无线电工程2024，Vol.54Issue(7)：1694-1701,8.DOI:10.3969/j.issn.1003-3106.2024.07.012
基于多尺度指导的遥感影像建筑物提取网络
Building Extraction Network Based on Multi-scale Guidance in Remote Sensing Images
摘要
Abstract
Extracting buildings from remote sensing images is a fundamental task in the field of computer vision.In recent years,deep learning based methods have become the mainstream method for automatically extracting buildings from remote sensing images.Due to the complex structure and diverse scale of buildings,accurately and efficiently extracting buildings from remote sensing images remains a challenge.A remote sensing image building extraction network based on multi-scale guidance is proposed to address the issue of the inability to simultaneously consider small and large buildings during the extraction process due to the diversity of building scales.This network extracts small-scale,large-scale and other scale features through four paths,and the features are then guided and optimzed by an interaction-based scale guidance module and a Selective Kernel(SK)convolution module,respectively.Finally,the features extracted from different paths are fused to predict building information.The effectiveness of the proposed method is evaluated on the WHU dataset and the inria dataset respectively.Comparative experimental results show that the Intersection over Union(IoU)of the proposed method on the WHU dataset is 2.37％,1.48％,1.05％,0.83％and 0.59％higher than SegNet,ENet,MMB-Net,Refine-Net and MAP-Net respectively.In the inria dataset,the IoU is 3.65％,4.93％,2.42％,1.82％and 1.21％higher than other networks respectively.The results show that the proposed method is an effective,more complete,and robust object extraction method.关键词
深度学习/遥感影像/建筑物提取/多尺度指导Key words
deep learning/remote sensing images/building extraction/multi-scale guidance分类
计算机与自动化引用本文复制引用
宋宝贵,石卫超,余快..基于多尺度指导的遥感影像建筑物提取网络[J].无线电工程,2024,54(7):1694-1701,8.基金项目
国家自然科学基金青年项目(41901341)National Natural Science Foundation of China(Youth Program)(41901341) （41901341）
