Remote sensing images are used for a variety of analyses, from agricultural monitoring, to disaster relief, to resource planning, among others. The images can be corrupted due to a number of reasons, including instrument errors and natural obstacles such as clouds. We present here a novel approach for reconstruction of missing information in such cases using only the corrupted image as the input. The Deep Image Prior methodology eliminates the need for a pre-trained network or an image database. It is shown that the approach easily beats the performance of traditional single-image methods.
@article{arxiv.2004.04209,
title = {A single image deep learning approach to restoration of corrupted remote sensing products},
author = {Anna Petrovskaia and Raghavendra B. Jana and Ivan V. Oseledets},
journal= {arXiv preprint arXiv:2004.04209},
year = {2020}
}
Comments
Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)