用MambaVision提升遥感图像变化检测的边界精度与区域定位。
MambaRefine-CD: MambaVision with Region-Boundary Temporal Refinement
- 通过时序特征分离出区域与边界流，实现精细化变化检测。
- 在DSIFN-CD和WHU-CD上达到90.3%和88.7%的改变类F1分数。
- 适合需要高精度变化边界的遥感监测任务，如城市扩张分析。
遥感中的二值变化检测需同时实现完整变化区域定位与精确边界勾画。我们提出MambaRefine-CD，一个基于共享MambaVision编码器的区域-边界时序精炼框架。所提出的D-RBI模块从成对特征、绝对差值与符号差值中构建时序证据，并将其分离为区域流与Sobel条件化边界流。区域特征经由CRAM-lite增强，并由自适应感受野FPN解码；最细粒度的边界流引导粗预测的受限残差精炼。在DSIFN-CD和WHU-CD数据集上的实验表明，在验证评估设置下，该方法取得优异的改变类F1与交并比，消融实验证实符号时序证据及完整区域-边界精炼流程的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。