предыдущий|следующий

Knowledge and data driven remote sensing image interpretation： Recent developments and prospects

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

  • Email：mengyu@aircas.ac.cn
  • Introduction：孟瑜，研究方向为遥感图像智能解译、自然资源监测。E-mail： mengyu@aircas.ac.cn
MENG Yu1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

CHEN Jingbo1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

  • Email：zhangzheng@aircas.ac.cn
  • Introduction：张正，研究方向为遥感图像智能处理、遥感大数据分析。E-mail： zhangzheng@aircas.ac.cn
ZHANG Zheng1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing101408, China

LIU Zhiqiang12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing101408, China

ZHAO Zhitao12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

HUO Lianzhi1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing101408, China

SHI Keli12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

LIU Diyou1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

DENG Yupeng1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing100094, China

TANG Ping1

реферат

Knowledge and data are the two main elements that have characterized the development of remote sensing image interpretation for decades. With the continuous enrichment of sensor platforms and rapid breakthroughs in deep learning, big data, multi-modal, and long time-series methodologies, data-driven intelligent remote sensing image interpretation has become a hot research direction in recent years. However, in the deepening and expanding research and applications, the limitations of data-driven methods such as difficult reuse between different scenarios, strong training sample dependence, and weak interpretability are beginning to emerge. Various types of knowledge accumulated in the long-term remote sensing image interpretation practice have the characteristics of objective reality, certainty, scene adaptability, interpretability, etc., which can be complemented with data-driven approaches, and the dual-driven of knowledge and data is becoming a new direction of remote sensing image interpretation. This paper first reviews the major stages in the development of remote sensing image interpretation and the respective roles of knowledge and data in each of these stages. Then the main types of knowledge involved in remote sensing image interpretation are summarized and categorized into fourteen types. The fusion of knowledge and deep learning is an important path to achieve the dual-drive of knowledge and data, and this paper summarizes five categories and fifteen subcategories of knowledge and deep neural network fusion methods with relevant cases. From the perspective of knowledge types, this paper further provides an overview of existing applications of remote sensing interpretation with joint knowledge and data. The effectiveness and capability increment of fusing knowledge and data is demonstrated by the analyses of typical examples. Lastly, this paper gives a systematic prospect on the framework and key techniques for knowledge and data compound driven remote sensing image interpretation.

ключеви́че слова́

remote sensing image interpretation;knowledge driven;data driven;artificial intelligence;knowledge graphs;deep learning;natural resources;review

References

  1. 1.
    Arvor D, Betbeder J, Daher F R G, Blossier T, Le Roux R, Corgne S, Corpetti T, De Freitas Silgueiro V and da Silva Junior C A. 2021. Towards user-adaptive remote sensing: knowledge-driven automatic classification of Sentinel-2 time series. Remote Sensing of Environment, 264: 112615
  2. 2.
    Bai X D, Feng X Y, Yin Y Y, Yang M C, Wang X Y and Yang X. 2023. Combining images and trajectories data to automatically generate road networks. Remote Sensing, 15(13): 3343
  3. 3.
    Baroud S, Chokri S, Belhaous S and Mestari M. 2021. A brief review of graph convolutional neural network based learning for classifying remote sensing images. Procedia Computer Science, 191: 349-354
  4. 4.
    Belgiu M and Drăguţ L. 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24-31
  5. 5.
    Bonettini N, Cannas E D, Mandelli S, Bondi L, Bestagini P and Tubaro S. 2021. Video face manipulation detection through ensemble of CNNs//2020 25th International Conference on Pattern Recognition (ICPR). Milan: IEEE:5012-5019
  6. 6.
    Bose R and Frew J. 2005. Lineage retrieval for scientific data processing: a survey. ACM Computing Surveys (CSUR), 37(1): 1-28
  7. 7.
    Cao R, Tu W, Yang C X, Li Q, Liu J, Zhu J S, Zhang Q, Li Q Q and Qiu G P. 2020. Deep learning-based remote and social sensing data fusion for urban region function recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 82-97
  8. 8.
    Cao R, Zhu J S, Tu W, Li Q Q, Cao J Z, Liu B Z, Zhang Q and Qiu G P. 2018. Integrating aerial and street view images for urban land use classification. Remote Sensing, 10(10): 1553
  9. 9.
    Chang J X, Wang S X, Yang Y W and Gao X J. 2020. Hierarchical optimization method of building contour in high-resolution remote sensing images. Chinese Journal of Lasers, 47(10): 1010002
  10. 10.
    Chen F, Niu Z, Luo C F and Wang C Y. 2005. A new algorithm of object recognition based on spectral library for TM images. Remote Sensing Technology and Application, 20(4): 386-392
  11. 11.
    Chi M M, Plaza A, Benediktsson J A, Sun Z Y, Shen J S and Zhu Y Y. 2016. Big data for remote sensing: challenges and opportunities. Proceedings of the IEEE, 104(11): 2207-2219
  12. 12.
    Cui C, Zhao L, Ren H Y, Lu W L and Huang Y H. 2022. Integrating high-resolution remote sensing and street view images to identify urban villages: a case study in Yuexiu District, Guangzhou City. National Remote Sensing Bulletin, 26(9): 1802-1813
  13. 13.
    Cui Z J, Gao T, Talamadupula K and Ji Q. 2023. Knowledge-augmented deep learning and its applications: a survey. IEEE Transactions on Neural Networks and Learning Systems
  14. 14.
    Ding W and Wu J D. 2020. An airport knowledge-based method for accurate change analysis of airport runways in VHR remote sensing images. Remote Sensing, 12(19): 3163
  15. 15.
    Du P J, Liu P, Xia J S, Feng L, Liu S C, Tan K and Cheng L. 2014. Remote sensing image interpretation for urban environment analysis: methods, system and examples. Remote Sensing, 6(10): 9458-9474
  16. 16.
    Du P J, Wang X, Meng Y P, Lin C, Zhang P and Lu G. 2020. Effective change detection approaches for geographic national condition monitoring and land cover map updating. Journal of Geo-Information Science, 22(4): 857-866
  17. 17.
    Du S J, Du S H, Liu B, Zhang X Y and Zheng Z J. 2020. Large-scale urban functional zone mapping by integrating remote sensing images and open social data. GIScience and Remote Sensing, 57(3): 411-430
  18. 18.
    Esteva A, Kuprel B, Novoa R A, Ko J, Swetter S M, Blau H M and Thrun S. 2017. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639): 115-118
  19. 19.
    Fang J Y and Yan X F. 2023. Classification of multi-modal remote sensing images based on knowledge graph. International Journal of Remote Sensing, 44(15): 4815-4835
  20. 20.
    Fu R R and Kuang Z. 2023. Study of mining activity information extraction by remote sensing image——Taken Xingren area as an example. Guizhou Geology, 40(2): 165-172
  21. 21.
    Ge Y, Zhang X N, Atkinson P M, Stein A and Li L F. 2022. Geoscience-aware deep learning: a new paradigm for remote sensing. Science of Remote Sensing, 5: 100047
  22. 22.
    Ghaffarian S, Valente J, Van Der Voort M and Tekinerdogan B. 2021. Effect of attention mechanism in deep learning-based remote sensing image processing: a systematic literature review. Remote Sensing, 13(15): 2965
  23. 23.
    Ghorbanzadeh O, Tiede D, Wendt L, Sudmanns M and Lang S. 2021. Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA. European Journal of Remote Sensing, 54(S1): 127-140
  24. 24.
    Gong J Y, Zhang Z, Jia H W, Zhou H, Zhao Y X and Xiong H J. 2022. Multi-source data ground object extraction based on knowledge-aware and multi-scale feature fusion network. Geomatics and Information Science of Wuhan University, 47(10): 1546-1554
  25. 25.
    Grinias I, Panagiotakis C and Tziritas G. 2016. MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images. ISPRS Journal of Photogrammetry and Remote Sensing, 122: 145-166
  26. 26.
    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
  27. 27.
    Gu W, Lv Z H and Hao M. 2017. Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools and Applications, 76(17): 17719-17734
  28. 28.
    Hao X J, Ji Z, Li X H, Yin L, Liu L Z Y, Sun M Y, Liu Q and Yang R J. 2021. Construction and application of a knowledge graph. Remote Sensing, 13(13): 2511
  29. 29.
    Hao Y. 2022. Multi-scale Forecast and Projection of Urban Waterlogging Risk Based on Coupled Hydro-meteorological Modelling. Nanjing: Nanjing University
  30. 30.
    He X, Chen Y S and Ghamisi P. 2020. Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3246-3263
  31. 31.
    Hospedales T M, Antoniou A, Micaelli P and Storkey A J. 2022. Meta-learning in neural networks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9): 5149-5169
  32. 32.
    Hu Q, Sulla-Menashe D, Xu B D, Yin H, Tang H J, Yang P and Wu W B. 2019. A phenology-based spectral and temporal feature selection method for crop mapping from satellite time series. International Journal of Applied Earth Observation and Geoinformation, 80: 218-229
  33. 33.
    Hua Y S, Mou L C and Zhu X X. 2020. Relation network for multilabel aerial image classification. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4558-4572
  34. 34.
    Huang Z H, Jiang B C and Wang Z Q. 2022. A remote sensing image object knowledge association method based on geographic knowledge. Bulletin of Surveying and Mapping, (10): 28-36
  35. 35.
    Kattenborn T, Leitloff J, Schiefer F and Hinz S. 2021. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173: 24-49
  36. 36.
    Kushibar K, Valverde S, González-Villà S, Bernal J, Cabezas M, Oliver A and Lladó X. 2018. Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features. Medical Image Analysis, 48: 177-186
  37. 37.
    Leigh H W and Magruder L A. 2016. Using dual-wavelength, full-waveform airborne lidar for surface classification and vegetation characterization. Journal of Applied Remote Sensing, 10(4): 045001
  38. 38.
    Li J, Wang J L, Zhang J, Liu C L, He S L and Liu L F. 2022a. Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector. Ecological Indicators, 136: 108620
  39. 39.
    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
  40. 40.
    Li M M and Stein A. 2020. Mapping land use from high resolution satellite images by exploiting the spatial arrangement of land cover objects. Remote Sensing, 12(24): 4158
  41. 41.
    Li W W, Hsu C Y and Hu M S. 2021a. Tobler’s first law in GeoAI: a spatially explicit deep learning model for terrain feature detection under weak supervision. Annals of the American Association of Geographers, 111(7): 1887-1905
  42. 42.
    Li W Z, Wang F D and Xia G S. 2020. A geometry-attentional network for ALS point cloud classification. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 26-40
  43. 43.
    Li Y S, Kong D Y, Zhang Y J, Ji Z and Xiao R. 2020. Zero-shot remote sensing image scene classification based on robust cross-domain mapping and gradual refinement of semantic space. Acta Geodaetica et Cartographica Sinica, 49(12): 1564-1574
  44. 44.
    Li Y S, Kong D Y, Zhang Y J, Tan Y H and Chen L. 2021b. Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification. ISPRS Journal of Photogrammetry and Remote Sensing, 179: 145-158
  45. 45.
    Li Y S, Ouyang S and Zhang Y J. 2022b. Combining deep learning and ontology reasoning for remote sensing image semantic segmentation. Knowledge-Based Systems, 243: 108469
  46. 46.
    Li Y S, Zhou Y H, Zhang Y J, Zhong L H, Wang J and Chen J D. 2022c. DKDFN: domain knowledge-guided deep collaborative fusion network for multimodal unitemporal remote sensing land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 186: 170-189
  47. 47.
    Li Z F, Liu H, Zhang Z L, Liu T T and Xiong N N. 2022d. Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Transactions on Neural Networks and Learning Systems, 33(8): 3961-3973
  48. 48.
    Lin C X, Zhong L H, Song X P, Dong J W, Lobell D B and Jin Z N. 2022. Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach. Remote Sensing of Environment, 274: 112994
  49. 49.
    Lin X G and Zhang J X. 2017. Object-based morphological building index for building extraction from high resolution remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 46(6): 724-733
  50. 50.
    Liu R Y, Huang J J, Li G, Feng J S, Wu X L and Li T H. 2023. Revisiting temporal modeling for clip-based image-to-video knowledge transferring//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE:6555-6564
  51. 51.
    Liu S C, Wen Z M, Tao Y, Zhu D J, Zhang J and Zeng H W. 2017. Influence of different topographic correction methods on the remote sensing extraction of Robinia pseudoacacia distribution. Journal of Beijing Forestry University, 39(5): 25-33
  52. 52.
    Liu X N, Zhou Y, Zhao J Q, Yao R, Liu B and Zheng Y. 2019. Siamese convolutional neural networks for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 16(8): 1200-1204
  53. 53.
    Liu Z Q, Tang P, Zhang W X and Zhang Z. 2022. CNN-enhanced heterogeneous graph convolutional network: inferring land use from land cover with a case study of park segmentation. Remote Sensing, 14(19): 5027
  54. 54.
    Lu X N, Sun X, Diao W H, Mao Y Q, Li J X, Zhang Y D, Wang P J and Fu K. 2023. Few-shot object detection in aerial imagery guided by text-modal knowledge. IEEE Transactions on Geoscience and Remote Sensing, 61: 5604719
  55. 55.
    Luo J C, Zhou C H and Yang Y. 2001. ANN remote sensing classification model and its integration approach with geo-knowledge. Journal of Remote Sensing (in Chinese), 5(2): 122-129
  56. 56.
    Luo R T, Zhang N, Han B and Yang L J. 2020. Context-aware zero-shot recognition//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York: AAAI:11709-11716
  57. 57.
    Ma L, Liu Y, Zhang X L, Ye Y X, Yin G F and Johnson B A. 2019. Deep learning in remote sensing applications: a meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152: 166-177
  58. 58.
    Mao Y X, Zhao H Q, Feng S Q, Xu H X, He T and Song L J. 2022. Research on hyperspectral remote sensing open-pit minerals identification method based on spectral matching. Natural Resources Informatization, 4: 28-32, 15
  59. 59.
    Mottaghi R, Bagherinezhad H, Rastegari M and Farhadi A. 2016. Newtonian image understanding: unfolding the dynamics of objects in static images//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE:3521-3529
  60. 60.
    Mou L C, Ghamisi P and Zhu X X. 2017. Deep recurrent neural networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3639-3655
  61. 61.
    Mountrakis G, Im J and Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3): 247-259
  62. 62.
    Nguyen T A, Kellenberger B and Tuia D. 2022. Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning. Remote Sensing of Environment, 281: 113217
  63. 63.
    Ouyang S and Li Y S. 2021. Combining deep semantic segmentation network and graph convolutional neural network for semantic segmentation of remote sensing imagery. Remote Sensing, 13(1): 119
  64. 64.
    Paris C, Bruzzone L and Fernández-Prieto D. 2019. A novel approach to the unsupervised update of land-cover maps by classification of time series of multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4259-4277
  65. 65.
    Park E, Han X F, Berg T L and Berg A C. 2016. Combining multiple sources of knowledge in deep CNNs for action recognition//2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid: IEEE:1-8
  66. 66.
    Petitjean F, Inglada J and Gançarski P. 2012. Satellite image time series analysis under time warping. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3081-3095
  67. 67.
    Phan T N, Kuch V and Lehnert L W. 2020. Land cover classification using Google earth engine and random forest classifier—the role of image composition. Remote Sensing, 12(15): 2411
  68. 68.
    Planque C, Lucas R, Punalekar S, Chognard S, Hurford C, Owers C, Horton C, Guest P, King S, Williams S and Bunting P. 2021. National crop mapping using sentinel-1 time series: a knowledge-based descriptive algorithm. Remote Sensing, 13(5): 846
  69. 69.
    Pouyanfar S, Sadiq S, Yan Y L, Tian H M, Tao Y D, Reyes M P, Shyu M L, Chen S C and Iyengar S S. 2018. A survey on deep learning: algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5): 92
  70. 70.
    Prodhan F A, Zhang J H, Yao F M, Shi L M, Pangali Sharma T P, Zhang D, Cao D, Zheng M X, Ahmed N and Mohana H P. 2021. Deep learning for monitoring agricultural drought in South Asia using remote sensing data. Remote Sensing, 13(9): 1715
  71. 71.
    Qi W J and Yang X M. 2017. Mountain and plain vegetation boundaries extraction in Duchang county province Jiangxi. Journal of Geo-Information Science, 19(4): 559-569
  72. 72.
    Raissi M, Perdikaris P and Karniadakis G E. 2019. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378: 686-707
  73. 73.
    Shen W H, Zhong Y F, Wang J J, Zheng Z and Ma A L. 2023. Construction and application of flood disaster knowledge graph based on multi-modal data. Geomatics and Information Science of Wuhan University, 48(12): 2009-2018
  74. 74.
    Shen X Q, Liu B, Zhou Y, Zhao J Q and Liu M M. 2020. Remote sensing image captioning via Variational Autoencoder and Reinforcement Learning. Knowledge-Based Systems, 203: 105920
  75. 75.
    Song J C, Lin T, Li X H and Prishchepov A V. 2018. Mapping urban functional zones by integrating very high spatial resolution remote sensing imagery and points of interest: a case study of Xiamen, China. Remote Sensing, 10(11): 1737
  76. 76.
    Su Y, Zhong Y F, Zhu Q Q and Zhao J. 2021. Urban scene understanding based on semantic and socioeconomic features: from high-resolution remote sensing imagery to multi-source geographic datasets. ISPRS Journal of Photogrammetry and Remote Sensing, 179: 50-65
  77. 77.
    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
  78. 78.
    Sun X, Wang P J, Lu W X, Zhu Z C, Lu X N, He Q B, Li J X, Rong X E, Yang Z J, Chang H, He Q L, Yang G, Wang R P, Lu J W and Fu K. 2023. RingMo: a remote sensing foundation model with masked image modeling. IEEE Transactions on Geoscience and Remote Sensing, 61: 5612822
  79. 79.
    Tang J W, Zhang Z, Zhao L J and Tang P. 2021. Increasing shape bias to improve the precision of center pivot irrigation system detection. Remote Sensing, 13(4): 612
  80. 80.
    Tao F, Zhang H, Liu A and Nee A Y C. 2019. Digital twin in industry: state-of-the-art. IEEE Transactions on Industrial Informatics, 15(4): 2405-2415
  81. 81.
    Uhl J H, Leyk S, Li Z K, Duan W W, Shbita B, Chiang Y Y and Knoblock C A. 2021. Combining remote-sensing-derived data and historical maps for long-term back-casting of urban extents. Remote Sensing, 13(18): 3672
  82. 82.
    Velasquez-Camacho L, Etxegarai M and de-Miguel S. 2023. Implementing Deep Learning algorithms for urban tree detection and geolocation with high-resolution aerial, satellite, and ground-level images. Computers, Environment and Urban Systems, 105: 102025
  83. 83.
    Wang B, Chen Z L, Wu L, Xie P, Fan D L and Fu B L. 2020. Road extraction of high-resolution satellite remote sensing images in U-Net network with consideration of connectivity. Journal of Remote Sensing (in Chinese), 24(12): 1488-1499
  84. 84.
    Wang D, Zhang Q M, Xu Y F, Zhang J, Du B, Tao D C and Zhang L P. 2023. Advancing plain vision transformer toward remote sensing foundation model. IEEE Transactions on Geoscience and Remote Sensing, 61: 5607315
  85. 85.
    Wang X, Li X B, Tan M H and Xin L J. 2015. Remote sensing monitoring of changes in winter wheat area in North China Plain from 2001 to 2011. Transactions of the Chinese Society of Agricultural Engineering, 31(8): 190-199
  86. 86.
    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
  87. 87.
    Wang Z H, Yang X M, Liu Y M, Liu B, Zhang J Y, Liu X L, Meng D, Gao K, Zeng X W and Ding Y X. 2024. Geographical principles of remote sensing image analysis and the hierarchical patch model based analysis framework. National Remote Sensing Bulletin, 28(6): 1412-1424
  88. 88.
    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
  89. 89.
    Wei J W, Hui W H, Cheng M Z and Li H. 2020. Geographic ontology-driven object oriented landslide recognition. Remote Sensing Information, 35(2): 94-99
  90. 90.
    Wu F Y, Wang X, Ding J W, Du P J and Tan K. 2020. Improved cascade forest deep learning model for hyperspectral imagery classification. National Remote Sensing Bulletin, 24(4): 439-453
  91. 91.
    Wu T J, Luo J C, Xia L G, Shen Z F and Hu X D. 2015. Prior knowledge-based automatic object-oriented hierarchical classification for updating detailed land cover maps. Journal of the Indian Society of Remote Sensing, 43(4): 653-669
  92. 92.
    Wu T J, Luo J C, Zhang X, Dong W, Huang Q T, Zhou Y N, Liu W, Sun Y W, Yang Y P, Hu X D and Gao L J. 2023. Remote sensing granular computing and precise applications based on geo-parcels. National Remote Sensing Bulletin, 27(12): 2774-2795
  93. 93.
    Xie M, Jean N, Burke M, Lobell D and Ermon S. 2016. Transfer learning from deep features for remote sensing and poverty mapping//Proceedings of the 30th AAAI Conference on Artificial Intelligence. Phoenix: AAAI:3929-3935
  94. 94.
    Xie X Z, Niu J W, Liu X F, Chen Z S, Tang S J and Yu S. 2021. A survey on incorporating domain knowledge into deep learning for medical image analysis. Medical Image Analysis, 69: 101985
  95. 95.
    Xie Y T, Xia Y, Zhang J P, Song Y, Feng D G, Fulham M and Cai W D. 2019. Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Transactions on Medical Imaging, 38(4): 991-1004
  96. 96.
    Xie Y T, Zhang J P, Xia Y, Fulham M and Zhang Y N. 2018. Fusing texture, shape and deep model-learned information at decision level for automated classification of lung nodules on chest CT. Information Fusion, 42: 102-110
  97. 97.
    Xu J Y, Zhang Z L, Friedman T, Liang Y T and van den Broeck G. 2018. A semantic loss function for deep learning with symbolic knowledge. arXiv:1711.11157
  98. 98.
    Yang A N, Xu Y H and Su H J. 2014. Urban built-up land extraction and change detection analysis using built-up indexes. Geomatics and Spatial Information Technology, 37(8): 30-34
  99. 99.
    Yang H Q, Yu J, Qin K and Zhang G N. 2006. The research on the ground objects extraction from the LIDAR based on knowledge. Bulletin of Surveying and Mapping, 12: 9-11, 15
  100. 100.
    Yang W K, Zhao J J, Qiang Y, Yang X T, Dong Y Y, Du Q Q, Shi G H and Zia M B. 2019. DScGANS: integrate domain knowledge in training dual-path semi-supervised conditional generative adversarial networks and S3VM for ultrasonography thyroid nodules classification//22nd International Conference on Medical Image Computing and Computer Assisted Intervention. Shenzhen: Springer:558-566
  101. 101.
    Yang Z J, Diao C Y and Gao F. 2023. Towards scalable within-season crop mapping with phenology normalization and deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16: 1390-1402
  102. 102.
    Yao Y H, Suonan D Z and Zhang J Y. 2020. Compilation of 1:50, 000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the north-south transitional zone of China. Journal of Geographical Sciences, 30(2): 267-280
  103. 103.
    Yin H X, Molchanov P, Alvarez J M, Li Z Z, Mallya A, Hoiem D, Jha N K and Kautz J. 2020. Dreaming to distill: data-free knowledge transfer via deepinversion//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE:8712-8721
  104. 104.
    You Y F, Wang S Y, Wang B, Ma Y X, Shen M, Liu W H and Xiao L. 2019. Study on hierarchical building extraction from high resolution remote sensing imagery. Journal of Remote Sensing (in Chinese), 23(1): 125-136
  105. 105.
    Yu D X, Zhang B M, Zhao C, Guo H T and Lu J. 2020. Scene classification of remote sensing image using ensemble convolutional neural network. Journal of Remote Sensing (in Chinese), 24(6): 717-727
  106. 106.
    Zhang D D, Yang X M, Su F Z, Du Y Y, Sun X Y and Xue Z S. 2010. Spatio-temporal differences in land use change in the Daya Bay and correlation with geomorphologic indicators. Resources Science, 32(8): 1551-1557
  107. 107.
    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
  108. 108.
    Zhang J Y, Yao Y H, Suonan D Z, Gao L J, Wang J and Zhang X H. 2019. Mapping of mountain vegetation in Taibai Mountain based on mountain altitudinal belts with remote sensing. Journal of Geo-Information Science, 21(8): 1284-1294
  109. 109.
    Zhang W, Tang P, Corpetti T and Zhao L J. 2021a. WTS: a weakly towards strongly supervised learning framework for remote sensing land cover classification using segmentation models. Remote Sensing, 13(3): 394
  110. 110.
    Zhang X, Huo C L, Xu N, Jiang H Z, Cao Y, Ni L and Pan C H. 2021b. Multitask learning for ship detection from synthetic aperture radar images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 8048-8062
  111. 111.
    Zhang Y, Li Q Z, Huang H P, Wu W, Du X and Wang H Y. 2017. The combined use of remote sensing and social sensing data in fine-grained urban land use mapping: a case study in Beijing, China. Remote Sensing, 9(9): 865
  112. 112.
    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
  113. 113.
    Zhang Y Z, Liu J J and Shen W J. 2022. A review of ensemble learning algorithms used in remote sensing applications. Applied Sciences, 12(17): 8654
  114. 114.
    Zheng H, Lin L F, Hu H J, Zhang Q W, Chen Q Q, Iwamoto Y, Han X H, Chen Y W, Tong R F and Wu J. 2019. Semi-supervised segmentation of liver using adversarial learning with deep atlas prior//22nd International Conference on Medical Image Computing and Computer Assisted Intervention. Shenzhen: Springer:148-156
  115. 115.
    Zhong Y F, Su Y, Wu S Q, Zheng Z D, Zhao J, Ma A L, Zhu Q Q, Ye R C, Li X M, Pellikka P and Zhang L P. 2020. Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: a case study of Chinese cities. Remote Sensing of Environment, 247: 111838
  116. 116.
    Zhou S Y, Kuester T, Bochow M, Bohn N, Brell M and Kaufmann H. 2021. A knowledge-based, validated classifier for the identification of aliphatic and aromatic plastics by WorldView-3 satellite data. Remote Sensing of Environment, 264: 112598
  117. 117.
    Zhou Y N, Shen Z F, Luo J C, Chen Q X, Hu X D and Shen J X. 2010. Shadow-assisted object-oriented extraction of urban buildings. Geography and Geo-Information Science, 26(3): 37-40
  118. 118.
    Zhou Y S, Liu H C, Ma F, Pan Z X and Zhang F. 2023. A sidelobe-aware small ship detection network for synthetic aperture radar imagery. IEEE Transactions on Geoscience and Remote Sensing, 61: 5205516
  119. 119.
    Zhuang J F, Dong Y and Bai H L. 2021. Ensemble learning with siamese networks for visual tracking. Neurocomputing, 464: 497-506

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