Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
Shi, Cunzhao1; Wang, Chunheng1; Wang, Yu1,2; Xiao, Baihua1
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
2017-06-01
Volume14Issue:6Pages:816-820
SubtypeArticle
AbstractGround-based cloud classification is crucial for meteorological research and has received great concern in recent years. However, it is very challenging due to the extreme appearance variations under different atmospheric conditions. Although the convolutional neural networks have achieved remarkable performance in image classification, no one has evaluated their suitability for cloud classification. In this letter, we propose to use the deep convolutional activations-based features (DCAFs) for ground-based cloud classification. Considering the unique characteristic of cloud, we believe the local rich texture information might be more important than the global layout information and, thus, give a comprehensive evaluation of using both shallow convolutional layers-based features and DCAFs. Experimental results on two challenging public data sets demonstrate that although the realization of DCAF is quite straightforward without any use-dependent tricks, it outperforms conventional hand-crafted features considerably.
KeywordCloud Classification Convolutional Activations Convolutional Neural Network (Cnn) Fine-tune Max Pooling Sum Pooling
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2681658
WOS KeywordIMAGES ; RECOGNITION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61531019 ; 61601462 ; 71621002)
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:000402092300006
Citation statistics
Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15125
Collection复杂系统管理与控制国家重点实验室_影像分析与机器视觉
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Shanxi Univ, Sch Software, Taiyuan 030006, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shi, Cunzhao,Wang, Chunheng,Wang, Yu,et al. Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(6):816-820.
APA Shi, Cunzhao,Wang, Chunheng,Wang, Yu,&Xiao, Baihua.(2017).Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(6),816-820.
MLA Shi, Cunzhao,et al."Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.6(2017):816-820.
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