摘要
【意义】制图级矢量要素提取是遥感智能解译可直接应用于真实场景的关键前提。【分析】尽管遥感观测技术和深度学习在遥感影像解译中取得了显著进步,但生产满足业务需求的矢量要素仍依赖大量人工目视解译和人机交互后处理。【进展】本文基于公众测绘产品生产等业务场景的实际数据需求,深入分析了大量业务场景中遥感影像解译的不同地物矢量要素的规则约束,初步定义了能够直接满足行业需求的“制图级矢量要素”。围绕该定义,从矢量类型,地物形状,边界定位,面积、长度、宽度和角度大小,拓扑约束以及邻接约束这9个维度对制图级矢量要素规则集内容进行了归纳和分析,并从类别属性、位置准确性、拓扑准确性以及综合取舍合理性4个方面梳理了制图级矢量要素的评价方法。随后,重点回顾了基于深度学习提取矢量要素的分割后处理、迭代式和并行式3类方法,分析它们的基本思路、提取矢量的特点与精度、灵活性以及计算效率等方面的优劣与异同,概括了当前面向制图级矢量要素遥感智能解译方法在制图级解译能力、制图级规则耦合以及遥感可解译性方面的不足。【展望】最后,从构建广泛且开放的制图规则集、构建并共享制图级矢量要素样本集、发展面向多要素的制图级矢量要素提取框架、探索多模态耦合语义规则潜力等方面对制图级矢量要素智能解译的未来研究方向进行了展望。
[Significance]The extraction of Cartographic-Level Vector Elements(CLVE)is a critical prerequisite for the direct application of remote sensing image intelligent interpretation in real-world scenarios.[Analysis]In recent years,the continuous rapid advancement of remote sensing observation technology has provided a rich data foundation for fields such as natural resource surveying,monitoring,and public surveying and mapping data production.However,due to the limitations of intelligent interpretation algorithms,obtaining the necessary vector elements data for operational scenarios still heavily relies on manual visual interpretation and human-computer interactive post-processing.Although significant progress has been made in remote sensing image interpretation using deep learning techniques,producing vector data that are directly usable in operational scenarios remains a major challenge.[Progress]This paper,based on the actual data needs of operational scenarios such as public surveying and mapping data production,conducts an in-depth analysis of the rule constraints for different vector elements in remote sensing image interpretation across a wide range of operational contexts.It preliminarily defines"cartographic-level vector elements"as vector element data that complies with certain cartographic standard constraints at a specific scale.Centered on this definition,the content of the rule set for CLVE is summarized and analyzed from nine dimensions,including vector types,object shapes,boundary positioning,area,length,width,angle size,topological constraints,and adjacency constraints.Evaluation methods for CLVE are then outlined in four aspects:class attributes,positional accuracy,topological accuracy,and rationality of generalization and compromise.Subsequently,through literature collection and statistical analysis,it was observed that research on deep learning-based vector extraction,while still in its early stages,has shown a rapid upward trend year by year,indicating increasing attention in the field.The paper then systematically reviews three major methodological frameworks for deep learning-based vector extraction:semantic segmentation&post-processing,iterative methods,and parallel methods.A detailed analysis is provided on their basic principles,characteristics and accuracy of vector extraction,flexibility,and computational efficiency,highlighting their respective strengths,weaknesses,and differences.The paper also summarizes the current limitations of remote sensing intelligent interpretation methods aimed at CLVE in terms of cartographic-level interpretation capabilities,rule coupling,and remote sensing interpretability.[Prospect]Finally,future research directions for intelligent interpretation of CLVE are explored from several perspectives,including the construction of broad and open cartographic-level rule sets,the development and sharing of CLVE datasets,the advancement of multi-element CLVE extraction frameworks,and the exploration of the potential of multimodal coupled semantic rules.
作者
刘帝佑
孔赟珑
陈静波
王晨昊
孟瑜
邓利高
邓毓弸
张正
宋柯
王志华
初启凤
LIU Diyou;KONG Yunlong;CHEN Jingbo;WANG Chenhao;MENG Yu;DENG Ligao;DENG Yupeng;ZHANG Zheng;SONG Ke;WANG Zhihua;CHU Qifeng(National Engineering Research Center for Geomatics,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 101408,China;State Key Laboratory of Resources and Environment Information System,Institute of Geographic Science and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Heilongjiang Geographic Information Engineering Institute,Harbin 150086,China)
出处
《地球信息科学学报》
2025年第2期285-304,共20页
Journal of Geo-information Science
基金
国家重点研发计划项目(2021YFB3900503)
中国科学院空天信息创新研究院科学与颠覆性技术项目(E3Z21102)。
关键词
矢量要素提取
制图级矢量
遥感影像
深度学习
制图级规则集
规则知识耦合
遥感智能解译
vector elements extraction
cartographic-level vector data
remote sensing image
deep learning
cartographic-level rule
knowledge coupling
intelligent extraction
