Band Clustering-Based Feature Extraction for Classification of Hyperspectral Images Using Limited Training Samples

Feature extraction plays a central role in classification of hyperspectral data. We propose a clustering-based feature extraction (CBFE) method in this letter. The proposed method is supervised and only needs to calculate the first-order statistics. Thus, CBFE has better performance than some popular supervised feature extraction methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted feature extraction in small sample size situation. In addition, CBFE works better than unsupervised approaches such as principal component analysis in classification applications. CBFE considers a vector associated with each band that is composed by the mean values of all classes in that band. Then, a clustering method such as k-means is run to group the similar bands in one cluster. The selected number of clusters is equal to the number of extracted features. Experiments carried out on two different hyperspectral data sets demonstrate that the CBFE has better performance in comparison with some conventional feature extraction methods.