results using two real hyperspectral datasets verify the improvements of the proposed methods over the other related models and their spatial variants.

Automated summary

In this letter, we analyze the SR-based framework from the perspective of sparse coefficient, develop the participation degree (PD)-driven decision mechanism, and establish a concise model called constraint representation (CR). An improved version called adjacent CR (ACR) is further proposed, considering spatial coherence via adjacent constraint. Experimental results using two real hyperspectral datasets verify the improvements of the proposed methods over the other related models and their spatial variants.