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Analysis of PCA Based Feature Extraction Methods for Classification of Hyperspectral Image

Hyperspectral Image (HSI) is a rich source of information for the analysis of the earth's surface. HSI produces a rich set of both spectral and spatial information for possible recognition of earth materials, minerals and vegetation categories. Since HSI has high dimensional spectral information so that, feature extraction methods has been used to reduce the dimensions. The most widely used feature extraction method Principal Component Analysis (PCA) is applied in HSI for dimension reduction. The aim of this paper is to analyze PCA and its different variants Segmented-PCA (SPCA), Folded-PCA (FPCA), and its nonlinear approach Kernel-PCA (KPCA) for effective feature extraction and classification of HSI. Moreover, the noise adjusted methods Minimum Noise fraction (MNF) and its variants segmented MNF is also studied for comparing the feature extraction methods. For comparing the robustness of the studied methods, two real HSI is used for the experiments. The experiments show that the classification accuracy of the MNF method are 95.94% and 97.61% for AVIRIS and HYDICE datasets respectively which outperforms that other PCA based methods.