摘要
建筑物是城市建设的主要地物特征,是构成城市的基本要素之一,是城市化建设不断发展的重要体现,是人类生产和生活的主要场所,对其进行有效管理和监督是至关重要的。当前遥感影像获取能力提升且应用常态化,如何快速准确地提取建筑物为后续的应用提供基础是当前急需解决的问题。本文通过分析并结合当前深度学习等先进技术,提出了基于前景感知的遥感影像建筑物提取方法。首先通过改进ResNet网络提取基本影像特征;然后使用双向FPN网络获取金字塔特征图,并利用前景和地理空间场景建模,形成相关上下文关联;最后增强输入特征图,放大前景特征与背景特征的差距,以提高前景特征区分度,并最终实现高效、精准的遥感影像建筑物的自动化提取。
Building is the main feature of urban construction, is one of the basic elements of a city, is an important embodiment of the continuous development of urbanization, is the main place of human production and life. Therefore, how to effectively manage and supervise buildings is crucial. With the improvement of remote sensing image acquisition capability and widely spread applications, how to quickly and accurately extract buildings to provide a basis for subsequent applications become an urgent problem to be solved. In this paper, a method of building extraction by using remote sensing images based on foreground-aware is proposed by analyzing and combining advanced technologies such as depth learning. Firstly, the basic features are extracted by employing the improved ResNet from the input remote sensing image. Then, the pyramid feature map is obtained by using two-way FPN;the construction of relevant context association is achieved with the using of the foreground and geospatial scene modelling. After that, the input feature map is enhanced and the gap between foreground features and background features is enlarged, thus the foreground feature differentiation is improved. Finally, the efficient and accurate automatic extraction of buildings from remote sensing image is realized.
作者
施仲添
沈正伟
杨四海
SHI Zhongtian;SHEN Zhengwei;YANG Sihai(Hangzhou Planning and Natural Resuorces Survey and Monitoring Center,Hangzhou 310012,China;Deqing Institute of Advanced Technology and Industrialization,Zhejiang University,Huzhou 313200,China)
出处
《测绘通报》
CSCD
北大核心
2023年第2期134-138,共5页
Bulletin of Surveying and Mapping
基金
浙江省重点研发计划择优委托项目(2021C01017)。
关键词
前景感知
遥感影像
建筑物提取
卷积神经网络
ResNet网络结构
foreground perception
remote sensing image
building extraction
convolutional neural network
ResNet network structure
