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- Title
Lightweight U-Net for cloud detection of visible and thermal infrared remote sensing images.
- Authors
Zhang, Jiaqiang; Li, Xiaoyan; Li, Liyuan; Sun, Pengcheng; Su, Xiaofeng; Hu, Tingliang; Chen, Fansheng
- Abstract
Accurate and rapid cloud detection is exceedingly significant for improving the downlink efficiency of on-orbit data, especially for the microsatellites with limited power and computational ability. However, the inference speed and large model limit the potential of on-orbit implementation of deep-learning-based cloud detection method. In view of the above problems, this paper proposes a lightweight network based on depthwise separable convolutions to reduce the size of model and computational cost of pixel-wise cloud detection methods. The network achieves lightweight end-to-end cloud detection through extracting feature maps from the images to generate the mask with the obtained maps. For the visible and thermal infrared bands of the Landsat 8 cloud cover assessment validation dataset, the experimental results show that the pixel accuracy of the proposed method for cloud detection is higher than 90%, the inference speed is about 5 times faster than that of U-Net, and the model parameters and floating-point operations are reduced to 12.4% and 12.8% of U-Net, respectively.
- Subjects
REMOTE sensing; CLOUDINESS; MICROSATELLITE repeats; MATHEMATICAL convolutions; THERMOGRAPHY; OPTICAL remote sensing
- Publication
Optical & Quantum Electronics, 2020, Vol 52, Issue 9, pN.PAG
- ISSN
0306-8919
- Publication type
Article
- DOI
10.1007/s11082-020-02500-8