Multi-Level Perceptual Network for Urban Building Extraction from High-Resolution Remote Sensing Images

Building extraction from high-resolution remote sensing images benefits various practical applications. However, automation of this process is challenging due to the variety of building surface coverings, complex spatial layouts, different types of structures, and tree occlusion. In this study, we propose a multilayer perception network for building extraction from high-resolution remote sensing images. By constructing parallel networks at different levels, the proposed network retains spatial information of varying feature resolutions and uses the parsing module to perceive the prominent features of buildings, thus enhancing the model's parsing ability to target scale changes and complex urban scenes. Further, a structure-guided loss function is constructed to optimize building extraction edges. Experiments on multi-source remote sensing data sets show that our proposed multi-level perception network presents a superior performance in building extraction tasks.