Unsupervised band extraction for hyperspectral images using clustering and kernel principal component analysis

Abstract

ABSTRACTThe ‘curse of dimensionality’ is a drawback for classification of hyperspectral images. Band extraction is a technique for reducing the dimensionality and makes it computationally less complex for classification. In this article, an unsupervised band extraction method for hyperspectral images has been proposed. In the proposed method, kernel principal component analysis (KPCA) is used for transformation of the original data, which integrates the nonlinear characteristics, as well as, the advantages of principal component analysis and extract higher order statistics of data. The KPCA is highly dependent on the number of patterns for calculating kernel matrix. So, a proper selection of subset of patterns, which represent the original data properly, may reduce the computational cost for the proposed method with considerably better performance. Here, density-based spatial clustering technique is first used to group the pixels according to their similarity, and then some percentages of pixels from each cluster are selected to form the proper subset of patterns. To demonstrate the effectiveness of the proposed clustering- and KPCA-based unsupervised band extraction method, investigation is carried out on three hyperspectral data sets, namely, Indian, KSC, and Botswana. Four evaluation measures, namely classification accuracy, kappa coefficient, class separability, and entropy are calculated over the extracted bands to measure the efficiency of the proposed method. The performance of the proposed method is compared with four state-of-the-art unsupervised band extraction approaches, both qualitatively and quantitatively, and shows promising results compared to them in terms of four evaluation measures.