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Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
Zhan, Yongjie1,2; Wang, Jian2; Shi, Jianping3; Cheng, Guangliang4; Yao, Lele2; Sun, Weidong1
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
2017-10-01
Volume14Issue:10Pages:1785-1789
SubtypeArticle
AbstractCloud 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.
KeywordCloud And Snow Detection Fully Convolutional Network Multiscale Prediction
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2735801
Indexed BySCI
Language英语
Funding OrganizationState Key Laboratory of Space-Ground Integrated Information Technology of China(2014 CXJJ-YG 04)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000413961200028
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20755
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
2.Space Star Technol Co Ltd, State Key Lab Space Ground Integrated Informat Te, Beijing 100086, Peoples R China
3.SenseTime Grp Ltd, Beijing 100084, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhan, Yongjie,Wang, Jian,Shi, Jianping,et al. Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(10):1785-1789.
APA Zhan, Yongjie,Wang, Jian,Shi, Jianping,Cheng, Guangliang,Yao, Lele,&Sun, Weidong.(2017).Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(10),1785-1789.
MLA Zhan, Yongjie,et al."Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.10(2017):1785-1789.
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