Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images

Zhaocong Wu, Jun Li, Yisong Wang, Zhongwen Hu, Matthieu Molinier

Research output: Contribution to journalArticleScientificpeer-review

27 Citations (Scopus)


Cloud detection is an important step in the processing of remote sensing images. Most methods based on convolutional neural networks (CNNs) for cloud detection require pixel-level labels, which are time-consuming and expensive to annotate. To overcome this challenge, this letter proposes a novel semisupervised algorithm for cloud detection by training a self-attentive generative adversarial network (SAGAN) to extract the feature difference between cloud images and cloud-free images. Our main idea is to introduce visual attention into the process of generating 'real' cloud-free images. The training of SAGAN is based on three guiding principles: expansion of attention maps of cloud regions which will be replaced with translated cloud-free images, reduction of attention maps to coincide with cloud boundaries, and optimization of a self-attentive network to handle the extreme cases. The inputs for SAGAN training are the images and image-level labels, which are easier, cheaper, and more time-saving than the existing methods based on CNN. To test the performance of SAGAN, experiments are conducted on the Sentinel-2A Level 1C image data. The results show that the proposed method achieves very promising results with only the image-level labels of training samples.

Original languageEnglish
Article number8924781
Pages (from-to)1792-1796
JournalIEEE Geoscience and Remote Sensing Letters
Issue number10
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed


  • Cloud detection
  • deep learning (DL)
  • generative adversarial network (GAN)
  • remote sensing
  • self-attention


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