Group sparsity based semi-supervised band selection for hyperspectral images

In this paper, we propose a novel group sparsity based semi-supervised band selection method. There are three key features in our method. First, it fulfills the band selection task by employing group sparsity on the regression coefficients in a robust linear regression for classification model, so that the selected bands hold lower classification errors. Second, the spatial smoothness prior is incorporated to preserve the similarity of spatial neighbors in band selection. Third, the objective function is efficiently optimized via an alternative iteration algorithm. Comparative results on two hyper-spectral data sets validate the effectiveness of our method, showing higher classification accuracies.