Hybrid Sub-space Detection Technique for Effective Hyperspectral Image Classification

Subspace detection for hyperspectral images is getting more interest now days because of the challenges of dealing with high dimensional feature space for reliable classification. The objective of supervised dimension reduction technique is to find a subspace of reliable features that preserves maximal information about the target objects. Principal Component Analysis and Mutual Information are two well-known feature extraction and feature selection method respectively, however, a combination of both could be a better approach which can significantly improve the feature reduction performances. In this paper, a hybrid approach is proposed which combines both the Principal Component Analysis (PCA) and Quadratic Mutual Information (QMI). The proposed method named (PCA-QMI) is tested on two real hyperspectral datasets and finally the classification accuracy is measured using kernel support vector machine classifier. The proposed method can detect subspace effectively and therefore the classification accuracy achieved is more than 99% which is better than the standard benchmark techniques.