Spectral-spatial classification for hyperspectral data using SVM and subspace MLR
This paper presents a new multiple-classifier approach for accurate spectral-spatial classification of hyperspectral images, where the spectral information is exploited by combining probabilistic support vector machines (SVM) and subspace-based multinomial logistic regression (MLRsub) and the spatial information is exploited by means of a Markov random field (MRF) regularizer. The proposed approach is based on the decision fusion of global posterior probability distributions and local probabilities which result from the whole image and the class combinations map respectively. With respect to the SVM or MLRsub algorithms, the proposed method greatly improves the classification accuracy. Our experimental results with real hyperspectral images collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) and the Reflective Optics Spectrographic Imaging System (ROSIS), indicate that the proposed multiple-classifier system leads to state-of-the-art classification performance for cases with very limited number of training samples.