Joint Within-Class Collaborative Representation for Hyperspectral Image Classification

Representation-based classification has gained great interest recently. In this paper, we extend our previous work in collaborative representation-based classification to spatially joint versions. This is due to the fact that neighboring pixels tend to belong to the same class with high probability. Specifically, neighboring pixels near the test pixel are simultaneously represented via a joint collaborative model of linear combinations of labeled samples, and the weights for representation are estimated by an l(2)-minimization derived closed-form solution. Experimental results confirm that the proposed joint within-class collaborative representation outperforms other state-of-the-art techniques, such as joint sparse representation and support vector machines with composite kernels.