Proceedings of International Conference on Applied Innovation in IT
2022/03/09, Volume 10, Issue 1, pp.125-132

Advanced Method of Land Cover Classification Based on High Spatial Resolution Data and Convolutional Neural Network

Andrii Shelestov, Bohdan Yailymov, Hanna Yailymova, Leonid Shumilo, Mykola Lavreniuk,

Abstract: Based on modern satellite products Planet with high spatial resolution 3 meters, authors of this paper improved the neural network methodology for constructing land cover classification maps based on satellite data of high spatial resolution using the latest architectures of convolutional neural networks. The process of information features formation for types of land cover is described and the method of land cover type classification on the basis of satellite data of high spatial resolution is improved. A method for filtering artificial objects and other types of land cover using a probabilistic channel is proposed, and a convolutional neural network architecture to classify high-resolution spatial satellite data is developed. The problem of building density maps for the quarters of the city atlas construction is solved and the metrics for estimating the accuracy of classification map construction methods are analyzed. This will make it possible to obtain high-precision building maps to calculate the building area by functional segments of the Urban Atlas and monitor the development of the city in time. This will make it possible to create the first geospatial analogue of the product Copernicus Urban Atlas for Kyiv using high spatial resolution data. This Urban Atlas will be the first such product in Ukraine, which can be further extended to other cities in Ukraine. As a further development, the authors plan to create a methodology for combining satellite and in-situ air quality monitoring data in the city based on the developed Urban Atlas, which will provide high-precision layers of PM10 and PM2.5 concentrations with high spatial and temporal resolution of Ukraine.

Keywords: Convolution Neural Network, Probability Classification, Land Cover Map, Urban Atlas, Smart City

DOI: 10.25673/76943

Download: PDF


  1. P. Casals-Carrasco, S. Kubo, and B. Babu, “MadhavanApplication of Spectral Mixture Analysis for TerrainEvaluation Studies”, Int. J. of Remote Sensing, 2000, vol.21, pp. 3039-3055.
  2. R. Manandhar, I. Odeh, and T. Ancev, “Improving theaccuracy of land use and land cover classification of landsat data using post-classification enhancement”, Remote Sens,2009, vol. 1, pp. 330-344.
  3. T. Blaschke, “Object based image analysis for remotesensing”, ISPRS J. Photogramm, Remote Sens, 2010, vol.65, pp. 2-16.
  4. D. C. Duro, S. E. Franklin, and M. G. Dubé, “A comparisonof pixel-based and object-based image analysis with selectedmachine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery”,Remote Sens, Environ, 2012, vol. 118, pp. 259-272.
  5. Z. Y. Lv, P. L. Zhang, and J. A. Benediktsson, “Automaticobject-oriented, spectral-spatial feature extraction driven byTobler’s first law of geography for very high-resolutionaerial imagery classification”, Remote Sens, 2017, vol. 9, p.285.
  6. D. Marmanis, K. Schindler, J. D. Wegner, S. Galliani,M. Datcu, and U. Stilla, “Classification with an edge:Improving semantic image segmentation with boundarydetection”, ISPRS J. Photogramm, Remote Sens, 2018, vol.135, pp. 158-172.
  7. W. Dong, T.J. Wu, J.C. Luo, Y.W. Sun, and L. G. Xia,“Land-parcel-based Digital Soil Mapping of Soil NutrientProperties in an Alluvial-diluvia Plain Agricultural Area inChina”, Geoderma, 2019, vol. 340, pp. 234-248.
  8. K. Simonyan and A. Zisserman, “Very deep convolutionalnetworks for large-scale image recognition”, In Proceedingsof the 3rd International Conference on LearningRepresentations (ICLR 2015), San Diego, CA, USA, 7-9May 2015, pp. 1-14.
  9. D. Lu and Q. Weng, “A survey of image classificationmethods and techniques for improving classificationperformance”, Int. J. Remote Sens, 2007, vol. 28, pp. 823-870.
  10. M. Pesaresi, A. Gerhardinger, and F. Kayitakire, “A robustbuilt-up area presence index by anisotropic rotation-invariant textural measure”, IEEE J. Sel. Top. Appl. EarthObserv. Remote Sens, 2008, vol. 1, pp.180-192.
  11. D. J. Marceau, P. J. Howarth, J. M. Dubois, andD. J. Gratton, “Evaluation of the grey-level co-occurrencematrix method for land-cover classification using SPOT imagery”, IEEE Trans. Geosci, Remote Sens, 1990, vol. 28.pp. 513-519.
  12. E. Hussain and J. Shan, “Object-based urban land coverclassification using rule inheritance over very high-resolution multisensor and multitemporal data”, GISci,Remote Sens, 2016, vol. 53, pp. 164-182.
  13. D. Li, Y. Ke, H. Gong, and X. Li, “Object-based urban treespecies classification using bi-temporal WorldView-2 andWorldView-3 images”, Remote Sens, 2015, vol. 7. pp.16917-16937.
  14. G. Fu, H. Zhao, C. Li, and L. Shi, “Segmentation for High-Resolution Optical Remote Sensing Imagery UsingImproved Quadtree and Region Adjacency GraphTechnique”, Remote Sens, 2013, vol. 5, pp. 3259-3279.
  15. M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler,R. Benenson, W. Franke, S. Roth, and B. Schiele, “Thecityscapes dataset for semantic urban scene understanding”,In Proceedings of the IEEE conference on computer visionand pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, pp. 3213-3223.
  16. V. Mnih and G. E. Hinton, “Learning to detect roads in high-resolution aerial images”, In Computer Vision—ECCV2010, Lecture Notes in Computer Science; Daniilidis, K.,Maragos, P., Paragios, N., Eds., Springer:Berlin/Heidelberg, Germany, 2010, vol. 6316, pp. 210-223.
  17. J. Wang, J. Song, M. Chen, and Z. Yang, “Road networkextraction: A neural-dynamic framework based on deeplearning and a finite state machine”, Int. J, Remote Sens,2015, vol. 36, pp. 3144-3169.
  18. F. Hu, G. S. Xia, J. Hu, and L. Zhang, “Transferring deepconvolutional neural networks for the scene classification of high-resolution remote sensing imagery,” Remote Sens,2015, vol. 7, pp. 14680-14707.
  19. M. Längkvist, A. Kiselev, M. Alirezaie, and A. Loutfi,“Classification and segmentation of satellite orthoimagery using convolutional neural networks”, Remote Sens, 2016,vol. 8, p. 329.
  20. E. Maltezos, “Deep convolutional neural networks forbuilding extraction from orthoimages and dense imagematching point clouds”, J. Appl, Remote Sens, 2017, vol. 11,pp. 1-22.
  21. X. Pan and J. Zhao, “A central-point-enhancedconvolutional neural network for high-resolution remote-sensing image classification”, Int. J, Remote Sens, 2017, vol.38, pp. 6554-6581.
  22. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutionalnetworks for semantic segmentation”, In Proceedings of theIEEE Conference on Computer Vision and PatternRecognition (CVPR), Boston, MA, USA, 5-7 June 2015, pp.3431-3440.
  23. J. Sherrah, “Fully convolutional networks for densesemantic labeling of high-resolution aerial imagery”, arXiv,2016, arXiv:1606.02585.
  24. N. Audebert, B. Le Saux, and S. Lefè vre, “Beyond RGB:Very high resolution urban remote sensing with multimodaldeep networks”, ISPRS J. Photogramm, Remote Sens, 2018, vol. 140, pp. 20-32.
  25. X. Sun, S. Shen, X. Lin, and Z. Hu, “Semantic Labeling of High Resolution Aerial Images Using an Ensemble of Fully Convolutional Networks”, J. Appl, Remote Sens, 2017, vol.11, p. 042617.
  26. E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez,“High-resolution aerial image labeling with convolutionalneural networks”, IEEE Trans. Geosci, Remote Sens, 2017,vol. 55, pp. 7092-7103.
  27. O. Ronneberger, P. Fischer, and T. Brox, “U-net:Convolutional networks for biomedical imagesegmentation”, In Proceedings of the InternationalConference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5-9 October 2015,Springer: Cham, Switzerland, 2015, pp. 234-241.
  28. S. Ioffe and C. Szegedy, “Batch Normalization:Accelerating Deep Network Training by Reducing InternalCovariate Shift”, arXiv, 2015, arXiv:1502.03167.
  29. M. Lavreniuk, N. Kussul, and A. Novikov “Deep learningcrop classification approach based on coding input satellitedata into the unified hyperspace”, 38th InternationalConference on Electronics and Nanotechnology (ELNANO), 2018, pp. 239-244, doi: 10.1109/ELNANO.2018.8477525.
  30. M. Lavreniuk, N. Kussul, and A. Novikov “Deep LearningCrop Classification Approach Based on Sparse Coding of Time Series of Satellite Data”, In IGARSS 2018-2018 IEEEInternational Geoscience and Remote Sensing Symposium,2018, pp. 4812-4815, doi: 10.1109/IGARSS.2018.8518263.
  31. A. Shelestov, H. Yailymova, B. Yailymov, L. Shumilo, andA. Lavreniuk “Extension of Copernicus Urban Atlas to non-european countries”, 2021 IEEE International Geoscienceand Remote Sensing Symposium (IGARSS), 2021, Brussels(virtual format), pp. 6789-6792, doi:10.1109/IGARSS47720.2021.9553546.[32]G. M. Bakan and N. N. Kussul, “Fuzzy ellipsoidal filteringalgorithm of static object state”, Problemy Upravleniya IInformatiki (Avtomatika), 1996, no. 5, pp. 77-92.[33]A. N. Kravchenko, N. N. Kussul, E. A. Lupian,V.P. Savorsky, L. Hluchy, and A. Y. Shelestov, “Waterresource quality monitoring using heterogeneous data andhigh-performance computations”, Cybernetics and SystemsAnalysis, 2008, vol. 44(4), 616-624. doi:10.1007/s10559-008-9032-x.
  32. N. Kussul, A. Shelestov, B. Yailymov, H. Yailymova, M.Lavreniuk, L. Shumilo, and Y. Bilokonska, “Cropmonitoring technology based on time series of satelliteimagery”, 2020 IEEE 11th International Conference onDependable Systems, Services and Technologies,DESSERT 2020, May 2020, pp. 346-350.
  33. N. Kussul, A. Shelestov, H. Yailymova, B. Yailymov, M.Lavreniuk, M. Ilyashenko, “Satellite AgriculturalMonitoring in Ukraine at Country Level: World BankProject”, 2020 IEEE International Geoscience and RemoteSensing Symposium, IGARSS 2020, 26 September 2020,pp. 1050-1053.
  34. A. Shelestov, A. Kolotii, S. Skakun, B. Baruth, R.L. Lozano,and B. Yailymov, “Biophysical parameters mapping withinthe SPOT-5 take 5 initiative”, European Journal of RemoteSensing, vol. 50, Issue 1, 2017, pp. 300-309.
  35. N. Kussul, A. Kolotii, A. Shelestov, B. Yailymov, andM. Lavreniuk, “Land degradation estimation from globaland national satellite based datasets within un program”,2017 IEEE 9th International Conference on Intelligent DataAcquisition and Advanced Computing Systems:Technology and Applications, IDAACS 2017, vol. 1, 3November 2017, pp. 383-386.



       - Committees
       - Proceedings


       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports





           ISSN 2199-8876
           Copyright © 2013-2021 Leonid Mylnikov, © 2022 at Anhalt University of Applied Sciences. All rights reserved.