Lightweight U-Net for cloud detection of visible and thermal infrared remote sensing images
OPTICAL AND QUANTUM ELECTRONICS(2020)
摘要
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.
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关键词
Fully convolutional network,Depthwise separable convolution,Cloud detection,Semantic segmentation,Lightweight network
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