Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Many studies show that nonparametric weighted feature extraction (NWFE) is a powerful tool for extracting hyperspectral image features. The detection of class boundaries is an important part in NWFE and the weighted mean was defined for this purpose. In this paper, a kernel-based feature extraction is proposed based on a new class boundary detection mechanism. The soft-margin support vector machine (SVM) binary classifier and the support vector domain description (SVDD) are applied to detect the boundaries between two classes and one class, respectively. The results of real data experiments show that the proposed method outperforms original NWFE.
['Cheng-Hsuan Li', 'Bor-Chen Kuo', 'Chin-Teng Lin', 'Chih-Cheng Hung']
Dimension Reduction for Hyperspectral Image Classification via Support Vector based Feature Extraction