Color-Coated Steel Sheet Roof Building Extraction from External Environment of High-Speed Rail Based on High-Resolution Remote Sensing Images

Abstract

The identification of color-coated steel sheet (CCSS) roof buildings in the external environment is of great significance for the operational security of high-speed rail systems. While high-resolution remote sensing images offer an efficient approach to identify CCSS roof buildings, achieving accurate extraction is challenging due to the complex background in remote sensing images and the extensive scale range of CCSS roof buildings. This research introduces the deformation-aware feature enhancement and alignment network (DFEANet) to address these challenges. DFEANet adaptively adjusts the receptive field to effectively separate the foreground and background facilitated by the deformation-aware feature enhancement module (DFEM). Additionally, feature alignment and gated fusion module (FAGM) is proposed to refine boundaries and preserve structural details, which can ameliorate the misalignment between adjacent features and suppress redundant information during the fusion process. Experimental results on remote sensing images along the Beijing–Zhangjiakou high-speed railway demonstrate the effectiveness of DFEANet. Ablation studies further underscore the enhancement in extraction accuracy due to the proposed modules. Overall, the DFEANet was verified as capable of assisting in the external environment security of high-speed rails.