DREAM-Mamba: Segmentation of VHR Remote Sensing Imagery Based on Dynamic Scanning and Adaptive Convolution

Remote sensing semantic segmentation faces significant challenges such as difficult multi-scale feature capture, complex and variable scenes, and boundary blurring. To address these issues, this study proposes the DREAM-Mamba model. The Dynamic Omnidirectional Scanning Mechanism is introduced to adaptively adjust scanning weights, synergizing global context with local details; the Adaptive Windmill Convolution Module is designed to accurately fit complex geometric features through dynamic sampling; and the Edge Enhancement Module is incorporated to suppress smoothing effects and preserve high-frequency edges. DREAM-Mamba achieves a 93.58% IoU on the WHU dataset and a 72.36% mIoU on the Potsdam dataset, notably becoming the only method to exceed 90% IoU (attaining 90.20%) in the Potsdam building category. The source code is available at https://github.com/wuduan521/DREAM-Mamba.