基于多尺度运动记忆模型的遥感云图预测方法

doi:10.19678/j.issn.1000-3428.0069950

计算机工程 ›› 2026, Vol. 52 ›› Issue (3): 128-140. doi: 10.19678/j.issn.1000-3428.0069950

• 计算机视觉与图形图像处理 • 上一篇下一篇

基于多尺度运动记忆模型的遥感云图预测方法

张永宏¹^,²^,*(), 孙书林¹, 龚蒙¹, 王俊飞³, 马光义³

1. 南京信息工程大学自动化学院, 江苏南京 210044
2. 南京信息工程大学江苏省大气环境与装备技术协同创新中心, 江苏南京 210044
3. 南京信息工程大学电子与信息学院, 江苏南京 210044

收稿日期:2024-06-03 修回日期:2024-08-20 出版日期:2026-03-15 发布日期:2025-03-10
通讯作者: 张永宏
作者简介:
张永宏, 男, 教授、博士, 主研方向为遥感大数据分析、深度学习
孙书林, 本科生
龚蒙, 本科生
王俊飞, 硕士研究生
马光义, 博士研究生
基金资助:
国家重点研发计划(2021YFE0116900); 国家自然科学基金(42175157); 风云应用开创性项目(FY-APP-2022.0604)

Remote Sensing Cloud Image Prediction Method Based on Multi-scale Motion Memory Model

ZHANG Yonghong¹^,²^,*(), SUN Shulin¹, GONG Meng¹, WANG Junfei³, MA Guangyi³

1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China
3. School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Received:2024-06-03 Revised:2024-08-20 Online:2026-03-15 Published:2025-03-10
Contact: ZHANG Yonghong

摘要/Abstract

摘要：

针对现有深度学习模型难以捕获云团运动模式导致云图长期预测结果模糊、准确度低的问题, 提出一种基于多尺度运动记忆模型(MSMM_Net)的遥感云图预测方法。该模型采用空间多尺度记忆流和运动差分记忆流相融合的双分支记忆流架构, 分别提取输入图片序列隐含的高低频空间特征和序列运动特征, 从而同时获得图片的全局信息、细节信息和运动信息, 在预测阶段融合双分支记忆, 缓解特征丢失问题并增强模型对云团运动轨迹的预测能力。在此基础上, 使用像素损失和边缘损失相结合的融合损失函数指导模型的训练, 强化模型对图片边缘细节的关注度, 促使模型生成清晰的预测图片。实验结果表明, 与基准模型PredRNN相比, MSMM_Net在Moving MNIST数据集上的均方误差(MSE)降低了31.71%, 在可学习感知图像块相似性指标(LPIPS)上降低了64.7%, 在遥感卫星云图数据集上, 峰值信噪比(PSNR)和结构相似性指数(SSIM)指标分别提升了5.51%和5.38%, 表明该模型生成的预测图片序列与真实图片序列更加相似, 能够有效提升长期预测准确率。

关键词: 云图预测, 时空序列预测, 深度学习, 循环卷积网络, 遥感卫星

Abstract:

Existing deep learning models find it difficult to capture cloud motion patterns, resulting in long-term cloud prediction results that are fuzzy and low in accuracy. To address this problem, this study proposes a remote sensing cloud image prediction method based on a Multi-Scale Motion Memory Network (MSMM_Net). This model adopts a dual-branch memory-flow architecture that combines spatial multi-scale and motion-differential memory flows. It extracts high- and low-frequency spatial features and sequence motion features hidden in the input image sequence, thereby simultaneously obtaining global, detail, and motion information of the image. In the prediction stage, dual-branch memory is fused to alleviate the problem of feature loss and enhance the ability of the model to predict the trajectory of cloud clusters. On this basis, a fusion loss function combining pixel and edge losses is used to guide model training, enhance the model's attention to image edge details, and promote the generation of clear predicted images. Experimental results show that, compared with the benchmark model PredRNN, MSMM_Net reduces the Mean Square Error (MSE) by 31.71% on the Moving MNIST dataset and the Learned Perceptual Image Patch Similarity (LPIPS) by 64.7%. On the remote sensing satellite cloud image dataset, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) indicators improve by 5.51% and 5.38%, respectively, indicating that the predicted image sequence generated by the model is more similar to the real image sequence and can effectively improve long-term prediction accuracy.

Key words: cloud image prediction, spatio-temporal sequence prediction, deep learning, recurrent convolutional network, remote sensing satellite

张永宏, 孙书林, 龚蒙, 王俊飞, 马光义. 基于多尺度运动记忆模型的遥感云图预测方法[J]. 计算机工程, 2026, 52(3): 128-140.

ZHANG Yonghong, SUN Shulin, GONG Meng, WANG Junfei, MA Guangyi. Remote Sensing Cloud Image Prediction Method Based on Multi-scale Motion Memory Model[J]. Computer Engineering, 2026, 52(3): 128-140.

https://www.ecice06.com/CN/Y2026/V52/I3/128

图/表 15

图1 多尺度运动记忆网络整体结构

Fig.1 Overall structure of multi-scale motion memory network

图2 PredRNN网络结构

Fig.2 PredRNN network structure

图3 MDSTM结构

Fig.3 MDSTM structure

图4 运动差分注意力模块

Fig.4 Motion difference attention module

图5 Canny边缘检测算法高低阈值取值对比示例

Fig.5 Comparison example of high and low threshold values for Canny edge detection algorithm

图6 Moving MNIST数据集示例

Fig.6 Example of Moving MNIST dataset

图7 GOES-16卫星第13波段示例

Fig.7 Example of the 13th band of GOES-16 satellite

图8 Moving MNIST数据集上的评价指标趋势

Fig.8 Trends in evaluation metrics on the Moving MNIST dataset

图9 Moving MNIST数据集上的预测效果对比

Fig.9 Comparison of prediction effect on the Moving MNIST dataset

图10 GOES-16数据集上的评价指标趋势

Fig.10 Trend of evaluation indicators on the GOES-16 dataset

图11 GOES-16数据集的预测效果对比1

Fig.11 Comparison 1 of prediction effect of GOES-16 dataset

图12 GOES-16数据集的预测效果对比2

Fig.12 Comparison 2 of prediction effect of GOES-16 dataset

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