遥感影像地学分析的地理学原理及等级斑块建模框架  

Geographical principles of remote sensing image analysis and the hierarchical patch model based analysis framework

作　　者：王志华[1,2] 杨晓梅 刘岳明[1,2] 刘彬 张俊瑶 刘晓亮 孟丹 郜酷 曾晓伟 丁亚新 WANG Zhihua;YANG Xiaomei;LIU Yueming;LIU Bin;ZHANG Junyao;LIU Xiaoliang;MENG Dan;GAO Ku;ZENG Xiaowei;DING Yaxin(State Key Laboratory of Resources and Environment Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Geography and Information Engineering,China University of Geosciences,Wuhan 102405,China;School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [2]中国科学院大学,北京100049 [3]中国地质大学(武汉)地理与信息工程学院,武汉102405 [4]河南理工大学测绘与土地信息工程学院,焦作454000

出　　处：《遥感学报》2024年第6期1412-1424,共13页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家重点研发计划(编号:2021YFB3900501);国家自然科学基金(编号:41890854,41901354);资源与环境信息系统国家重点实验室自主创新项目(编号:KPI001,YPI004)。

摘　　要：单纯借鉴图像处理、计算机视觉技术,难以从根本上解决遥感地学分析问题。为此,以面向对象影像分析范式为例,剖析区域、尺度、格局与功能的地理学原理,将遥感影像地学分析视为基于遥感影像信息的精细尺度下地理空间区域划分、等级结构表达、空间结构推断空间功能的过程。在此基础上,提出基于等级斑块建模的循环迭代式遥感影像地学分析框架。该框架,首先由遥感影像与其它地理信息数据和知识共同构建能够表达地理空间的等级斑块模型;然后,依托等级斑块模型的上下层次关系和遥感影像特征,协同实现对象的精准识别;进而,使用精准识别结果更新等级斑块模型,提供更为精准的上下层次关系特征,从而实现后续迭代过程中更高精度的识别。此外,还提出了实现该框架的10条建议,例如为不同地理要素构建不同的等级斑块模型。该框架提供了一种地学知识引导下的遥感大数据智能解译思路,有望实现地学知识自动积累更新和遥感智能解译精度提升的协同互促。In the past two decades,Geographical Object-Based Image Analysis(GEOBIA)has been widely studied and applied;however,it still does not meet the expectation for big remote sensing image analysis in geographical cognition activities in terms of accuracy and intelligence.We think that the major problem is the lack of geographical thoughts to lead the research and development(R&D)of GEOBIA key techniques,especially when introducing the techniques of computer vision,which does not regard comprehending the earth’s surface as the objective.On this basis,we review the concepts of GEOBIA from a geographical perspective,specifically the principles of region,scale,and pattern and function.From the region principle,we regard the image segmentation in GEOBIA grouping the spatial neighbor pixels sharing similar spectral and textures as the representation of a fine-scale geographical zoning in remote sensing image spaces.From the scale principle,we regard the multiscale of segmentation as the representation model quantifying the relationship of geographical zones among different scales.From the pattern and function principle,we regard the multiscale segmentation as an ideal hierarchical patch model representing the earth surface structure,i.e.,the landscape,and could quantify the pattern(e.g.,orientation,shape,arrangement,distance,etc.)for the function recognition.In other words,we think that the target of GEOBIA is to recover the hierarchical multiscale structure of the earth’s surface from the remote sensing images so that we can quantify the structure and then recognize and comprehend its function.On the basis of these reviews,we propose an iterative GEOBIA framework where the core is constructing a hierarchical patch model of the earth’s surface.The framework starts with fusing the big geographical data,including remote sensing images,existing geographical thematic maps,and other helpful knowledge,to construct an initial hierarchical patch model of the earth’s surface.Then,object features are extracted from the hiera

关 键 词：地理信息科学 遥感地学分析 面向对象分类 遥感智能解译 面向地理对象影像分析 地学知识图谱 对地观测 格局 尺度 区域 等级 斑块 遥感大数据 

分 类 号：P2[天文地球—测绘科学与技术]