Self-Configuring nnU-Nets Detect Clouds in Satellite Images
This addresses cloud detection for satellite image processing, enabling data reduction and autonomy, but is incremental as it applies an existing self-configuring framework to a new domain.
The paper tackled cloud detection in satellite images using nnU-Nets, achieving state-of-the-art performance with a Jaccard index of 0.882 on over 10k unseen Sentinel-2 patches, ranking in the top 7% in a challenge.
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).