Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network

doi:10.1109/LGRS.2017.2735801

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network
	Zhan, Yongjie 1,2; Wang, Jian 2; Shi, Jianping 3; Cheng, Guangliang4 ; Yao, Lele 2; Sun, Weidong 1
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2017-10-01
卷号	14 期号:10 页码:1785-1789
文章类型	Article
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.
关键词	Cloud And Snow Detection Fully Convolutional Network Multiscale Prediction
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/LGRS.2017.2735801
收录类别	SCI
语种	英语
项目资助者	State Key Laboratory of Space-Ground Integrated Information Technology of China(2014 CXJJ-YG 04)
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000413961200028
引用统计	被引频次：89[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/20755
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	1.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
推荐引用方式 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.