基于CNN-LSTM的卫星云图云分类方法研究

Study on Cloud Classification Method of Satellite Cloud Images Based on CNN-LSTM

卫星云图分类一直都是气象领域的研究热点之一,但存在同一云型光谱特征不同、不同云型光谱特征相同以及主要利用点云光谱特征而忽视空间特征等问题。针对以上问题,提出一种基于CNN-LSTM网络的卫星云图云分类方法,充分利用光谱信息和空间信息来提升云分类准确率。首先,根据云的物理特性对光谱特征进行筛选,并结合点云的正方形邻域作为点云的空间信息;然后,通过卷积神经网络(Convolutional neural network,CNN)自动提取空间特征,解决了单用光谱特征分类难的问题;最后,在此基础上结合长短时记忆网络(Long short-term memory,LSTM)提取的空间局部差异特征,为卫星云图分类提供多角度特征,解决了云块空间结构相似导致误判的问题。实验结果表明,所提方法对卫星云图的整体分类准确率达到93.4%,相比单一CNN方法的整体云分类准确率提高了2.7%。

The classification of satellite cloud images has always been one of the research hotspots in the field of meteorology.But there are some problems,such as the same cloud type has different spectral features,different cloud types have the same spectral features,and mainly use the spectral features and ignore spatial features.To solve the above problems,this paper proposes a cloud classification method of satellite cloud image based on CNN-LSTM,which makes full use of spectral information and spatial information to improve the accuracy of cloud classification.Firstly,the spectral features are screened based on the physical characteristics of the cloud,and the square neighborhood of the point cloud is used as the spatial information.Then,the convolutional neural network(CNN)is used to automatically extract the spatial features,which solves the problem of difficult classification with spectral feature alone.Finally,on this basis,combined with the spatial local difference features extracted by the long short-term memory(LSTM)network,it provides multi-view features for the classification of satellite cloud images,and solves the problem of misjudgment caused by the similarity of cloud spatial structure.Experimental results show that the overall classification accuracy of the proposed method for satellite cloud images reaches 93.4%,which is 2.7%higher than that of the single CNN method.