SEMU-Net: A Structure-Enhanced Multi-Branch U-Shaped Network for High-Resolution Remote Sensing Land-Cover Segmentation.

Authors

Bingyan Lu, Mei Li, Xiaorong Xue, Wen Zhang, Xin Zhao, Jingtong Yang, Yishuo Tian, Wancheng Wang

Published in

Sensors (Basel, Switzerland). Volume 26. Issue 16. Aug 20, 2026. Epub Aug 20, 2026.

Abstract

High-resolution remote sensing semantic segmentation remains challenging because repeated downsampling progressively weakens the fine-grained spatial information of small objects, while direct fusion of heterogeneous multi-scale features may introduce semantic discrepancies and redundant background responses. To address these issues, this study proposes SEMU-Net, a structure-enhanced multi-branch U-shaped network. First, an independent multi-scale complementary branch is constructed outside the main encoder pathway to provide auxiliary hierarchical representations and compensate for information degradation during progressive semantic abstraction. Second, a scale-consistent feature embedding module is introduced to project and normalize side-branch features before residual injection, thereby improving the compatibility of cross-path feature representations. Third, a discriminative channel modulation module is incorporated into the decoder to adaptively strengthen task-relevant channel responses and suppress redundant background activations. Experiments were conducted on the ISPRS Vaihingen dataset and a self-annotated high-resolution remote sensing dataset. On the Vaihingen dataset, SEMU-Net achieved a mIoU of 72.21% and an Average F1 score of 83.64%, outperforming the strongest competing method by 0.59 and 0.44 percentage points, respectively. The IoU of the Car class increased by 3.50 percentage points. On the self-annotated dataset, the IoU of the narrow Road class improved by 4.12 percentage points. These results demonstrate that SEMU-Net improves overall segmentation accuracy and enhances the recognition of small objects, with the observed improvements being consistent with the design objectives of multi-scale information compensation, cross-path feature adaptation, and channel recalibration.

PMID:
42655598
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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