With the increasing availability of optical and synthetic aperture radar
(SAR) images thanks to the Sentinel constellation, and the explosion of deep
learning, new methods have emerged in recent years to tackle the reconstruction
of optical images that are impacted by clouds. In this paper, we focus on the
evaluation of convolutional neural networks that use jointly SAR and optical
images to retrieve the missing contents in one single polluted optical image.
We propose a simple framework that ease the creation of datasets for the
training of deep nets targeting optical image reconstruction, and for the
validation of machine learning based or deterministic approaches. These methods
are quite different in terms of input images constraints, and comparing them is
a problematic task not addressed in the literature. We show how space
partitioning data structures help to query samples in terms of cloud coverage,
relative acquisition date, pixel validity and relative proximity between SAR
and optical images. We generate several datasets to compare the reconstructed
images from networks that use a single pair of SAR and optical image, versus
networks that use multiple pairs, and a traditional deterministic approach
performing interpolation in temporal domain.Comment: 17 page