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- W3216185315 abstract "The detection of clouds in satellite images is an essential preprocessing task for big data in remote sensing. Convolutional neural networks (CNNs) have greatly advanced the state-of-the-art in the detection of clouds in satellite images, but existing CNN-based methods are costly as they require large amounts of training images with expensive pixel-level cloud labels. To alleviate this cost, we propose Fixed-Point GAN for Cloud Detection (FCD), a weakly-supervised approach. Training with only image-level labels, we learn fixed-point translation between clear and cloudy images, so only clouds are affected during translation. Doing so enables our approach to predict pixel-level cloud labels by translating satellite images to clear ones and setting a threshold to the difference between the two images. Moreover, we propose FCD+, where we exploit the label-noise robustness of CNNs to refine the prediction of FCD, leading to further improvements. We demonstrate the effectiveness of our approach on the Landsat-8 Biome cloud detection dataset, where we obtain performance close to existing fully-supervised methods that train with expensive pixel-level labels. By fine-tuning our FCD+ with just 1% of the available pixel-level labels, we match the performance of fully-supervised methods." @default.
- W3216185315 created "2021-12-06" @default.
- W3216185315 creator A5024068673 @default.
- W3216185315 creator A5031166339 @default.
- W3216185315 date "2021-11-23" @default.
- W3216185315 modified "2023-09-27" @default.
- W3216185315 title "Weakly-Supervised Cloud Detection with Fixed-Point GANs." @default.
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- W3216185315 hasPublicationYear "2021" @default.
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