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论文编号: |
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中文论文题目: |
Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
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英文论文题目: |
Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
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论文题目英文: |
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作者: |
Zhan, Yongjie
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论文出处: |
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刊物名称: |
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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年: |
2017
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卷: |
14 |
期: |
10 |
页: |
1785-1789 |
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摘要: |
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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