CISRMamba: Cross-Modal Interaction and Scan-Routing Mamba for Multi-Sensor Flood Inundation Mapping

Accurate flood mapping is essential for disaster response, yet optical-SAR fusion remains difficult when clouds, speckle noise, and terrain interference cause the two sensors to provide uneven or conflicting evidence. Multimodal models may suffer from "modality collapse", while fixed scan patterns make water boundaries difficult to delineate. To address these issues, we propose CISRMamba, a multimodal state space model (SSM) based on a lightweight MobileMamba backbone. A Deformable Scan Router (DSR) adapts the scan trajectory to irregular flood contours while preserving SSM efficiency. Instead of forcing all regions into a rigid "align-then-fuse" process, the Feature Complementary Module (FCM) suppresses discrepant responses, supplies complementary information where one modality is incomplete, or enhances consistent evidence according to the local optical-SAR relationship. The Refined Feature Integration (RFI) module reduces distributional shift and modality collapse through a difference-guided residual, while the Spectral Refinement Block (SRB) recovers high-frequency spatial details weakened by downsampling. Experiments on CAU-Flood and Wuhan show that CISRMamba achieves mIoU scores of 91.53% and 58.67%, respectively, outperforming baselines based on CNNs, Transformers, and Mamba. These results indicate that CISRMamba combines context modeling with boundary-level refinement for efficient flood monitoring under heterogeneous weather and scene conditions.