A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images.

Autor: Hao, Ming, Chen, Shilin, Lin, Huijing, Zhang, Hua, Zheng, Nanshan
Předmět:
Zdroj: Urban Informatics; 2/26/2024, Vol. 3 Issue 1, p1-8, 8p
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
Externí odkaz: