A new framework for hyperspectral image classification using multiple spectral and spatial features
This paper presents a new multiple feature learning approach for accurate spectral-spatial classification of hyperspec-tral images. The proposed method integrates multiple features based on the logarithmic opinion pool. We consider subspace multinomial logistic regression for classification as it exhibits a flexible structure for the combination of multiple features through the posterior probability. At the same time, it is able to cope with highly mixed hyperspectral data and with the presence of limited training samples. In this work, we considered lowpass filtering and morphological attribute profiles for spatial feature extraction. Our experimental results with a real hyperspectral images collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the proposed method exhibits state-of-the-art classification performance.