This paper presents a deep-learning based framework for addressing the problem of accurate cloud detection in remote sensing images. This framework benefits from a Fully Convolutional Neural Network (FCN), which is capable of pixel-level labeling of cloud regions in a Landsat 8 image. Also, a gradient-based identification approach is proposed to identify and exclude regions of snow/ice in the ground truths of the training set. We show that using the hybrid of the two methods (threshold-based and deep-learning) improves the performance of the cloud identification process without the need to manually correct automatically generated ground truths. In average the Jaccard index and recall measure are improved by 4.36% and 3.62%, respectively.
@article{arxiv.1810.05782,
title = {Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks},
author = {Sorour Mohajerani and Thomas A. Krammer and Parvaneh Saeedi},
journal= {arXiv preprint arXiv:1810.05782},
year = {2018}
}