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BD-WNet: Boundary Decoupling based W-shape Network for Road Segmentation in Optical Remote Sensing Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Fan, Shilong Xie, Jianlin Zhuang, Yin Chen, He Su, Zhibao Li, Lianlin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing 100081 China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing 100072 China Peking University Schools of Computer Science and State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing 100871 China

Using very high-resolution optical remote sensing images for road segmentation is a challenging and important interpretation task. Different from other segmentation tasks, road segmentation typically faces unpredictable structure, irregular distribution, and complex background interference. Thus, establishing an effective and stable semantic description for road segmentation becomes a challenge. In this article, a novel architecture called Boundary Decoupling based W-shape Network (BD-WNet) is proposed for achieving road segmentation from optical remote sensing imagery. First, a novel W-shaped double encoder-decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. Second, the unstable semantic features within the initial stage of double encoder-decoder architecture are considered for road boundary description. For decoupling unstable boundary information from the output feature of first encoder-decoder, a boundary separate model is designed, which is called Boundary-Body Decoupling (BBD) module. This module utilizes the flow field mechanism to compare image features before and after passing through the encoder-decoder. The stable features among the overall features are decoupled into the main body of the road, while the dynamic features are decoupled into the boundary of the road. Third, a boundary and body weighting fusion model is also designed to fuse stable road body and unstable road boundary information for supervised learning. Extensive experiments have been carried out on remote sensing road segmentation datasets, and our method achieves impressive performance. Specifically, the proposed BD-WNet achieves 82.5% F1 score and 69.8 IoU% on DeepGlobe dataset, 75.5% F1 score and 60.7% IoU on CHN6-CUG dataset and 93.8% F1 score and 88.3% IoU on Ottawa Road Dataset. © 2008-2012 IEEE.

关键词： Boundary extraction double encoding-decoding remote sensing semantic segmentation

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