Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 17, Issue 10, Pages 1792-1796Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2955071
Keywords
Image restoration; Remote sensing; Gallium nitride; Generative adversarial networks; Clouds; Training; Optimization; Cloud detection; deep learning (DL); generative adversarial network (GAN); remote sensing; self-attention
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Funding
- National Key Research and Development Program of China [2017YFC0506200]
- National Natural Science Foundation of China [41501369, 41871227]
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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.
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