A subspace weighting kernel method combining clustering-based grouping for feature extraction in hyperspectral imagery classification

High dimensionality of hyperspectral data and relatively limited training samples induce the Hughes phenomenon in hyperspectral image classification. To prevent this problem and decrease the computational cost, feature extraction often acts as pre-processing. In this paper, a subspace weighting kernel method combining clustering-based grouping is proposed for feature extraction in hyperspectral imagery classification. In the proposed method, spectral bands of hyperspectral data are firstly grouped into subspaces and a subspace-modulated kernel principal component analysis (SM-KPCA) is given for feature extraction, where the modulated kernel is determined by classification-oriented schemes. Support vector machine (SVM) classifier is performed on the extracted features to validate the performance. Experiments are conducted on real data and the results prove that the proposed SM-KPCA is effective on feature extraction for improving the accuracy of hyperspectral classification.