PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification

The hyperspectral remote sensing images (HSIs) are acquired to encompass the essential information of land objects through contiguous narrow spectral wavelength bands. The classification accuracy is not often satisfactory in a cost-effective way using the entire original HSI for practical applications. To enhance the classification result of HSIs the band reduction strategies are applied which can be divided into feature extraction and feature selection methods. PCA (Principal Component Analysis), a linear unsupervised statistical transformation, is frequently adopted for the extraction of features from HSIs. In this paper, PCA and SPCA (Segmented-PCA), SSPCA (Spectrally Segmented-PCA), FPCA (Folded-PCA) and MNF (Minimum Noise Fraction) as linear variants of PCA together with KPCA (Kernel-PCA) and KECA (kernel Entropy Component Analysis) as nonlinear variants of…