Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we explore how recent advances in multi-image fusion can be leveraged to bootstrap single image cloud detection. We demonstrate that a network optimized to estimate image quality also implicitly learns to detect clouds. To support the training and evaluation of our approach, we collect a large dataset of Sentinel-2 images along with a per-pixel semantic labelling for land cover. Through various experiments, we demonstrate that our method reduces the need for annotated training data and improves cloud detection performance.
|Title of host publication||2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings|
|Number of pages||4|
|State||Published - Sep 26 2020|
|Event||2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States|
Duration: Sep 26 2020 → Oct 2 2020
|Name||International Geoscience and Remote Sensing Symposium (IGARSS)|
|Conference||2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020|
|Period||9/26/20 → 10/2/20|
Bibliographical notePublisher Copyright:
© 2020 IEEE.
- multi-image fusion
- weakly-supervised learning
ASJC Scopus subject areas
- Computer Science Applications
- Earth and Planetary Sciences (all)