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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
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| 英文论文题目: |
Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
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| 论文题目英文: |
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| 作者: |
Shi, Cunzhao
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| 论文出处: |
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| 刊物名称: |
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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| 年: |
2017
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| 卷: |
14 |
| 期: |
6 |
| 页: |
816-820 |
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| 收录类别: |
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| 影响因子: |
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| 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. |
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