Multiresolution Fusion and Classification of Hyperspectral and Panchromatic Remote Sensing Images

This paper proposes a supervised method for the joint classification and fusion of multiresolution panchromatic and hyperspectral data based on the combination of probabilistic graphical models (PGMs) and deep learning methods. The idea is to exploit the spatial and spectral information contained in panchromatic and hyperspectral images at different resolutions with the aim to generate a classification map at the spatial resolution of the panchromatic channel, while exploiting the richness of the spectral information provided by the hyperspectral channels. The proposed technique is based on deep learning, with FCN-type architectures, and PGMs, through the definition of a conditional random field (CRF) model approximating the behavior of the ideal fully connected CRF in a computationally tractable manner. The neural architecture aims to integrate hyperspectral and panchromatic data at the corresponding spatial resolution and generate posterior probability estimates, while the CRF incorporates information associated with not only local but also long-distance spatio-spectral relationships. The algorithm has been experimentally validated with PRISMA data from the Italian Space Agency with promising results.