摘要
高分辨率遥感图像建筑物提取对城市实景三维重建具有重要意义。针对传统卷积方法分割复杂背景遥感图像中的建筑物时出现的地物互相遮挡和边界模糊导致分割精度不高问题,提出一种改进HRNet的遥感图像建筑物提取网络模型(SCGAN)。基于HRNet结构引入形状修正单元提升模型对建筑物边缘和形状的感知能力,结合对抗学习策略强化建筑物边界和几何形状等细节特征。实验结果表明,基于对抗学习和形状修正单元的SCGAN有效提升了分割精度,在WHU和Massachusetts数据集上的IoU分别为90.94%和70.89%,与流行的语义分割模型相比表现最佳。
The extraction of buildings from high-resolution remote sensing images is of great significance in three-dimensional reconstruction of urban scenes.A remote sensing image building extraction shape correction generative adversarial network(SCGAN)with improved high-resolution network(HRNet)is proposed to address the problem of low segmentation accuracy caused by mutual occlusion and blurred boundaries of buildings in complex background remote sensing images when using traditional convolutional methods.Based on the HRNet structure,shape correction units are introduced to enhance the model s perception of building edges and shapes,and adversarial learning strategies are used to strengthen detailed features such as building boundaries and geometric shapes.The experimental results show that the SCGAN model based on adversarial learning and shape correction units effectively improves segmentation accuracy in building extraction,with IoU of 90.94%and 70.89%on the WHU and Massachusetts datasets,respectively,exhibiting the best performance compared to popular semantic segmentation models.
作者
王若兰
李辉
WANG Ruolan;LI Hui(National Key Laboratory of Fundamental Science on Synthetic Vision,Sichuan University,Chengdu 610064,CHN)
出处
《半导体光电》
北大核心
2024年第6期1031-1038,共8页
Semiconductor Optoelectronics
基金
国家自然科学基金项目(U20A20161)。
关键词
神经网络
语义分割
遥感图像
建筑物提取
对抗学习
neural network
semantic segmentation
remote sensing images
building extraction
adversarial learning
作者简介
王若兰(1997-),女,重庆市人,硕士研究生,主要研究领域为遥感图像处理;通信作者:李辉(1970-),男,四川省人,硕士生导师,教授,主要研究领域为智能计算和虚拟现实。
