Target Class Oriented Subspace Detection for Effective Hyperspectral Image Classification

Achieving high classification accuracy in hyperspectral image classification is a challenging task. This problem can be addressed by reducing the irrelevant features for the task of classification. Principal Component Analysis (PCA) is a popular feature extraction technique but it depends solely on global variance which makes it limited for some application. To address this, a target class oriented feature reduction method is proposed which incorporates the normalized Mutual Information (NMI) over PCA images to maximize the relevance of the selected subspace. Experimental analysis is performed to assess the effectiveness of the proposed method and the selected subspace is evaluated using kernel Support Vector Machine (KSVM) classifier. The proposed approach can achieve 96.57% classification accuracy on real hyperspectral data which is better than the standard approaches studied.