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SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation

文献类型：期刊论文

作者Xie, Wanyi1,3; Liu, Dong1,3; Yang, Ming2; Chen, Shaoqing2; Wang, Benge2; Wang, Zhenzhu1,3; Xia, Yingwei4; Liu, Yong1,4; Wang, Yiren1,3; Zhang, Chaofang4
刊名ATMOSPHERIC MEASUREMENT TECHNIQUES
出版日期2020-04-17
卷号13
ISSN号1867-1381
DOI10.5194/amt-13-1953-2020
通讯作者Wang, Yiren(wyiren90@mail.ustc.edu.cn) ; Zhang, Chaofang(zcf0413@mail.ustc.edu.cn)
英文摘要Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder-decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.
WOS关键词CLASSIFICATION ; SYSTEM
资助项目Science and Technology Service Network Initiative of the Chinese Academy of Sciences[KFJ-STS-QYZD-022] ; Youth Innovation Promotion Association, CAS[2017482] ; Research on Key Technology of Short-Term Forecasting of Photovoltaic Power Generation Based on All-sky Cloud Parameters[201904b11020031]
WOS研究方向Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000527801200002
出版者COPERNICUS GESELLSCHAFT MBH
资助机构Science and Technology Service Network Initiative of the Chinese Academy of Sciences ; Youth Innovation Promotion Association, CAS ; Research on Key Technology of Short-Term Forecasting of Photovoltaic Power Generation Based on All-sky Cloud Parameters
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/103349]  
专题中国科学院合肥物质科学研究院
通讯作者Wang, Yiren; Zhang, Chaofang
作者单位1.Univ Sci & Technol China, Grad Sch, Sci Isl Branch, Hefei 230026, Peoples R China
2.Civil Aviat Adm China, Anhui Air Traff Management Bur, Hefei 230094, Peoples R China
3.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Opt, Hefei 230088, Peoples R China
4.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Optoelect Appl Technol Res Ctr, Hefei 230031, Peoples R China
推荐引用方式
GB/T 7714		 Xie, Wanyi,Liu, Dong,Yang, Ming,et al. SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation[J]. ATMOSPHERIC MEASUREMENT TECHNIQUES,2020,13.
APA Xie, Wanyi.,Liu, Dong.,Yang, Ming.,Chen, Shaoqing.,Wang, Benge.,...&Zhang, Chaofang.(2020).SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation.ATMOSPHERIC MEASUREMENT TECHNIQUES,13.
MLA Xie, Wanyi,et al."SegCloud: a novel cloud image segmentation model using a deep convolutional neural network for ground-based all-sky-view camera observation".ATMOSPHERIC MEASUREMENT TECHNIQUES 13(2020).

入库方式： OAI收割

来源：合肥物质科学研究院

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