Semi-supervised class-specific feature selection for VHR remote sensing images

Features relevant to a thematic class, that is, class-specific features are beneficial to thematic information extraction. However, existing class-specific feature selection methods require abundant labelled samples, while sample labelling is always labour intensive and time consuming. Therefore, it is necessary to select class-specific features with insufficient labelled objects. In this paper, we raise this problem as semi-supervised class-specific feature selection and propose a new two-stage method. First, a weight matrix fully integrates local geometrical structure and discriminative information. Second, the weight matrix is incorporated into a l(2); 1-norm minimization optimization problem of data reconstruction to objectively measure the effectiveness of features for a thematic class. Different from the explicit binarization in the label vector, the new method only implicitly employs binarization in the weight matrix. With area under receiver-operating characteristic curve, class-specific features result in an increase from 3% and 4% on average for Bayes and linear support vector machine, respectively.