Remote sensing hyperspectral image (HSI) retains significant information of ground surface which is actually acquired as a set of hundreds narrow and contiguous spectral bands. 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 the commonly adopted feature extraction technique for dimensionality reduction of HSI. 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 introduced to effectively 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 feature selection over the new transformed features was carried out using cumulative-variance accumulation based approach. The experimental result shows that the classification accuracy of SFPCA (95.6262%) outperforms conventional FPCA (95.1292%), SPCA (93.837%) and PCA (93.7376%). Moreover, it provides the least space complexity.