LOCALITY AND GLOBALITY DISCRIMINANT FEATURE AND ITS APPLICATION IN HYPERSPECTRAL IMAGE CLASSIFICATION

Feature selection has attracted a huge amount of interest in both research and application communities of hyperspectral image (HSI) classification. Generally, supervised feature selection methods are superior to unsupervised ones without label information. However, in classification of HSI, the labeled samples are often difficult, expensive or time-consuming to obtain. In this paper, we proposed a novel semi-supervised feature selection method, called Locality and Globality Discriminant Feature (LGDF), for HSI classification. This method combines Fisher's criteria and Graph Laplacian, which makes full use of both labeled and unlabeled data points to discover both manifold and discriminant structure in HSI data. In the proposed method, an optimal subset of features is identified if at this subset neighbor points or points sharing the same label are close to each other, while non-neighbor points or points with different labels are far away from each other. Experimental results on Washington DC Mall and AVIRIS Indian Pines hyperspectral datasets demonstrate the effectiveness of the proposed method.