Incorporating spatial properties in subspace detection

The aim of this analysis is to develop a subspace detection technique using a hybrid approach which combines nonlinear feature extraction and feature selection for the task of hyperspectral image classification. In the proposed approach Kernel Principal Component Analysis (KPCA) is applied at the first step to generate the new features from the original data. Then pixel based spatial correlation is measured for each of the KPCA images to rank them based on their spatial objects/contents. These KPCA and spatial correlation based ranking scores are combined to obtain an informative subset of features. The experimental analysis conducted on a real hyperspectral image acquired by the AVIRIS sensor shows the advantage of the proposed approach in terms of classification accuracy.