Deep Networks Under Block-Level Supervision for Pixel-Level Cloud Detection in Multi-Spectral Satellite Imagery

Cloud cover hinders the usability of optical remote sensing imagery. Existing cloud detection methods either require hand-crafted features or utilize deep networks. Generally, deep networks perform better than hand-crafted features. However, deep networks for cloud detection need massive and expensive pixel-level annotation labels. To alleviate that, this paper proposes a weakly supervised deep learning-based cloud detection method using only block-level labels, with a new global convolutional pooling operation and a local pooling pruning strategy to improve the performance. For evaluating, we collect a training dataset containing over 160,000 image blocks with block-level labels and a testing dataset including ten large image scenes with pixel-level labels. Even under extremely weak supervision, our method performed well with the average overall accuracy reached 97.2 %. Experiments demonstrate that our proposed method obviously outperforms the state-of-the-art methods.