Feature Reduction Based on Segmented Principal Component Analysis for Hyperspectral Images Classification

Subspace detection is an essential step which is used as a preprocessing for the task of hyperspectral image classification, and ground surface identification. An informative subspace can be obtained through feature extraction/feature selection or using both. This paper proposed an efficient subspace detection technique using a both segmented principal component analysis (SPCA) and normalized mutual information (NMI) measure. At first, the original dataset is partitioned into several groups using NMI measure and then perform the principal component transform (PCT) on each group. Finally, the NMI is utilized to select the most informative images to obtain a resultant subspace and this method is named as (SPCA-nMI). The proposed method is tested on two real hyperspectral images, the experimental results shows the superiority of the proposed approach and obtain 95.47% classification accuracy on dataset 1 and (99.026%) on dataset 2 which is best among the methods studied.