- By:
- Couwenhoven, Scott; Ientilucci, Emmett; Park, Byung H; Hughes, David C
- Page Number:
- 619-622
- Issue Number:
- IGARSS 202
- Book Title:
- IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- International Geoscience and Remote Sensing Symposium (IGARSS)
- Conference Location:
- Kuala Lumpur, Malaysia
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/IGARSS46834.2022.9883550
Abstract
We propose a weakly-supervised, multitask framework for training a convolutional neural network to solve the problem of cloud shadow mitigation given only cloud and shadow masks as labels. The network minimizes the Wasserstein distance between shadows and their proximal sunlit neighborhoods, generating a supervisory signal directly from within the input image. We extract further utility from the shadow mask through multitask learning by introducing an auxiliary task of shadow segmentation. Our approach is advantageous since it performs mitigation in an end-to-end framework which requires only a shadowed image for inference. We apply this process to the Landsat 8 OLI SPARCS validation data set and demonstrate plausible results.
