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中文论文题目: Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
英文论文题目: Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
论文题目英文:
作者: Zhan, Yongjie
论文出处:
刊物名称: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
年: 2017
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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