Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
Shi, Cunzhao1; Wang, Chunheng1; Wang, Yu1,2; Xiao, Baihua1
2017-06-01
发表期刊IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷号14期号:6页码:816-820
文章类型Article
摘要Ground-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.
关键词Cloud Classification Convolutional Activations Convolutional Neural Network (Cnn) Fine-tune Max Pooling Sum Pooling
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2681658
关键词[WOS]IMAGES ; RECOGNITION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61531019 ; 61601462 ; 71621002)
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:000402092300006
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15125
专题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
作者单位1.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
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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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