| Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification | |
Shi, Cunzhao1 ; Wang, Chunheng1; Wang, Yu1,2; Xiao, Baihua1
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| 发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 2017-06-01 | |
| 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 |
| DOI | 10.1109/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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 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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| Deep Convolutional A（755KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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