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Research progress and trend of high-resolution remote sensing imagery intelligent interpretation

  • role： First author第一作者
  • Affiliation：

    National Quality Inspection and Testing Center for Surveying and Mapping Products State, Beijing 100830, China

  • Email：zhangjx@casm.ac.cn
  • Introduction：张继贤，1965年生，男，研究员，研究方向为摄影测量与遥感。E-mail：zhangjx@casm.ac.cn
ZHANG Jixian1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

  • Email：guhy@casm.ac.cn
  • Introduction：顾海燕，1982年生，女，副研究员，研究方向为遥感智能解译。E-mail：guhy@casm.ac.cn
GU Haiyan2*,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

YANG Yi2,  
  • Affiliation：

    National Quality Inspection and Testing Center for Surveying and Mapping Products State, Beijing 100830, China

ZHANG He1,  
  • Affiliation：

    Chinese Academy of Surveying and Mapping, Beijing 100830, China

LI Haitao2

résumé

Remote sensing imagery interpretation is a continuously developing research direction. With the ever-changing needs of remote sensing applications, the rapid development of high-resolution remote sensing data, the accumulation of geographic knowledge, and the development of artificial intelligence, automated and intelligent classification technology should be urgently developed. This paper aims at the development of intelligent interpretation. First, the research progress is elaborated from three aspects: interpretation unit, classification method, and interpretation recognition. Then, a geographic scene-level overall framework for intelligent understanding of remote sensing imagery is presented. The framework includes geographic knowledge map construction, deep convolutional neural network model construction, and the semantic classification method based on geographic knowledge graph and deep learning model. The preliminary test results are provided. Lastly, the important development trend of intelligent understanding is projected. A geographic knowledge graph can realize formal description and reasoning calculation of geographic knowledge and improve the learning capability and the utilization rate of prior knowledge. Classification and related semantic information can also be obtained, which is helpful for in-depth cognition of geographic scenes. This study looks forward to expanding the ideas and methods for intelligent interpretation of remote sensing images and improving its fineness and intelligence. Intelligent interpretation can understand geospatial capabilities intelligently and promote in-depth transformation of data, information, knowledge, and intelligence.

mots-clés

intelligent interpretation;deep learning;geographic knowledge graph;high-resolution remote sensing imagery

References

  1. 1.
    Arvor D, Belgiu M, Falomir Z, Mougenot I and Durieux L. 2019. Ontologies to interpret remote sensing images: why do we need them? GIScience and Remote Sensing, 56(6): 911-939
  2. 2.
    Ball J E, Anderson D T and Chan C S Sr. 2017. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of Applied Remote Sensing, 11(4): 042609
  3. 3.
    Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2-16
  4. 4.
    Chen G, Weng Q H, Hay G J and He Y N. 2018. Geographic object-based image analysis (GEOBIA): emerging trends and future opportunities. GIScience and Remote Sensing, 55(2): 159-182
  5. 5.
    Cheng G, Han J W and Lu X Q. 2017. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105(10): 1865-1883
  6. 6.
    Cheng G, Xie X X, Han J W, Guo L and Xia G S. 2020. Remote sensing image scene classification meets deep learning: challenges, methods, benchmarks, and opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 3735-3756
  7. 7.
    Fu T Y, Ma L, Li M C and Johnson B A. 2018. Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery. Journal of Applied Remote Sensing, 12(2): 025010
  8. 8.
    Ghazouani F, Farah I R and Solaiman B. 2019. A multi-level semantic scene interpretation strategy for change interpretation in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 8775-8795
  9. 9.
    Gu H Y, Li H T, Yan L, Liu Z J, Blaschke T and Soergel U. 2017. An object-based semantic classification method for high resolution remote sensing imagery using ontology. Remote Sensing, 9(4): 329
  10. 10.
    Gu H Y, Han Y S, Yang Y, Li H T, Liu Z J, Soergel U, Blaschke T and Cui S Y. 2018. An efficient parallel multi-scale segmentation method for remote sensing imagery. Remote Sensing, 10(4): 590
  11. 11.
    Han W, Feng R Y, Wang L Z and Cheng Y F. 2018. A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 23-43
  12. 12.
    Huang B, Zhao B and Song Y M. 2018. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214: 73-86
  13. 13.
    Kemker R, Salvaggio C and Kanan C. 2018. Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 60-77
  14. 14.
    Li D J, Guo H T, Lu J, Zhao C, Lin Y Z and Yu D H. 2020. A remote sensing image classification procedure based on multilevel attention fusion U-Net. Acta Geodaetica et Cartographica Sinica, 49(8): 1051-1064
  15. 15.
    Li H T, Gu H Y, Han Y S and Yang J H. 2008. An efficient multi-scale segmentation for high-resolution remote sensing imagery based on Statistical region merging and minimum heterogeneity rule//2008 International Workshop on Earth Observation and Remote Sensing Applications. Beijing, China: IEEE: 1-6
  16. 16.
    Li H T, Gu H Y, Han Y S and Yang J H. 2010. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. International Journal of Remote Sensing, 31(6): 1453-1470
  17. 17.
    Li J Y, Huang X and Gong J Y. 2019. Deep neural network for remote-sensing image interpretation: status and perspectives. National Science Review, 6(6): 1082-1086
  18. 18.
    Li Q J, Liu J, Mi X F, Yang J and Yu T. 2021. Object-oriented crop classification for GF-6 WFV remote sensing images based on convolutional neural network. Journal of Remote Sensing, 25(2): 549-558
  19. 19.
    Lin D Y, Fu K, Wang Y, Xu G L and Sun X. 2017. MARTA GANs: unsupervised representation learning for remote sensing image classification. IEEE Geoscience and Remote Sensing Letters, 14(11): 2092-2096
  20. 20.
    Ma F, Gao F, Sun J P, Zhou H Y and Hussain A. 2019. Attention graph convolution network for image segmentation in big SAR imagery data. Remote Sensing, 11(21): 2586
  21. 21.
    Ma L, Li M C, Ma X X, Cheng L, Du P J and Liu Y X. 2017. A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 277-293
  22. 22.
    Maggiori E, Tarabalka Y, Charpiat G and Alliez P. 2017. Convolutional neural networks for large-scale remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 645-657
  23. 23.
    Marcos D, Volpi M, Kellenberger B and Tuia D. 2018. Land Cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models. ISPRS Journal of Photogrammetry and Remote Sensing, 145: 96-107
  24. 24.
    Maxwell A E, Warner T A and Fang F. 2018. Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9): 2784-2817
  25. 25.
    Panboonyuen T, Jitkajornwanich K, Lawawirojwong S, Srestasathiern P and Vateekul P. 2019. Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning. Remote Sensing, 11(1): 83
  26. 26.
    Réjichi S, Chaabane F and Tupin F. 2015. Expert knowledge-based method for satellite image time series analysis and interpretation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 2138-2150
  27. 27.
    Rotich G, Aakur S, Minetto R, Segundo M P and Sarkar S. 2018. Using semantic relationships among objects for geospatial land use classification//2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). Washington, DC, USA: IEEE: 1-7
  28. 28.
    Saini R and Ghosh S K. 2017. Ensemble classifiers in remote sensing: a review//2017 International Conference on Computing, Communication and Automation (ICCCA). Greater Noida, India: IEEE: 1148-1152
  29. 29.
    Sun W W and Wang R S. 2018. Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM. IEEE Geoscience and Remote Sensing Letters, 15(3): 474-478
  30. 30.
    Sun X, Shi A J, Huang H and Mayer H. 2020. BAS4Net: boundary-aware semi-supervised semantic segmentation network for very high resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 5398-5413
  31. 31.
    Sun Y, Tian Y and Xu Y P. 2019. Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: structural stereotype and insufficient learning. Neurocomputing, 330: 297-304
  32. 32.
    Troya-Galvis A, Gançarski P and Berti-Équille L. 2018. Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. Pattern Recognition, 73: 259-274
  33. 33.
    Wang H F, Qi G L and Chen H J. 2019. Knowledge Graph: Method, Practice and Application. Beijing: Electronic Industry Press
  34. 34.
    Wang J J, Zhong Y F, Zheng Z, Ma A L and Zhang L P. 2021. RSNet: the search for remote sensing deep neural networks in recognition tasks. IEEE Transactions on Geoscience and Remote Sensing 59(3): 2520-2534
  35. 35.
    Xia L G, Luo J C, Wang W H and Shen Z F. 2014. Land cover automatic classification based on RS-Informatic Tupu. Journal of Remote Sensing, 18(4): 788-803
  36. 36.
    Yan X F, Ai T H, Yang M and Yin H M. 2019. A graph convolutional neural network for classification of building patterns using spatial vector data. ISPRS Journal of Photogrammetry and Remote Sensing, 150: 259-273
  37. 37.
    Yu Y, Gong Z Q, Wang C and Zhong P. 2018. An unsupervised convolutional feature fusion network for deep representation of remote sensing images. IEEE Geoscience and Remote Sensing Letters, 15(1): 23-27
  38. 38.
    Zhang C, Sargent I, Pan X, Li H P, Gardiner A, Hare J and Atkinson P M. 2018b. An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment, 216: 57-70
  39. 39.
    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
  40. 40.
    Zhang X Y, Du S H, Wang Q and Zhou W Q. 2018a. Multiscale geoscene segmentation for extracting urban functional zones from VHR satellite images. Remote Sensing, 10(2): 281
  41. 41.
    Zhang X Y, Du S H and Wang Y C. 2015. Semantic classification of heterogeneous urban scenes using intrascene feature similarity and interscene semantic dependency. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5): 2005-2014
  42. 42.
    Zhao B, Zhong Y F, Xia G S and Zhang L P. 2016. Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2108-2123
  43. 43.
    Zhao W Z, Du S H and Emery W J. 2017. Object-based convolutional neural network for high-resolution imagery classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7): 3386-3396
  44. 44.
    Zhou P C, Cheng G, Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. Journal of Remote Sensing, 25(1): 182-197
  45. 45.
    Zhu X X, Tuia D, Mou L C, Xia G S, Zhang L P, Xu F and Fraundorfer F. 2017. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5(4): 8-36

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