通过连续特征迁移学习变化，提升遥感图像变化检测精度。
FM-ChangeNet: Learning Change through Pathwise Feature Transport
- 将变化检测建模为特征空间的连续迁移过程，而非静态对比。
- 学习时间条件速度场，实现更密集、更清晰的监督信号。
- 速度场兼具运输与可解释性，能区分真实变化与光照扰动。
我们提出 FM-ChangeNet，一种基于路径监督的变化检测框架，将双时相推理重构为特征空间中的连续传输，而非静态端点比较。给定预处理和后处理的编码表示，构建中间潜在状态，并学习沿变换轨迹的时间条件速度场 $ ilde{v}_θ(z_t,t)$。该路径式建模在连续中间状态上约束预测器，提供比传统仅端点分割更密集、更少歧义的监督信号，使模型能显式捕捉时间演化过程。所学速度场不仅是传输机制，也是可解释的变化表征：其幅值作为局部化变化线索，有助于区分真实结构变化与光照变化、空间错位等干扰因素。我们设计了分层多尺度架构，包含跨时相对齐、时间条件粗到精流解码，以及耦合流监督、轨迹一致性、空间正则化与分割损失的统一目标。在遥感基准测试中，该框架生成更结构化且鲁棒的变化表征，达到最先进性能。
原文摘要 · Abstract (English)
We present FM-ChangeNet, a pathwise-supervised framework for change detection that reformulates bi-temporal reasoning as continuous transport in feature space rather than static endpoint comparison. Given encoded pre and post-temporal representations, we construct intermediate latent states and learn a time-conditioned velocity field $\hat{v}_θ(z_t,t)$ along the transformation trajectory. This pathwise formulation constrains the predictor over a continuum of intermediate states, providing a denser and less ambiguous supervision signal than conventional endpoint-only segmentation and enabling the model to capture temporal evolution explicitly. The learned velocity field is not only a transport mechanism but also an interpretable representation of change: its magnitude serves as a spatially localized change cue that helps distinguish true structural variation from nuisance effects such as illumination shifts and spatial misalignment. We develop a hierarchical multi-scale architecture with cross-temporal alignment, time-conditioned coarse-to-fine flow decoding, and a unified objective that couples flow supervision, trajectory consistency, spatial regularization, and segmentation loss. Experiments on remote sensing benchmarks show that the proposed framework produces more structured and robust change representations while achieving state-of-the-art performance.
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