Optical image gap filling using deep convolutional autoencoder from optical and radar images

A major issue affecting optical imagery is the presence of clouds. The need of cloud-free scenes at specific date is crucial in a number of operational monitoring applications. On the other hand, the cloud-insensitive SAR sensors are a solid asset and they provide orthogonal information with respect to optical satellite, that enable the retrieval of information lost in optical images due to cloud cover. In the context of an increasing availability of both optical and SAR images, thank to the Sentinel constellation, we propose a deep learning method to reconstruct (gap-fill) optical data, polluted by cloud phenomena, exploiting multi-temporal SAR and optical images.