Feature Extraction Using Attraction Points for Classification of Hyperspectral Images in a Small Sample Size Situation

Hyperspectral images provide a large volume of spectral bands. Feature extraction (FE) is an important preprocessing step for classification of high-dimensional data. Supervised FE methods such as linear discriminant analysis, generalized discriminant analysis, and nonparametric weighted FE use the criteria of class separability. Theses methods maximize the between-class scatter matrix and minimize the within-class scatter matrix. We propose a supervised FE method in this letter, which uses no statistical moments. Thus, it works well using limited training samples. The proposed FE method consists of two important phases. In the first phase, an attraction point for each class is found. In the second phase, by using an appropriate transformation, the samples of each class move toward the attraction point of their class. The experimental results on two real hyperspectral images demonstrate that FE using attraction points has better performance in comparison with some other supervised FE methods in a small sample size situation.

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