Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

2026-09-22

AI Summary

为解决高分辨率遥感图像变化检测中的计算成本和精度问题，提出了一种结合时序状态空间建模和logit空间扩散的方法BMD-CD，通过双向状态传播和多尺度解码实现高效准确的变化检测。

Abstract

Remote sensing change detection requires both global reasoning across bitemporal images and precise localization of changed regions. However, dense attention is computationally expensive for high-resolution imagery, while conventional feature fusion and coarse decoding may inadequately separate genuine changes from appearance variations or preserve object boundaries. We present Bitemporal Mamba-Diffusion for Change Detection (BMD-CD), which combines temporally structured state-space modeling with logit-space diffusion refinement. BMD-CD converts deep bitemporal features into region tokens and arranges them in explicit temporal partitions before bidirectional state-space propagation. Its Bitemporal Ordered Mamba Operator enables long-range cross-temporal interaction with linear sequence complexity, while Orthogonal Feature Disentanglement forms a change-oriented output and a complementary rotated output using learned pairwise rotations and unchanged-region consistency. Multiscale decoding then produces coarse change logits, which are refined through a five-step Conditional Diffusion Decoder operating directly in logit space. Experiments on LEVIR-CD, WHU-CD, DSIFN-CD, CDD, and S2Looking demonstrate strong performance across diverse change-detection settings. BMD-CD achieves F1 scores of 93.7%, 96.0%, 97.8%, and 99.0% on the four standard benchmarks and improves 3-pixel Boundary-F1 to 87.7% and 91.4% on LEVIR-CD and WHU-CD, respectively. The full model requires 32.09 GFLOPs and 47 ms per 256 x 256 image pair, while also showing zero-shot transfer to ValaisCD and B-FLAIR-test. Our code is available at https://github.com/Aparup2139/Public_WACV/

Problem

• Remote Sensing
• Change Detection
• Bitemporal Images
• Feature Fusion
• Object Boundaries

Innovation

• Bitemporal Mamba-Diffusion
• state-space modeling
• logit-space diffusion refinement
• Orthogonal Feature Disentanglement
• Conditional Diffusion Decoder