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首页 > 过刊浏览>2023年第51卷第7期 >1025-1032. DOI:10.11908/j.issn.0253-374x.23145
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遥感智能信息处理的发展及技术前景
CSTR:
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作者:
作者单位:

1.中国科学院 地理科学与资源研究所,北京 100101;2.中国科学院 资源与环境信息系统国家重点实验室,北京 100101;3.中国科学院大学,北京 101408

作者简介:

杨晓梅（1970—），女，研究员，博士生导师，理学博士，主要研究方向为遥感影像地学理解与智能计算。 E-mail： yangxm@lreis.ac.cn

中图分类号:

P237

基金项目:

国家重点研发计划（2021YFB3900501）


Development and Technical Prospect of Remote Sensing Intelligent Information Processing
Author:
  • YANG Xiaomei 1,2,3

    YANG Xiaomei

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;University of Chinese Academy of Sciences， Beijing 101408， China
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  • WANG Zhihua 1,2,3

    WANG Zhihua

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;University of Chinese Academy of Sciences， Beijing 101408， China
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  • LIU Yueming 1,2

    LIU Yueming

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China
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  • ZHANG Junyao 1,2,3

    ZHANG Junyao

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;University of Chinese Academy of Sciences， Beijing 101408， China
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  • LIU Xiaoliang 1,2,3

    LIU Xiaoliang

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;University of Chinese Academy of Sciences， Beijing 101408， China
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  • LIU Bin 1,2,3

    LIU Bin

    Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;University of Chinese Academy of Sciences， Beijing 101408， China
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Affiliation:

1.Institute of Geographic Sciences and Natural Resources Research， Chinese Academy of Sciences， Beijing 100101， China;2.State Key Laboratory of Resources and Environmental Information System， Chinese Academy of Sciences，Beijing 100101， China;3.University of Chinese Academy of Sciences， Beijing 101408， China

  • 摘要
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  • 参考文献 [41]
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    摘要:

    遥感信息提取技术虽不断推陈出新，但在智能化、精准实用性方面始终存在巨大的瓶颈问题，有必要围绕遥感智能计算和信息提取这个发展主题进行总结和讨论。从“机理—尺度—数据—智能”4个层面，逐步就遥感信息提取与定量反演路径的发展融合、基于像素和面向对象不同处理单元模式、时空谱数据融合、遥感解译的智能化因素四方面进行剖析，从而提出未来“数据获取知识”和“知识引导数据”双向驱动、遥感大数据和地学知识图谱相融合的遥感智能计算架构，尝试推动遥感科学从经典向现代化的跃迁。

    Abstract:

    Although the remote sensing information extraction technology is constantly being innovated， there are still huge bottlenecks in terms of intelligence， precision， and practicality. Therefore， it is necessary to conduct a comprehensive summary and discussion on the development topic of remote sensing intelligent computing and information extraction. From the four levels of “mechanism-scale-data-intelligence”， this paper gradually discusses the development and fusion of remote sensing information extraction and quantitative inversion paths， the different processing unit modes based on pixels and object-oriented， the spatial-temporal spectral data fusion， the intelligent factors of remote sensing interpretation. It proposes a future-oriented remote sensing intelligent computing architecture which is driven by two-way “data acquisition knowledge” and “knowledge-guided data”， and integrates remote sensing big data and geoscience knowledge maps， trying to promote the transition of remote sensing science from classics to modernization.

    参考文献
    [1] 周成虎, 王华, 王成善, 等. 大数据时代的地学知识图谱研究[J]. 中国科学:地球科学, 2021, 51(7): 1070.ZHOU Chenghu, WANG Hua, WANG Chengshan, et al. Geoscience knowledge graph in the big data era [J]. Science China Earth Sciences, 2021, 51(7): 1070.
    [2] GUO H, WANG L, LIANG D. Big earth data from space: A new engine for Earth science [J]. Science Bulletin, 2016, 61(7): 505.
    [3] LARY D J, ALAVI A H, GANDOMI A H, et al. Machine learning in geosciences and remote sensing [J]. Geoscience Frontiers, 2016, 7(1): 3.
    [4] 王志华,杨晓梅,周成虎.面向遥感大数据的地学知识图谱构想[J]. 地球信息科学学报, 2021, 23(1): 16.WANG Zhihua, YANG Xiaomei, ZHOU Chenghu. Geographic knowledge graph for remote sensing big data [J]. Journal of Geo-information Science, 2021, 23(1): 16.
    [5] 张良培, 沈焕锋. 遥感数据融合的进展与前瞻[J]. 遥感学报, 2016, 20(5): 1050.ZHANG Liangpei, SHEN Huanfeng. Progress and future of remote sensing data fusion [J]. Journal of Remote Sensing, 2016, 20(5): 1050.
    [6] 梁顺林, 白瑞, 陈晓娜, 等. 2019年中国陆表定量遥感发展综述[J]. 遥感学报, 2020, 24(6): 618.LIANG Shunlin, BAI Rui, CHEN Xiaona, et al. Review of China's land surface quantitative remote sensing development in 2019 [J]. Journal of Remote Sensing, 2020, 24(6): 618.
    [7] PAUDEL D, BOOGAARD H, WIT A D, et al. Machine learning for large-scale crop yield forecasting[J]. Agricultural Systems, 2021 (187): 103016.
    [8] AVAND M, MORADI H R, RAMAZANZADEH M. Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability [J]. Journal of Hydrology, 2021 (595): 125663.
    [9] MA L, LIU Y, ZHANG X, et al. Deep learning in remote sensing applications: A meta-analysis and review [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152: 166.
    [10] 吴炳方, 邢强. 遥感的科学推动作用与重点应用领域[J]. 地球科学进展, 2015, 30(7): 751.WU Bingfang, XING Qiang. Remote sensing roles on driving science and major applications [J]. Advances in Earth Science, 2015, 30(7): 751.
    [11] 骆剑承, 吴田军, 吴志峰, 等.地理图斑智能计算及模式挖掘方法研究[J]. 地球信息科学学报, 2020, 22(1): 57.LUO Jiancheng, WU Tianjun, WU Zhifeng, et al. Methods of intelligent computation and pattern mining based on geo-parcels [J]. Journal of Geo-information Science, 2020, 22(1): 57.
    [12] BLASCHKE T. Object based image analysis for remote sensing [J]. Isprs Journal of Photogrammetry and Remote Sensing, 2010, 65, 2.
    [13] BURNETT C, BLASCHKE T. A multi-scale segmentation/object relationship modelling methodology for landscape analysis [J]. Ecological modelling, 2003, 168(3), 233.
    [14] CHEN B, XU B, GONG P. Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities [J]. Big Earth Data, 2021, 5(3): 1.
    [15] STRAHLER A H, WOODCOCK C E, SMITH J A. On the nature of models in remote sensing [J]. Remote Sensing of Environment, 1986, 20(2), 121.
    [16] KUCHARCZYK M, HAY G J, GHAFFARIAN S, et al. Geographic object-based image analysis: A primer and future directions [J]. Remote Sensing, 2020, 12(12): 2012.
    [17] ZHANG X, DU S, MING D. Segmentation scale selection in geographic object-based image analysis [J]. High Spatial Resolution Remote Sensing, 2018, 27: 201.
    [18] WOODCOCK C E, STRAHLER A H. The factor of scale in remote-sensing [J]. Remote Sensing of Environment, 1987, 21(3): 311.
    [19] DR?GU? L, CSILLIK O, EISANK C, et al. Automated parameterisation for multi-scale image segmentation on multiple layers [J]. Isprs Journal of Photogrammetry and Remote Sensing, 2014, 88, 119.
    [20] WANG Z, LU C, YANG X. Exponentially sampling scale parameters for the efficient segmentation of remote sensing images [J]. International Journal of Remote Sensing, 2018, 39(6): 1628.
    [21] COSTA H, FOODY G M, BOYD D S. Supervised methods of image segmentation accuracy assessment in land cover mapping [J]. Remote Sensing of Environment, 2018, 205, 338.
    [22] GHAMISI P, RASTI B, YOKOYA N, et al. Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art [J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(1): 6.
    [23] SCHMITT M, ZHU X X. Data fusion and remote sensing: An ever-growing relationship [J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(4): 6.
    [24] reichstein MARKUS, camps-valls GUSTAU, stevens BJORN, et al. Deep learning and process understanding for data-driven Earth system science [J]. Nature, 2019, 566(7743): 195.
    [25] 张兵. 遥感大数据时代与智能信息提取[J]. 武汉大学学报:信息科学版, 2018, 43(12): 11.ZHANG Bing. Remotely sensed big data era and intelligent information extraction [J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 11.
    [26] 阎守邕, 刘亚岚, 魏成阶, 等. 遥感影像群判读理论与方法[M]. 北京: 海洋出版社, 2007.YAN Shouyong, LIU Yalan, WEI Chengjie, et al. Theory and Method of Remote Sensing Image Group Interpretation [M]. Beijing: China Ocean Press, 2007.
    [27] MARR D. Vision: A computational investigation into the human representation and processing of visual information [M]. [s.l.]: MIT Press, 2010.
    [28] 陈霖. 新一代人工智能的核心基础科学问题:认知和计算的关系[J]. 中国科学院院刊, 2018, 33(10): 1104.CHEN Lin. Core basic scientific issues of the new generation of artificial intelligence: the relationship between cognition and computation [J]. Bulletin of Chinese Academy of Sciences, 2018, 33(10): 1104.
    [29] 张兵, 杨晓梅, 高连如, 等. 遥感大数据智能解译的地理学认知模型与方法[J], 测绘学报, 2022, 51(7): 1398.ZHANG Bing, YANG Xiaomei, GAO Lianru, et al. Geo-cognitive models and methods for intelligent interpretation of remotely sensed big data [J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1398.
    [30] 朱建章, 石强, 陈凤娥, 等. 遥感大数据研究现状与发展趋势[J]. 中国图象图形学报, 2016, 21(11): 1425.ZHU Jianzhang, SHI Qiang, CHEN Feng’e, et al. Research status and development trends of remote sensing big data [J]. Journal of Image and Graphics, 2016, 21(11): 1425.
    [31] ZHONG Y, MA A, SOON Ong Y, et al. Computational intelligence in optical remote sensing image processing [J]. Applied Soft Computing, 2018, 64: 75.
    [32] ZHUANG Y, WU F, CHEN C, et al. Challenges and opportunities: From big data to knowledge in AI 2.0 [J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3.
    [33] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4): 379.LI Deren. Towards geo-spatial information science in big data era [J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4): 379.
    [34] MA Y, WU H, WANG L, et al. Remote sensing big data computing: Challenges and opportunities [J]. Future Generation Computer Systems, 2015, 51: 47.
    [35] 陈述彭, 岳天祥, 励惠国. 地学信息图谱研究及其应用[J]. 地理研究, 2000, 19(4): 337.CHEN Shupeng, YUE Tianxiang, LI Huiguo. Studies on geo-informatic tupu and its application [J]. Geographical Research, 2000, 19(4): 337.
    [36] SINGHAL A. 2012. Introducing the knowledge graph: Things, not strings [EB/OL]. [2012-05-16]. https://www.blog.google/products/search/introducing-knowledge-graph-things-not/.
    [37] WANG Q, MAO Z, WANG B, et al. Knowledge graph embedding: A survey of approaches and applications [J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724.
    [38] CHEN X, JIA S, XIANG Y. A review: Knowledge reasoning over knowledge graph [J]. Expert Systems with Applications, 2020, 141: 112948.
    [39] 陆锋, 余丽, 仇培元. 论地理知识图谱[J]. 地球信息科学学报, 2017, 19(6): 723.LU Feng, YU Li, QIU Peiyuan. On geographic knowledge graph [J]. Journal of Geo-information Science, 2017, 19(6): 723.
    [40] 许珺, 裴韬, 姚永慧. 地学知识图谱的定义、内涵和表达方式的探讨[J]. 地球信息科学学报, 2010, 12(4): 496.XU Jun, PEI Tao, YAO Yonghui. Conceptual framework and representation of geographic knowledge map [J]. Journal of Geo-information Science, 2010, 12(4): 496.
    [41] 王志华,杨晓梅,周成虎.面向遥感大数据的地学知识图谱构想[J]. 地球信息科学学报, 2021, 23(1): 16.WANG Zhihua, YANG Xiaomei, ZHOU Chenghu. Geographic knowledge graph for remote sensing big data [J]. Journal of Geo-information Science, 2021, 23(1): 16.
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杨晓梅,王志华,刘岳明,张俊瑶,刘晓亮,刘彬.遥感智能信息处理的发展及技术前景[J].同济大学学报(自然科学版),2023,51(7):1025~1032

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  • 收稿日期:2023-05-03
  • 在线发布日期: 2023-07-25
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