Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
Deep Convolutional Activations-Based Features for Ground-Based Cloud Classification
复制标题
用于地基云分类的基于深度卷积激活的特征
DOI：
10.1109/lgrs.2017.2681658
复制
发表时间：
2017-06-01
影响因子：
4.8
通讯作者：
Xiao, Baihua
中科院分区：
文献类型：
--
作者：
Shi, Cunzhao;Wang, Chunheng;Xiao, Baihua
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.
