JOINT MULTI-FEATURE HYPERSPECTRAL IMAGE CLASSIFICATION WITH SPATIAL CONSTRAINT IN SEMANTIC MANIFOLD

This paper presents a novel method for hyperspectral classification combining multiple features and exploiting spatial information at the same time. We proposed a supervised classification method under the Markov random field (MRF)-based framework. Firstly using the probability SVM to map multiple features from different low-level subspace to the same semantic space (probability space), then integrating these features in semantic space with MRF-based model to enforce a smooth and accurate representation, in addition the manifold distance has been used in MRF-based model to measure the similarity of two point. To further improve the classification accuracy, a new approach of building the adaptive neighborhood has been proposed and used in our method. As our model is a derivable and convex problem, gradient descent can be used to solve this problem with less computational and time cost. Experimental results on real hyperspectral dataset shows that the proposed method provides improved classification accuracy in terms of the overall accuracy, average accuracy and kappa statistic.