Image classification -or semantic segmentation -from input multiresolution imagery is a demanding task. In particular, when dealing with images of the same scene collected at the same time by very different acquisition systems, for example multispectral sensors onboard satellites and unmanned aerial vehicles (UAVs), the difference between the involved spatial resolutions can be very large and multiresolution information fusion is particularly challenging. This work proposes two novel multiresolution fusion approaches, based on deep convolutional networks, Bayesian modeling, and probabilistic graphical models, addressing the challenging case of input imagery with very diverse spatial resolutions. The first method aims to fuse the multimodal multiresolution imagery via a posterior probability decision fusion framework, after computing posteriors on the multiresolution data separately through deep neural networks or decision tree ensembles. The optimization of the parameters of the model is fully automated by also developing an approximate formulation of the expectation maximization (EM) algorithm. The second method aims to perform the fusion of the multimodal multiresolution information through a pyramidal tree structure, where the imagery can be inserted, modeled, and analyzed at its native resolutions. The application is to the semantic segmentation of areas affected by wildfires for burnt area mapping and management. The experimental validation is conducted with UAV and satellite data of the area of Marseille, France. The code is available at https://github.com/Ayana-Inria/BAS_UAV_satellite_fusion.