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CoMS-UNet: A Cohesive Multi-Scale Network for Semantic Reorganization and Scale-Aware Context Modeling in Remote Sensing Image Segmentation
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DOI：10.3390/s26154987.png)
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Abstract
En 中文
Semantic segmentation is essential for fine-grained land-cover interpretation from high-resolution remote sensing imagery. However, existing methods still show limitations in coordinating multi-stage encoder features, providing scale-aware contextual guidance at the bottleneck layer, and facilitating cross-channel information exchange during decoder reconstruction. To address these issues, this paper proposes CoMS-UNet, a cohesive multi-scale U-Net that integrates semantic reorganization, scale-aware context modeling, and channel-aware decoder reconstruction. In encoder–decoder skip connections, the Multi-Scale Semantic Reorganization Module (MSRM) reorganizes multi-stage encoder features through a fusion–separation–refusion process to achieve cross-scale semantic alignment. At the bottleneck stage, the Scale-Aware Module (SAM) constructs predefined receptive-field branches and adaptively weights their contextual responses to enhance scale-relevant semantic guidance. During decoder reconstruction, the Channel-Spatial Shuffle Mamba Block (CSSMBlock) promotes cross-channel information exchange and long-range spatial dependency modeling to improve structural detail recovery. Through this unified feature-processing framework, CoMS-UNet strengthens semantic consistency, scale-aware context aggregation, and channel-aware decoder reconstruction. Experiments on the ISPRS Vaihingen dataset and the labeled LoveDA validation set show that CoMS-UNet achieves a mean mIoU of 86.11 ± 0.35 % over three independent runs on Vaihingen and an mIoU of 52.48% on LoveDA.
Keywords:
remote sensing semantic segmentation
high-resolution remote sensing imagery
multi-scale semantic reorganization
scale-aware learning
land-cover interpretation
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