Self-Configuring nnU-Nets Detect Clouds in Satellite Images

• URL: http://arxiv.org/abs/2210.13659v1
• Date: Mon, 24 Oct 2022 23:39:58 GMT
• Title: Self-Configuring nnU-Nets Detect Clouds in Satellite Images
• Authors: Bartosz Grabowski, Maciej Ziaja, Michal Kawulok, Nicolas Long\'ep\'e, Bertrand Le Saux, Jakub Nalepa
• Abstract summary: nnU-Nets is a self-reconfigurable framework able to perform meta-learning of a segmentation network over various datasets. Our experiments, performed over Sentinel-2 and Landsat-8 multispectral images revealed that nnU-Nets deliver state-of-the-art cloud segmentation performance without any manual design.
• Score: 30.46904432868366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can help to reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling of the cloudy areas. We approach this important task with nnU-Nets, a self-reconfigurable framework able to perform meta-learning of a segmentation network over various datasets. Our experiments, performed over Sentinel-2 and Landsat-8 multispectral images revealed that nnU-Nets deliver state-of-the-art cloud segmentation performance without any manual design. Our approach was ranked within the top 7% best solutions (across 847 participating teams) in the On Cloud N: Cloud Cover Detection Challenge, where we reached the Jaccard index of 0.882 over more than 10k unseen Sentinel-2 image patches (the winners obtained 0.897, whereas the baseline U-Net with the ResNet-34 backbone used as an encoder: 0.817, and the classic Sentinel-2 image thresholding: 0.652).

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