A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images
Ming Hao, Shilin Chen, Huijing Lin, Zhang Hua, Nanshan Zheng
Abstract
Abstract There are problems such as poor interpretability and insufficient generalization ability when extracting buildings from high-resolution remote sensing images based on deep learning. This paper proposes a building extraction model called BPKG-SegFormer (Building Prior Knowledge Guided SegFormer) that combines prior knowledge of buildings with data-driven methods. This model constructs a building feature attention module and utilizes the multi-task loss function to optimize the extraction of buildings. Experimental results show that on the WHU building dataset, the proposed model outperforms UNet, Deeplabv3 + , and SegFormer models with OA, P, R, and MIoU of 96.63%, 95.94%, 94.76%, and 90.6%, respectively. The BPKG-SegFormer model extracts buildings with more regular shapes and flatter edges, reducing internal voids and increasing the number of correctly detected buildings.