地理空间信息2024，Vol.22Issue(6)：25-28,4.DOI:10.3969/j.issn.1672-4623.2024.06.006
D-Net:一种动态模拟道路形态的路网提取模型
D-Net:A Road Network Extraction Model for Dynamical Simulation of Road Morphology
摘要
Abstract
In the current road extraction from remote sensing images using deep learning techniques,the issue arises due to the use of rectangular convolutions in the network,leading to difficulties in capturing road morphology features and low extraction accuracy.We proposed a convolu-tional neural network based on deformable convolution(D-Net),aiming to accurately extract road areas from remote sensing images.To validate the model's performance,we conducted experiments on the road datasets of Massachusetts and DeepGlobe.Additionally,we chose a GF-2 satel-lite image to evaluate the model's generalization ability.Experimental results demonstrate that D-Net significantly improves road segmentation accuracy,providing strong support for optimizing extraction results through deformable convolutions.The progress of this research holds signifi-cant theoretical and practical implications for further exploration in intelligent road extraction.关键词
形变卷积/深度学习/遥感技术/道路提取Key words
deformable convolution/deep learning/remote sensing technology/road extraction分类
天文与地球科学引用本文复制引用
高玉慧,朱武,张锐煊,王越..D-Net:一种动态模拟道路形态的路网提取模型[J].地理空间信息,2024,22(6):25-28,4.基金项目
国家重点研发计划项目(2020YFC1512000) （2020YFC1512000）
陕西省杰出青年基金项目(2023-JC-JQ-24). （2023-JC-JQ-24）
