计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 675-679.doi: 10.11896/jsjkx.210300177
王杉1, 徐楚怡1, 师春香2, 张瑛3
WANG Shan1, XU Chu-yi1, SHI Chun-xiang2, ZHANG Ying3
摘要： 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%。
中图分类号:
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