Title: Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
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| Authors: Zhan, YJ; Wang, J; Shi, JP; Cheng, GL; Yao, LL; Sun, WD
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| Author Full Names: Zhan, Yongjie; Wang, Jian; Shi, Jianping; Cheng, Guangliang; Yao, Lele; Sun, Weidong
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| Source: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 14 (10):1785-1789; 10.1109/LGRS.2017.2735801 OCT 2017
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| Language: English
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| Abstract: Cloud and snow detection has significant remote sensing applications, while they share similar low-level features due to their consistent color distributions and similar local texture patterns. Thus, accurately distinguishing cloud from snow in pixel level from satellite images is always a challenging task with traditional approaches. To solve this shortcoming, in this letter, we proposed a deep learning system to classify cloud and snow with fully convolutional neural networks in pixel level. Specifically, a specially designed fully convolutional network was introduced to learn deep patterns for cloud and snow detection from the multispectrum satellite images. Then, a multiscale prediction strategy was introduced to integrate the low-level spatial information and high-level semantic information simultaneously. Finally, a new and challenging cloud and snow data set was labeled manually to train and further evaluate the proposed method. Extensive experiments demonstrate that the proposed deep model outperforms the state-of-the-art methods greatly both in quantitative and qualitative performances.
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| ISSN: 1545-598X
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| eISSN: 1558-0571
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| IDS Number: FL1FX
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| Unique ID: WOS:000413961200028
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