自然资源遥感2026，Vol.38Issue(2)：89-97,9.DOI:10.6046/zrzyyg.2025073
基于去噪扩散概率模型的台风图像生成式路径预测方法
A typhoon image-based generative path prediction method using the denoising diffusion probabilistic model
摘要
Abstract
Typhoon path prediction serves as a core technology for early warning of meteorological disasters.Its accuracy improvement holds significant scientific value for disaster prevention and mitigation.However,in the modeling of temporal features of typhoon satellite images,existing deep learning models face challenges such as insufficient dynamic weight allocation and loss of high-frequency details.Hence,this study proposed a typhoon generative diffusion model(TGDiff)based on the denoising diffusion probabilistic model(DDPM):the DDPM-TGDiff model.Specifically,a channel prior convolutional attention(CPCA)mechanism was employed to dynamically decouple channel dependencies and spatiotemporal evolution patterns in typhoon images,enabling precise modeling of typhoon motion characteristics.Combined with the joint optimization in time and frequency domains through a high-frequency perceptual loss(HFPL)function,the DDPM-TGDiff model can more effectively capture the details of eyewall motion trajectories and cloud system evolution.Considering the temporal discontinuities in satellite data,a bidirectional dynamic interpolation module was incorporated to achieve physically consistent reconstruction of missing time-series data within the diffusion framework.Experimental results demonstrate that the DDPM-TGDiff model yielded a mean error of 63.36 km in predicting the 6 h typhoon path on the typhoon dataset of the northwest Pacific.The generated prediction image showed a peak signal-to-noise ratio(PSNR)of 16.21,a structural similarity index measure(SSIM)of 0.254,and a learned perceptual image patch similarity(LPIPS)of 0.149.Compared to existing deep learning models,the DDPM-TGDiff model significantly improves the structural fidelity of generated typhoon features and prediction accuracy.关键词
台风路径预测/扩散模型/高频感知损失/时序插值/深度学习Key words
typhoon path prediction/diffusion model/high-frequency perceptual loss(HFPL)/temporal interpo-lation/deep learning分类
信息技术与安全科学引用本文复制引用
郑宗生,史帅帅,张月维,周涛,郭诗仪,刘静静,赵浩然..基于去噪扩散概率模型的台风图像生成式路径预测方法[J].自然资源遥感,2026,38(2):89-97,9.基金项目
上海市科委地方能力建设项目"复杂潮汐环境下海岛(礁)地物信息提取与精度验证方法及其示范应用"(编号:19050502100)及广州气象卫星地面站项目"基于气象卫星遥感的台风中心定位AI模型引进"(编号:D-8006-23-0157)共同资助. （礁）
