摘要
建筑物提取作为遥感影像处理领域备受关注的研究方向之一,对于城市规划、灾害管理、智慧城市建设等方面具有重要意义。近年来,随着遥感技术的不断突破和深度学习算法的迅速发展,卷积神经网络凭借强大的特征提取能力成为从遥感影像中提取建筑物的新兴解决方案。本文对基于卷积神经网络的建筑物提取方法进行系统总结,并将相关文献的方法针对模型结构、多尺度特征差异性、边界信息缺失以及模型复杂度的优化策略进行归纳分析。随后,我们阐述了典型的建筑物数据集以及当前数据集存在的问题,并根据数据集上的实验结果对相关方法的精度及参数量进行详细分析,旨在帮助读者更好地理解各种方法的性能和适用范围。最后,立足于领域的研究现状,面向人工智能高质量发展的新时代,从Transformer与CNN的结合、深度学习与强化学习的结合、跨模态数据融合、无监督或半监督学习方法、基于大规模遥感模型的实时提取、建筑物实例分割和建筑物轮廓矢量提取等方面对建筑物提取的未来研究方向进行了展望。
Building extraction is one of the important research directions that has attracted great attention in the field of remote sensing image processing.It refers to the process of accurately extracting building information such as the location and shape of buildings by analyzing and processing remote sensing images.This technology plays an irreplaceable and important role in urban planning,disaster management,map production,smart city construction,and other fields.In recent years,with the advancement of science and technology,especially the continuous evolution of earth observation technology and the rapid development of deep learning algorithms,Convolutional Neural Networks(CNNs)have become an emerging solution for extracting buildings from remote sensing images because of their powerful feature extraction capability.The aim of this paper is to provide a comprehensive and systematic overview and analysis of building extraction methods based on convolutional neural networks.We conduct a comprehensive literature review to summarize the building extraction methods from perspectives of model structure,multi-scale feature differences,lack of boundary information,and model complexity.This will help researchers to better understand the advantages and disadvantages of different methods and the applicable scenarios.In addition,several typical building datasets in this field are described in detail,as well as the potential issues associated with these datasets.Subsequently,by collecting experimental results of relevant algorithms on these typical datasets,a detailed discussion on the accuracy and parameter quantities of various methods is conducted,aiming to provide a comprehensive assessment of performance and applicability of these methods.Finally,based on the current research status of this field and looking forward to the new era of high-quality development in artificial intelligence,the future directions for building extraction are prospected.Specifically,this paper discusses the combination of Transformers and CNNs,the c
作者
杨明旺
赵丽科
叶林峰
蒋华伟
杨震
YANG Mingwang;ZHAO Like;YE Linfeng;JIANG Huawei;YANG Zhen(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)
出处
《地球信息科学学报》
EI
CSCD
北大核心
2024年第6期1500-1516,共17页
Journal of Geo-information Science
基金
国家自然科学基金项目(41901276、41901265)
河南省科技攻关项目(232102320348、232102321057)
河南工业大学自科创新基金(2021ZKCJ18)
河南工业大学青年骨干教师培育计划(21420198)。
关键词
卷积神经网络
遥感影像
建筑物提取
深度学习
非对称网络结构
多尺度特征融合
边界优化
轻量化网络结构
convolutional neural network
remote sensing image
building extraction
deep learning
asymmetric network structure
multi-scale feature fusion
boundary optimization
lightweight network structure
