面向动态时空融合的语义自适应扩散框架
Semantic-Adaptive Diffusion for Dynamic Spatiotemporal Fusion
把扩散模型当作多源卫星图像的条件生成器，用 Taylor 式解码器解耦低分辨率与高频分量来约束解空间，并配合时序特征对齐与语义自适应融合，在统一潜变量空间中做时空超分融合；这套「解耦约束 + 潜空间对齐」的思路可迁移到视频超分、多帧复原等需要跨帧细节补全的视觉任务。
原文依据 we propose a semantic-adaptive diffusion framework for dynamic spatiotemporal fusion (SA-STF)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论频繁且精确的地表监测对众多应用至关重要，然而现有卫星难以同时兼顾二者。时空融合（STF）通过整合多幅卫星图像来生成具有更高时序与空间分辨率的数据，从而应对这一挑战，使更频繁、更精确的地表观测成为可能。然而，由于多源图像之间存在显著的尺度差异，现有方法往往无法恢复动态地表变化。为解决这些难题，我们提出一种面向动态时空融合的语义自适应扩散框架（SA-STF），该框架利用由受 Taylor 启发的解码器解耦出的低分辨率特征与高频特征来约束解空间。通过引入时序特征对齐与语义自适应融合模块，SA-STF 将具有时序动态的多模态图像投影到统一的潜变量空间，并在保持重建图像光谱一致性的同时自适应地增强空间细节。在基准数据集上的实验表明，SA-STF 在定量与定性评估中均优于现有方法，在复杂动态场景中尤为明显。
英文原文摘要 1216 字符
Frequent and precise land surface monitoring is critical for numerous applications, yet existing satellites struggle to achieve both simultaneously. Spatiotemporal fusion (STF) tackles this challenge by integrating multiple satellite images to generate data with improved temporal and spatial resolution, enabling more frequent and precise land surface observations. However, current methods often fail to recover dynamic landscape changes due to significant scale discrepancies among multi-source images. To address these challenges, we propose a semantic-adaptive diffusion framework for dynamic spatiotemporal fusion (SA-STF), which constrains the solution space using low-resolution and high-frequency features decoupled via a Taylor-inspired decoder. By incorporating temporal feature alignment and semantic-adaptive fusion modules, SA-STF projects multimodal images with temporal dynamics into a unified latent space, and adaptively enhances spatial details while maintaining the spectral consistency of the reconstructed images. Experiments on benchmark datasets demonstrate that SA-STF outperforms existing methods in both quantitative and qualitative evaluations, particularly in complex and dynamic scenes.