Geomatics and Environmental Engineering, vol. 15, no. 4

Issue

Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

Date Log

Submitted
May 6, 2021
Accepted
Sep 22, 2021
Published
Oct 8, 2021
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

https://doi.org/10.7494/geom.2021.15.4.101
Lamyaa Gamal El-deen Taha
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
https://orcid.org/0000-0003-2890-2498
Rania Elsayed Ibrahim
National Authority for Remote Sensing and Space Sciences, Cairo, Egypt
http://orcid.org/0000-0001-5116-5866

Corresponding Author(s) : Rania Elsayed Ibrahim

ranyaalsayed@gmail.com

Geomatics and Environmental Engineering, Vol. 15 No. 4 (2021): Geomatics and Environmental Engineering

Abstract

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.
The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

Keywords

ensemble classifiers machine learning random forest maximum likelihood support vector machines backpropagation image classification
Taha, L. G. E.- deen, & Ibrahim, R. E. (2021). Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images. Geomatics and Environmental Engineering, 15(4), 101–116. https://doi.org/10.7494/geom.2021.15.4.101
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. Dahiya S., Garg P.K., Jat M.K., Garg P.K.: Building Extraction from High-Resolution Satellite Images Using Matlab Software Building Extraction from High-resolution Satellite Images. [in:] 14th International Multidisciplinary Scientific Geo-Conference and Expo 2014 (SGEM 2014): Albena, Bulgaria, 17–26 June 2014, Book 2, Vol. 3, Curran, 2014, pp. 71–78. https://doi.org/10.5593/SGEM2014/B23/S10.009.
  2. Liu W., Prinet V.: Building Detection from High-resolution Satellite Image Using Probability Model. [in:] Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05, IEEE, 2005, pp. 3888–3891. https://doi.org/10.1109/IGARSS.2005.1525759.
  3. Zhang A., Liu X., Gros A., Tiecke T.: Building Detection from Satellite Images on a Global Scale. arXiv, 1707.08952, 2017.
  4. Xu Y., Wu L., Xie Z., Chen Z., Xu Y., Wu L., Xie Z., Chen Z.: Building Extraction in Very High-Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters. Remote Sensing, vol. 10(1), 2018, 144. https://doi.org/10.3390/rs10010144.
  5. Aamir M., Pu Y.F., Rahman Z., Tahir M., Naeem H., Dai Q.: A Framework for Automatic Building Detection from Low-Contrast Satellite Images. Symmetry, vol. 11(1), 2019, 3. https://doi.org/10.3390/sym11010003.
  6. Dronova I., Gong P., Wang L.: Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sensing of Environment, vol. 115(12), 2011, pp. 3220–3236. https://doi.org/10.1016/j.rse.2011.07.006.
  7. Matinfar H.R., Sarmadian F., AlaviPanah S.K., Heck R.J.: Comparisons of object-oriented and pixel-based classification of land use/land cover types based on lansadsat7, ETM+ spectral bands (case study: arid region of Iran). American-Eurasian Journal of Agricultural & Environmental Sciences, vol. 2(4), 2007, pp. 448–456.
  8. Yang J., Shi Z.K., Wu Z.Y.: Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images. International Journal of Automation and Computing, vol. 13(4), 2016, pp. 338–349. http://dx.doi.org/10.1007/s11633-016-0965-7.
  9. Bassier M., Genechten B.V., Vergauwen M.: Classification of sensor independent point cloud data of building objects using random forests. Journal of Building Engineering, vol. 21, 2019, pp. 468–477. https://doi.org/10.1016/j.jobe.2018.04.027.
  10. Lan T., Hu H., Jiang C., Yang G., Zhao Z.: A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Advances in Space Research, vol. 65(8), 2020, pp. 2052–2061. https://doi.org/10.1016/j.asr.2020.01.036.
  11. Hu Q., Zhen L., Mao Y., Zhou X., Zhou G.: Automated building extraction using satellite remote sensing imagery. Automation in Construction, vol. 123, 2021, 103509. https://doi.org/10.1016/j.autcon.2020.103509.
  12. Yuan J.: Learning Building Extraction in Aerial Scenes with Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 11, 2018, pp. 2793–2798. https://doi.org/10.1109/TPAMI.2017.2750680.
  13. Alwan I.A., Aziz N.A.: An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq. Geomatics and Environmental Engineering, vol. 15, no. 1, 2021, pp. 5–21. https://doi.org/10.7494/geom.2021.15.1.5.
  14. Hsu C.-W., Chang C.-C., Lin C.-J.: A Practical Guide to Support Vector Classification. National Taiwan University, 2010. http://ntu.csie.org/~cjlin/papers/guide/guide.pdf [access: 5.03.2020].
  15. Salah M., Trinder J.C., Shaker A., Hamed M., Elsagheer A.: Integrating Multiple Classifiers with Fuzzy Majority Voting for Improved Land Cover Classification. [in:] Paparoditis N., Pierrot-Deseilligny M., Mallet C., Tournaire O. (eds.), ISPRS Commission III Symposium PCV 2010: Photogrammetric Computer Vision and Image Analysis: Saint-Mandé, France 1–3 September 2010, vol. XXXVIII, Part 3A, International Society for Photogrammetry and Remote Sensing, 2010, pp. 7–12.
  16. Haykin S.: Neural Networks: A Comprehensive Foundation. Macmillan, 1994.
  17. Turker M., Koc-San D.: Building extraction from high‑resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. International Journal of Applied Earth Observation and Geoinformation, vol. 34, 2015, pp. 58–69. https://doi.org/10.1016/j.jag.2014.06.016.
  18. Lari Z., Ebadi H.: Automatic extraction of buildings features from high resolution satellite images using artificial neural networks. [in:] Proceedings of the ISPRS Commission VII Conference on Information Extraction from SAR and Optical Data, with Emphasis on Developing Countries. Istanbul, Turkey, 16–18 May, 2007.
  19. Shoaib M.H., Mostafa Y.G., Abbas Y.A.: Buildings extraction from very high‑resolution satellite images for map updating in Egypt. Journal of Engineering Sciences, vol. 48(5), pp. 869–887, 2020. https://doi.org/10.21608/jesaun.2020.115673.
  20. Xu B., Ye Y., Nie L.: An Improved Random Forest Classifier for Image Classification. [in:] 2012 IEEE International Conference on Information and Automation, IEEE, 2012, pp. 795–800. https://doi.org/10.1109/ICInfA.2012.6246927.
  21. Walton J.T.: Subpixel urban land cover estimation. Photogrammetric Engineering and Remote Sensing, vol. 74(10), 2008, pp. 1213–122. http://dx.doi.org/10.14358/PERS.74.10.1213.
  22. Zhou P., Chang Y.: Automated classification of building structures for urban built environment identification using machine learning. Journal of Building Engineering, vol. 43, 2021, 103008. https://doi.org/10.1016/j.jobe.2021.103008.
  23. Poli D., Remondino F., Angiuli E., Agugiaro G.: Radiometric and geometric evaluation of GeoEye-1, WorldView-2. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 100, 2015, pp. 35–47. https://doi.org/10.1016/j.isprsjprs.2014.04.007.
  24. Onojeghuo A.O., Blackburn G.A.: Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats. Remote Sensing of Environment, vol. 115(8), 2011, pp. 2025–2034. http://dx.doi.org/10.1016/j.rse.2011.04.004.
  25. Evans F.: An investigation into the use of maximum likelihood classifiers, decision trees, neural networks and conditional probabilistic networks for mapping and predicting salinity. Curtin University of Technology, 1998 [M.Sc. thesis].
  26. Lu D., Weng Q.: Use of impervious surface in urban land-use classification. Remote Sensing of Environment, vol. 102(1–2), 2006, pp. 146–160. https://doi.org/10.1016/j.rse.2006.02.010.
  27. Yuhendra K.H., Sumantyo J.T.S., Kuze H.: Performance analyzing of high resolution Pan-Sharpening techniques: Increasing image quality for classification using supervised kernel support vector machine. Research Journal of Information Technology, vol. 3(1), 2011, pp. 12–23. https://dx.doi.org/10.3923/rjit.2011.12.23.
  28. Rao K.V.R., Kumar P.R.: Land Cover Classification Using Sentinel-1 SAR Data. International Journal for Research in Applied Science and Engineering Technology, vol. 5, no. 12, 2017, pp. 1054–1060.
  29. Zhang T., Su J., Liu C., Chen W., Liu H., Liu G.: Band selection in sentinel-2 satellite for agriculture applications. [in:] 2017 23rd International Conference on Automation and Computing (ICAC), IEEE, 2017, pp. 1–6. https://dx.doi.org/10.23919/IConAC.2017.8081990.
  30. Mohamed A.E.: Comparative Study of Four Supervised Machine Learning Techniques for Classification. International Journal of Applied Science and Technology, vol. 7, no. 2, 2017, pp. 5–18.
  31. Rumelhart D.E., Hinton G.E., Williams R.J.: Learning Internal Representations by Error Propagation. [in:] Rumelhart D.E., McClelland J.L., PDP Research Group (eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, MIT Press, 1987, pp. 318–362.
  32. Weng Q.: Remote sensing of impervious surfaces in the urban areas: requirements, methods and trend. Remote Sensing of Environment, vol. 117, 2012, pp. 34–49. https://doi.org/10.1016/j.rse.2011.02.030.
  33. Breiman L.: Random forests. Machine Learning, vol. 45, 2001, pp. 5–32. https://doi.org/10.1023/A:1010933404324.
  34. Filippi A.M., Jensen J.R.: Fuzzy learning vector quantization for hyperspectral coastal vegetation classification. Remote Sensing of Environment, vol. 100(4), 2006, pp. 512–530. https://doi.org/10.1016/j.rse.2005.11.007.
  35. Xiao H., Zhang X., Du Y.: A Comparison of neural network, rough sets and support vector machine on remote sensing image classification. [in:] Advances on applied computer & applied computational science: proceedings of the 7th WSEAS International Conference on Applied Computer & Applied Computational Science (ACACOS ’08): Hangzhou, China, April 6–8, 2008, WSEAS Press, 2008.
  36. Nelson M.: Evaluating Multitemporal Sentinel-2 data for Forest Mapping using Random Forest. Physical Geography and Quaternary Geology, 2017 [M.Sc. thesis].
  37. Fouedjio F.: Exact Conditioning of Regression Random Forest for Spatial Prediction. Artificial Intelligence in Geosciences, vol. 1, 2020, pp. 11–23. https://doi.org/10.1016/j.aiig.2021.01.001.
  38. Akar Ö., Güngör O.: Classification of multispectral images using Random Forest algorithm. Journal of Geodesy and Geoinformation, vol. 1, no. 2, 2012, pp. 105–112. https://doi.org/10.9733/jgg.241212.1.
  39. Deus D.: Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data. Advances in Remote Sensing, vol. 7, no. 2, 2018, pp. 47–60. https://doi.org/10.4236/ars.2018.72004.
  40. Ming D., Zhou T., Wang M., Tan T.: Land cover classification using random forest with genetic algorithm-based parameter optimization. Journal of Applied Remote Sensing, vol. 10, no. 3, 2016, 035021. https://doi.org/10.1117/1.JRS.10.035021.
  41. Goldblatt R., Deininger K., Hanson G.: Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam. Development Engineering, vol. 3, 2018, pp. 83–99. https://doi.org/10.1016/j.deveng.2018.03.001.
  42. Jhonnerie R., Siregar V.P., Nababan B., Prasetyo L.B., Wouthuyzen S.: Random forest classification for mangrove land cover mapping using Landsat 5 TM and ALOS PALSAR imageries. Procedia Environmental Sciences, vol. 24, 2015, pp. 215–221. https://doi.org/10.1016/j.proenv.2015.03.028.
  43. Wurm M., d’Angelo P., Reinartz P., Taubenböck H.: Investigating the Applicability of Cartosat-1 DEMs and Topographic Maps to Localize Large-Area UrbanMass Concentrations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, 2014, pp. 4138–4152. https://doi.org/10.1109/JSTARS.2014.2346655.
  44. Gavankar N.L., Ghosh S.K.: Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters. Geocarto International, vol. 34, no. 6, 2018, pp. 626–643. https://doi.org/10.1080/10106049.2018.1425736.
  45. Dang S.K., Singh K.: Predicting tensile-shear strength of nugget using M5P model tree and random forest: An analysis. Computers in Industry, vol. 124, 2021, 103345. https://doi.org/10.1016/j.compind.2020.103345.
  46. Guo L., Chehata N., Mallet C., Boukir S.: Relevance of airborne LiDAR and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66(1), 2011, pp. 56–66. https://doi.org/10.1016/j.isprsjprs.2010.08.007.
  47. Tooke T.R., Coops N.C., Webster J.: Predicting building ages from LiDAR data with random forests for building energy modeling. Energy and Buildings, vol. 68, Part A, 2014, pp. 603–610. https://doi.org/10.1016/j.enbuild.2013.10.004.
  48. He X., Zhang X., Xin Q.: Recognition of building group patterns in topographic maps based on graph partitioning and random forest. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 136, 2018, pp. 26–40. https://doi.org/10.1016/j.isprsjprs.2017.12.001.
Read More
Author biographies is not available.
Download this PDF file
PDF
Statistic
Read Counter : 292 Download : 351

Downloads

Download data is not yet available.