Volume 38, Number 4, August 2020
|Page(s)||740 - 746|
|Published online||06 October 2020|
Cloud Image Classification Method Based on Deep Convolutional Neural Network
School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China
Compared with satellite remote sensing images, ground-based invisible images have limited swath, but featured in higher resolution, more distinct cloud features, and the cost is greatly reduced, conductive to continuous meteorological observation of local areas. For the first time, this paper proposed a high-resolution cloud image classification method based on deep learning and transfer learning technology for ground-based invisible images. Due to the limited amount of samples, traditional classifiers such as support vector machine can't effectively extract the unique features of different types of clouds, and directly training deep convolutional neural networks leads to over-fitting. In order to prevent the network from over-fitting, this paper proposed applying transfer learning method to fine-tune the pre-training model. The proposed network achieved as high as 85.19% test accuracy on 6-type cloud images classification task. The networks proposed in this paper can be applied to classify digital photos captured by cameras directly, which will reduce the cost of system greatly.
Key words: cloud classification / convolutional neural network / deep learning / transfer learning / model
关键字 : 云分类 / 卷积神经网络 / 深度学习 / 迁移学习 /
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
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