Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification

Abstract

ABSTRACT The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands. Appropriate classification performance can only offer the required knowledge from these immense bands of HSI since the classification result is not reasonable using all the original features (bands) of the HSI. Although it is not easy to calculate the intrinsic features from the bands, band (dimensionality) reduction techniques through feature extraction and feature selection are usually applied to increase the classification result and to fix the curse of dimensionality problem. Though the Principal Component Analysis (PCA) has been commonly adopted for the feature reduction of HSI, it can often fail to extract the local useful characteristics of the HSI for effective classification as it considers the global statistics of the HSI. Consequently, Segmented-PCA (SPCA), Spectrally-Segmented-PCA (SSPCA), Folded-PCA (FPCA) and Superpixelwise PCA (SuperPCA) have been introduced for better feature extraction of HSI in diverse ways. In this paper, feature extraction through SPCA & FPCA and SSPCA & FPCA, termed as Segmented-FPCA (SFPCA) and Spectrally-Segmented-FPCA (SSFPCA) respectively, has further been improved through applying FPCA on the highly correlated or spectrally separated bands’ segments of the HSI rather than not applying the FPCA on the entire dataset directly. The proposed methods are compared and analysed for a real mixed agricultural and an urban HSI classification using per-pixel SVM classifier. The experimental result shows that the classification performance using SSFPCA and SFPCA outperforms that of using conventional PCA, SPCA, SSPCA, FPCA, SuperPCA and using the entire original dataset without employing any feature reduction. Moreover, the proposed feature extraction methods provide the least memory and computation cost complexity.