Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification

We propose a kernel nonlocal joint collaborative representation classification method based on column generation for hyperspectral imagery. The proposed approach first maps the original spectral space to a higher implicit kernel space by directly taking the similarity measures between spectral pixels as a feature, and then utilizes a nonlocal joint collaborative regression model for kernel signal reconstruction and the subsequent pixel classification. We also develop two kinds of specific radial basis function kernels for measuring the similarities. The experimental results indicate that the proposed algorithms obtain a competitive performance and outperform other state-of-the-art regression-based classifiers and the classical support vector machines classifier.