面向复杂自然场景的遥感地学分区智能解译框架及初探  

A Remote Sensing Intelligent Interpretation Framework Through Geo-Science Zoning for Complex Nature Scenes and Its Preliminary Experiments

作　　者：王志华[1,2] 杨晓梅 张俊瑶[1,2] 刘晓亮 李连发 董文 贺伟[1,2] WANG Zhihua;YANG Xiaomei;ZHANG Junyao;LIU Xiaoliang;LI Lianfa;DONG Wen;HE Wei(State Key Laboratory of Resources and Environmental 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;State Key Laboratory of Remote Sensing Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China)

机构地区：[1]中国科学院地理科学与资源研究所地理信息科学与技术全国重点实验室,北京100101 [2]中国科学院大学,北京100049 [3]中国科学院空天信息创新研究院遥感科学国家重点实验室,北京100094

出　　处：《地球信息科学学报》2025年第2期305-330,共26页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2021YFB3900501);国家自然科学基金项目(42371473)。

摘　　要：【目的】当下,面向多圈层耦合、人类干扰强烈的复杂自然场景遥感智能解译在地学研究和实际业务中常存在不好用的问题。为此,本文从遥感地学认知原理角度出发,在明晰遥感智能解译的使命是依托遥感大数据更好地辅助建立数字地球之后,认为达成一致的知识表征模型是解决问题的关键,进而提出遥感解译与地学认知应该耦合为一个系统,以实现“数据获取知识”与“知识引导数据”的双向驱动。【分析】在此基础上,提出以遥感地学分区为纽带的智能解译框架,以打通已有地学知识向遥感智能解译过程的输入与引导,增加解译结果与已有地学知识体系的匹配度。该框架主要依靠定量化的场景复杂性度量和地理分区知识耦合,形成面向遥感智能解译的地学分区方法以及分区样本抽样与规范,从而实现面向大区域的知识耦合下分区解译策略。【展望】通过复杂度与优化抽样实验、影像分区分割尺度优选、耕地类型细分等实验,初步揭示了本框架思路在优选样本、影像分割、耕地精细类型识别等遥感智能解译多方面均存在巨大潜力。[Objectives]Remote Sensing Intelligent Interpretation(RSII)often encounters challenges when applied for practical resource and environmental management,especially for complex scenes.To address this,we start from the explanation of why remote sensing interpretation is needed,and clarify that the mission of RSII is to achieve more rapid interpretation to build the digital twin earth with lower cost compared to manual interpretation.However,most RSII systems operate as a unidirectional process from remote sensing data to geoscience knowledge,lacking the feedback from knowledge to data.As a result,remote sensing information extracted from data often mismatch the knowledge of existing geoscience,creating a trust crisis between RSII researchers and geoscience researchers.And the crisis becomes more severe with the uncertainty of remote sensing information.[Analysis]We believe that an agreed upon representation model of geoscience knowledge between RSII researchers and geoscience researchers is necessary to alleviate the crisis.Based on this analysis,we propose a framework using geo-science zoning as the bridge to connect RSII researchers and geoscience researchers.In this framework,knowledge from geoscience could be transferred into the RSII system through geo-science zoning so that the interpretation results could be more coincided with geoscience knowledge.The framework mainly relies on(a)the scene complexity measurement,(b)the knowledge coupling of geographic regions to form the geological zoning method for remote sensing intelligent interpretation,and(c)the sampling specification of regional samples.The scene complexity measurement provides quantitative features for geoscience zoning and sampling weights assignment.Existing zoning data,such as ecological zoning data,geographic elements,and multisource remote sensing images are the main data inputs for geoscience zoning.The main principles for constructing zoning methods include(a)the geoscience elements type,(b)the scale of geoscience zoning,and(c)the process of in

关 键 词：遥感大数据 数字地球 遥感智能解译 信息提取 地理分区/区划 土地利用/覆被分类 复杂自然场景 场景分类 地学知识图谱 

分 类 号：P237[天文地球—摄影测量与遥感]