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

  • role： First author第一作者
  • Affiliation：

    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

  • Email：zhwang@lreis.ac.cn
  • Introduction：王志华，研究方向为土地利用/土地覆被、遥感智能解译。E-mail： zhwang@lreis.ac.cn
WANG Zhihua12,  
  • role： Corresponding author通信作者
  • Affiliation：

    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

  • Email：yangxm@lreis.ac.cn
  • Introduction：杨晓梅，研究方向为遥感地学分析。E-mail：yangxm@lreis.ac.cn
YANG Xiaomei12*,  
  • Affiliation：

    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

LIU Yueming12,  
  • Affiliation：

    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

LIU Bin12,  
  • Affiliation：

    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

ZHANG Junyao12,  
  • Affiliation：

    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

LIU Xiaoliang12,  
  • Affiliation：

    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

MENG Dan12,  
  • Affiliation：

    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

GAO Ku12,  
  • Affiliation：

    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

ZENG Xiaowei123,  
  • Affiliation：

    State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

DING Yaxin14

Resümee

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 hierarchical patch model, and the function of these objects is recognized; the features include the internal features extracted from the object itself (e.g., shape and spectrum) and the external features extracted from its relationship with other objects (e.g., its neighbor objects, parent objects, and children objects). Finally, the recognized results are used to update the hierarchical patch model for the next recognition cycles. With the iteration of the remote sensing image analysis, the accuracy of geographical object recognition can be improved because we also have a more accurate hierarchical patch model describing the earth’s surface due to the updating process, which could provide an accurate calculation of the object’s features.To achieve the above proposed framework, we also propose a few suggestions for further R&D, such as constructing different hierarchical patch models for different geographical elements, fusing multiresolution images by using the hierarchical patch model instead of pixels, and choosing the suitable interpretation models instead of one model for different big geographical patches.We hope the above insights could provide an instructive idea of how to embed geographical knowledge into intelligent interpretation models to extract new knowledge from big remote sensing images with improved accuracy.

Schlüsselwort

Geographic Information Science (GIS);remote sensing geoscience analysis;object-based classification;remote sensing intelligent interpretation;Geographic Object-Based Image Analysis (GEOBIA);Geo-knowledge graphs;earth observation;pattern;scale;region;hierarchy;patch;remote sensing big data

References

  1. 1.
    Benz U C, Hofmann P, Willhauck G, Lingenfelder I and Heynen M. 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3/4): 239-258
  2. 2.
    Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16
  3. 3.
    Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R Q, Van der Meer F, Van der Werff H, Van Coillie F and Tiede D. 2014. Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180-191
  4. 4.
    Blaschke T, Lang S and Hay G J. 2008. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin: Springer
  5. 5.
    Burnett C and Blaschke T. 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3): 233-249
  6. 6.
    Chen Y H, Feng T, Shi P J and Wang J F. 2006. Classification of remot sensing image based on object oriented and class rules. Geomatics and Information Science of Wuhan University, 31(4): 316-320
  7. 7.
    Chen Y Y, Ming D P, Zhao L, Lv B R, Zhou K Q and Qing Y. 2018. Review on high spatial resolution remote sensing image segmentation evaluation. Photogrammetric Engineering and Remote Sensing, 84(10): 629-646
  8. 8.
    Costa H, Foody G M and Boyd D S. 2018. Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205: 338-351
  9. 9.
    Drǎguţ L, Tiede D and Levick S R. 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6): 859-871
  10. 10.
    Espindola G M, Câmara G, Reis I A, Bins L S and Monteiro A M. 2006. Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. International Journal of Remote Sensing, 27(14): 3035-3040
  11. 11.
    Fu B J, Chen L D, Ma K M and Wang Y L. 2011. Principles and Applications of Landscape Ecology. Beijing: Science Press
  12. 12.
    Fu B J, Liu G H, Chen L D, Ma K M and Li J R. 2001. Scheme of ecological regionalization in China. Acta Ecologica Sinica, 21(1): 1-6
  13. 13.
    Gong J Y. 2018. Chances and challenges for development of surveying and remote sensing in the age of artificial intelligence. Geomatics and Information Science of Wuhan University, 43(12): 1788-1796
  14. 14.
    Gong P. 2021. Intelligent mapping with remote sensing, iMap. National Remote Sensing Bulletin, 25(2): 527-529
  15. 15.
    Goodchild M F. 2004. The validity and usefulness of laws in geographic information science and geography. Annals of the Association of American Geographers, 94(2): 300-303
  16. 16.
    Gu H Y, Li H T, Yan L, Han Y S, Yu F, Yang Y and Liu Z J. 2018. A geographic object-based image analysis methodology based on geo-ontology. Geomatics and Information Science of Wuhan University, 43(1): 31-36
  17. 17.
    Haralick R M and Shapiro L G. 1985. Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29(1): 100-132
  18. 18.
    Hay G J and Castilla G. 2008. Geographic Object-Based Image Analysis (GEOBIA): a new name for a new discipline//Blaschke T, Lang S and Hay G J, eds. Object-Based Image Analysis. Berlin: Springer: 75-89
  19. 19.
    Hill R A. 1999. Image segmentation for humid tropical forest classification in Landsat TM data. International Journal of Remote Sensing, 20(5): 1039-1044
  20. 20.
    Hossain M D and Chen D M. 2019. Segmentation for Object-Based Image Analysis (OBIA): areview of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 115-134
  21. 21.
    Huang B, Wu B, Liu B and Cao K. 2008. Spatial intelligence: advancement of geographic information science. Journal of Remote Sensing (in Chinese), 12(5): 766-771
  22. 22.
    Huang X, Li J Y, Yang J, Zhang Z, Li D R and Liu X P. 2021. 30 m global impervious surface area dynamics and urban expansion pattern observed by Landsat satellites: from 1972 to 2019. Science China Earth Sciences, 64(11): 1922-1933
  23. 23.
    Johnson B and Xie Z X. 2011. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4): 473-483
  24. 24.
    Johnson B and Xie Z X. 2013. Classifying a high resolution image of an urban area using super-object information. ISPRS Journal of Photogrammetry and Remote Sensing, 83: 40-49
  25. 25.
    Lang S and Blaschke T. 2006. Bridging remote sensing and GIS–What are the main supportive pillars?//Proceedings of the 1st International Conference on Object-Based Image Analysis.
  26. 26.
    Li Y S and Zhang Y J. 2022. A new paradigm of remote sensing image interpretation by coupling knowledge graph and deep learning. Geomatics and Information Science of Wuhan University, 47(8): 1176-1190
  27. 27.
    Liu N W, Guo Y L and Zhang Z S. 2009. Comprehensive Physical Geography. 3rd ed. Beijing: Science Press
  28. 28.
    Lobo A. 1997. Image segmentation and discriminant analysis for the identification of land cover units in ecology. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1136-1145
  29. 29.
    Lu F, Zhu Y Q and Zhang X Y. 2023. Spatiotemporal knowledge graph: advances and perspectives. Journal of Geo-Information Science, 25(6): 1091-1105
  30. 30.
    Luo J C, Hu X D, Wu T J, Liu W, Xia L G, Yang H P, Sun Y W, Xu N, Zhang X, Shen Z F and Zhou N. 2021. Research on intelligent calculation model and method of precision land use/cover change information driven by high-resolution remote sensing. National Remote Sensing Bulletin, 25(7): 1351-1373
  31. 31.
    Luo J C, Wu T J, Wu Z F, Zhou Y N, Gao L J, Sun Y W, Wu W, Yang Y P, Hu X D, Zhang X and Shen Z F. 2020. Methods of intelligent computation and pattern mining based on geo-parcels. Journal of Geo-Information Science, 22(1): 57-75
  32. 32.
    Luo J C, Zhou C H, Shen Z F, Yang X M, Qiao C, Chen Q X and Ming D P. 2009. Theoretic and methodological review on sensor information tupu computation. Journal of Geo-Information Science, 11(5): 664-669
  33. 33.
    Ni L and Shu N. 1997. Object oriented knowledge representation for expert system of remote sensing image understanding. Journal of Wuhan Technical University of Surveying and Mapping, 22(1): 32-34, 46
  34. 34.
    Qiu Z C and Goldberg M. 1985. A new classification scheme based upon segmentation for remote sensing. Canadian Journal of Remote Sensing, 11(1): 59-69
  35. 35.
    Sha Z Y and Bian F L. 2004. Object-oriented spatial knowledge representation and its application. Journal of Remote Sensing (in Chinese), 8(2): 165-171
  36. 36.
    Sun H L. 2017. Dictionary of Geoscience. Beijing: Science Press
  37. 37.
    Sun X, Meng Y, Diao W H, Huang L J, Zhang X, Luo J C, Gao L R, Wang P J, Yan Z Y, Gao L J, Dong W, Feng Y C, Li J H and Fu K. 2022. The review of AI-based intelligent remote sensing capabilities. Journal of Image and Graphics, 27(6): 1799-1822
  38. 38.
    Tobler W R. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(S1): 234-240
  39. 39.
    Verhagen P and Drăguţ L. 2012. Object-based landform delineation and classification from DEMs for archaeological predictive mapping. Journal of Archaeological Science, 39(3): 698-703
  40. 40.
    Wang W and Gong J Y. 1999. Object-oriented integrated geographic information system infrastructure software. Journal of Image and Graphics, 4(5): 439-444
  41. 41.
    Wang Z H, Gao K, Yang X M, Su F Z, Huang C, Shi T Z, Yan F Q, Li H, Zhang H F, Lü N and Pan T T. 2022. Land use/land cover classification development from a geographical perspective. Geographical Research, 41(11): 2946-2962
  42. 42.
    Wang Z H, Lu C and Yang X M. 2018a. Exponentially sampling scale parameters for the efficient segmentation of remote-sensing images. International Journal of Remote Sensing, 39(6): 1628-1654
  43. 43.
    Wang Z H, Yang X M, Lu C and Yang F S. 2018b. A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images. International Journal of Applied Earth Observation and Geoinformation, 69: 88-98
  44. 44.
    Wang Z H, Yang X M and Zhou C H. 2021. Geographic knowledge graph for remote sensing big data. Journal of Geo-Information Science, 23(1): 16-28
  45. 45.
    Wang Z H, Zhang J Y, Yang X M, Huang C, Su F Z, Liu X L, Liu Y M and Zhang Y Z. 2022. Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 115: 103100
  46. 46.
    Woodcock C E and Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311-332
  47. 47.
    Wu J. 1999. Hierarchy and scaling: extrapolating information along a scaling ladder. Canadian Journal of Remote Sensing, 25(4): 367-380
  48. 48.
    Wu J G. 2007. Landscape Ecology: Pattern, Process, Scale and Hierarchy. 2nd ed. Beijing: Higher Education Press
  49. 49.
    Wu J G and Loucks O L. 1995. From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. The Quarterly Review of Biology, 70(4): 439-466
  50. 50.
    Xiao D N, Li X Z, Gao J, Chang Y, Zhang N and Li T S. 2010. Landscape Ecology. 2nd ed. Beijing: (Science Press
  51. 51.
    Yu H, Zhang S Q, Kong B and Li X F. 2010. Optimal segmentation scale selection for object-oriented remote sensing image classification. Journal of Image and Graphics, 15(2): 352-360
  52. 52.
    Zhang B. 2018. Remotely sensed big data era and intelligent information extraction. Geomatics and Information Science of Wuhan University, 43(12): 1861-1871
  53. 53.
    Zhang B, Yang X M, Gao L R, Meng Y, Sun X, Xiao C C and Ni L. 2022. Geo-cognitive models and methods for intelligent interpretation of remotely sensed big data. Acta Geodaetica et Cartographica Sinica, 51(7): 1398-1415
  54. 54.
    Zhang B P, Zhou C H and Chen S P. 2003. The geo-info-spectrum of montane altitudinal belts in China. Acta Geographica Sinica, 58(2): 163-171
  55. 55.
    Zhang F, Du B and Zhang L P. 2016. Scene classification via a gradient boosting random convolutional network framework. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1793-1802
  56. 56.
    Zhang J X, Gu H Y, Yang Y, Zhang H and Li H T. 2021. Research progress and trend of high-resolution remote sensing imagery intelligent interpretation. National Remote Sensing Bulletin, 25(11): 2198-2210
  57. 57.
    Zhang J X, Gu H Y, Yang Y, Zhang H, Li H T, Han W L and Shen J. 2022. Research progress and trend of intelligent interpretation for natural resources features. Acta Geodaetica et Cartographica Sinica, 51(7): 1606-1617
  58. 58.
    Zhang X Y, Du S H and Wang Q. 2018. Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping. Remote Sensing of Environment, 212: 231-248
  59. 59.
    Zhang Y J, Wang F, Li Y S, Ouyang S, Wei D, Liu X J, Kong D Y, Chen R X and Zhang B. 2023. Remote sensing knowledge graph construction and its application in typical scenarios. National Remote Sensing Bulletin, 27(2): 249-266
  60. 60.
    Zheng J, Luo J C, Chen Q X, Cai S H, Lu X J, Shen Z F and Sun Q H. 2003. Knowledge-based intelligent RS understanding system. Geo-Information Science, 5(1): 95-102
  61. 61.
    Zhu A X, Lu G N, Liu J, Qin C Z and Zhou C H. 2018. Spatial prediction based on Third Law of Geography. Annals of GIS, 24(4): 225-240

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