MambaRefine-CD:基于MambaVision的遥感变化检测区域-边界时序精炼方法 / MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement
1️⃣ 一句话总结
该论文提出了一种用于遥感图像中建筑物变化检测的新方法,通过结合共享的MambaVision图像编码器和一种同时关注变化区域内部完整性与边界清晰度的双路径精炼机制,显著提升了检测的准确率和边界质量。
Binary change detection in remote sensing requires both complete changed-region localization and accurate boundary delineation. We present MambaRefine-CD, a region-boundary temporal refinement framework built on a shared MambaVision encoder. The proposed D-RBI module constructs temporal evidence from paired features, absolute differences, and signed differences, then separates it into region and Sobel-conditioned boundary streams. Region features are enhanced with CRAM-lite and decoded by an adaptive receptive-field FPN, while the finest boundary stream guides a bounded residual refinement of the coarse prediction. Experiments on DSIFN-CD and WHU-CD show strong changed-class F1 and IoU under verified evaluation settings, and ablations support the contribution of signed temporal evidence and the full region-boundary refinement pipeline.
MambaRefine-CD:基于MambaVision的遥感变化检测区域-边界时序精炼方法 / MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement
该论文提出了一种用于遥感图像中建筑物变化检测的新方法,通过结合共享的MambaVision图像编码器和一种同时关注变化区域内部完整性与边界清晰度的双路径精炼机制,显著提升了检测的准确率和边界质量。
源自 arXiv: 2607.04403