Graph-based semi-supervised weighted band selection for classification of hyperspectral data

When the number of labeled samples is limited, traditional supervised feature selection techniques often fail due to unrepresentative sample problem. However, in classification of hyperspectral data, the labeled samples are often difficult, expensive or time-consuming to obtain. Recently, several semi-supervised feature selection algorithms have been proposed, which aim at doing feature selection using some unlabeled data. In this paper, a novel semi-supervised band selection method which aims to improve classification accuracy of highspectral remote sensing data is proposed. This algorithm combines Fisher´s criteria and Graph Laplacian, exploits labeled and unlabeled samples at the same time. With the help of the generalized eigenvalue method, we can easily get the loading factors from the linear transformation matrix to determine the weight value for each band. Experimental results demonstrate effectiveness of the proposed method.