Semantic segmentation of remote sensing image based on Contextual U-Net

Xiaoyan Shao; Yuxi Qiang; Jie Li; Lingling Li; Xuanzhuan Zhao; Quanfu Wang

doi:10.1117/12.2692004

23 August 2023 Semantic segmentation of remote sensing image based on Contextual U-Net

Xiaoyan Shao, Yuxi Qiang, Jie Li, Lingling Li, Xuanzhuan Zhao, Quanfu Wang

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 127841K (2023) https://doi.org/10.1117/12.2692004
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Semantic segmentation of remote sensing images is a challenging and critical task. The complexity of the remote sensing environment often poses difficulties in accurately capturing object boundaries. To address this challenge, we propose a Contextual U-Net (CU-Net) architecture for semantic segmentation of remote sensing images, which incorporates three collaborative improvements. Firstly, a Boundary Feature Extraction (BFE) module is introduced to fuse semantic feature information from the backbone network with boundary feature information, thereby enhancing the accuracy of edge segmentation in remote sensing images. Secondly, we propose an Adaptive Feature Selection (AFS) module that highlights representative semantic channels for irregular objects, enabling long-distance dependence capture between pixels in the irregular region of the boundary and pixels inside the objects. Thirdly, a Recursive Feature Fusion (RFF) module is introduced to effectively aggregate hierarchical features through adaptive inter-layer feature guidance, facilitating accurate capture of image edges and textures. We collected high-quality remote sensing data through UAVs, comprising 4509 images across 6 different categories. Extensive experiments demonstrate that the proposed CU-Net architecture outperforms state-of-the-art methods.

Citation Download Citation

Xiaoyan Shao, Yuxi Qiang, Jie Li, Lingling Li, Xuanzhuan Zhao, and Quanfu Wang "Semantic segmentation of remote sensing image based on Contextual U-Net", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 127841K (23 August 2023); https://doi.org/10.1117/12.2692004

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