基于DAR-CapsNet的地基云图云分类  

CLOUD CLASSIFICATION OF GROUND-BASED CLOUD IMAGES BASED ON DAR-CapsNet

作　　者：魏亮 朱婷婷 过奕任 倪超[1] 滕广 李岩 Wei Liang;Zhu Tingting;Guo Yiren;Ni Chao;Teng Guang;Li Yan(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学机械电子工程学院,南京210037

出　　处：《太阳能学报》2023年第11期189-195,共7页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金青年项目(62006120)。

摘　　要：在当前地基云图分类任务中,存在识别准确率低等问题。为了提高云分类的精度,有效融合深度可分离卷积、注意力机制和残差结构的特点,构建DAR-CapsNet地基云图分类模型。首先,收集整理美国国家新能源实验室公开数据库中的地基云图,构建云分类数据库;然后,对所提出的DAR-CapsNet分类模型进行训练优化;最后,在不同数据集上验证所提出的分类模型性能。实验结果表明所提出的DAR-CapsNet分类模型,分类准确率高达95.50%,优于现有公开分类方法,且在不同数据集上具有较好的泛化性能。In the current ground-based cloud image classification task,there are problems such as low recognition accuracy.In order to improve the accuracy of cloud classification,the DAR-CapsNet classification model for ground-based cloud images has been constructed by effectively integrating the features of depthwise separable convolution,attention mechanism and residual structure.Firstly,the ground-based cloud images were collected from the public database of the National New Energy Laboratory of the United States to build a cloud classification database;then,the proposed DAR-CapsNet classification model was trained and optimized;finally,experiments were conducted on different datasets to verify the performance of the proposed classification model.The experimental results show that the classification accuracy of the DAR-CapsNet model is as high as 95.50%,which is better than some published classification models,and the DAR-CapsNet model has better generalization performance on different datasets.

关 键 词：光伏发电 气象云 图像分类 卷积神经网络 机器学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]