论文标题
自我配置的NNU-NET检测卫星图像中的云
Self-Configuring nnU-Nets Detect Clouds in Satellite Images
论文作者
论文摘要
云检测是一个关键的卫星图像预处理步骤，可以在地面和船上执行以标记有用图像的卫星。在后一种情况下，它可以通过修剪多云的区域来减少数据量，或者通过对多云区域的重新安排进行数据驱动的采集来使卫星更加自主。我们使用NNU-NET来处理这项重要的任务，NNU-NET是一个自我调查的框架，能够通过各种数据集对细分网络进行元学习。我们的实验是通过Sentinel-2和Landsat-8多光谱图像进行的，表明NNU-NET提供了最先进的云分割性能，而无需任何手动设计。 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阈值：0.652）。
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).
