MRF-based decision fusion for hyperspectral image classification

The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.