Segmented FPCA for hyperspectral image classification

Remote sensing hyperspectral image (HSI) contains significant information of ground surface which is actually acquired as a set of immense narrow and contiguous spectral bands. Proper classification approach can only give us the required knowledge from the hundreds of bands of HSI. Though it is quite difficult to extract features from these bands, dimensionality reduction techniques through feature extraction and feature selection are used to improve the classification performance of the HSI. Principal Component Analysis (PCA) is usually adopted as unsupervised linear feature extraction method for feature reduction. However, PCA can be failure to extract local characteristics of the HSI due to considering global variance. Thus, segmented-PCA (SPCA) and folded-PCA (FPCA) are used to efficiently extract the local structures in different ways. In this paper, feature extraction using FPCA, termed as segmented FPCA (SFPCA), has been improved through applying it on the highly correlated bands' segments of the real HSI rather than not applying on the whole dataset directly. The effectiveness of SFPCA is additionally compared with the unsupervised nonlinear feature extraction methods kernel-PCA (KPCA) and Kernel Entropy Component Analysis (KECA). The experimental result shows that the classification accuracy of SFPCA (95.6262%) outperforms conventional FPCA (95.1292%), SPCA (93.837%) and PCA (93.7376%) providing the least space complexity. Moreover, it attempts to fix the nonlinearity like very similar to KPCA (95.9245%) and KECA (95.6262%).