ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 885–892, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-885-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 885–892, 2020
https://doi.org/10.5194/isprs-annals-V-2-2020-885-2020

  03 Aug 2020

03 Aug 2020

AUTOMATIC CLOUD DETECTION METHOD BASED ON GENERATIVE ADVERSARIAL NETWORKS IN REMOTE SENSING IMAGES

J. Li1, Z. Wu1, Z. Hu2, Y. Zhang1, and M. Molinier3 J. Li et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
  • 2College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
  • 3VTT Technical Research Centre of Finland Ltd, Espoo, Finland

Keywords: Cloud detection, Generative adversarial networks (GANs), Attention mechanism, Deep learning, Auto-GAN

Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the difference between cloudy and cloud-free images (i.e. clouds). Last, a generator (G) is used to fill in the different regions in cloud image in order to confuse the discriminator. Auto-GAN only requires images and their labels (1 for a cloud-free image, 0 for a cloudy image) in the training phase which is more time-saving to acquire than existing methods based on CNNs that require pixel-level labels. Auto-GAN is applied to cloud detection in Sentinel-2A Level 1C imagery. The results indicate that Auto-GAN method performs well in cloud detection over different land surfaces.