Closest class measure based subspace detection for hyperspectral image classification

The objective of this study is to develop a hybrid nonlinear subspace detection technique in which Kernel Principal Component Analysis (KPCA) is combined with a Closest Class Pair (CCP) measure for the task of hyperspectral image classification. In the proposed approach, KPCA is applied first to generate the new features from original dataset then the CCP is applied to rank the features that are able to separate the complex or overlapping classes. Finally, the two ranked scores such as KPCA and CCP are combined to select a subset of features which is relevant and able to provide better discrimination among the input classes of interest. Experiments are performed on a real hyperspectral image acquired by the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor and it can be seen that the proposed approach obtained the best classification accuracy 84.58%.