Robust and Reliable Technique of Automatic Building Extraction from High Resolution Imagery

  • Arvind PandeyEmail author
  • Mriganka Shekhar Sarkar
  • Gajendra Singh
  • Sarita Palni
  • Nisha Chand
  • Manish Kumar
Part of the Advances in Geographical and Environmental Sciences book series (AGES)


The automation in man-made object extraction such as building habitation from urban area imagery has become a challenging task for photogrammetry, computer vision, and remote sensing. This study aims to automatically extract building of an urban area using high resolution intensity data and fuzzy membership logic to classify the image object by using e-Cognition software. The object oriented method was implemented and high resolution Quick-Bird imagery was used for automatic building extraction in Dehradun city of Uttarakhand district, India. We have further evaluated the performance of this automated extracted building feature by using accuracy completeness (89.74%), correctness (94.50%), and the quality (85.29%). The study however, clearly shows that the segmentation-based classification is much superior to the traditional per-pixel methods mainly used on high resolution images. It also shows that high spatial resolution satellite data and appropriate data processing play not only an important role in modern urban planning but also reduce the cost of manpower and saves time.


e-cognition Automatic building extraction Fuzzy logic Photogrammetry 



The authors would like to thank Dr. Durgesh Panth, Director of Uttarakhand Space Application Center (USAC), Uttarakhand for providing funds to carry out this short-term research. We thank Mr. Shashank Lingwal, Scientist, USAC for providing support during this study.


  1. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46CrossRefGoogle Scholar
  2. Dash J, Steinle E, Singh RP, Bähr HP (2004) Automatic building extraction from laser scanning data: an input tool for disaster manage. Adv Space Res 33(3):317–322CrossRefGoogle Scholar
  3. Eidenbenz C, Kaeser C, Baltsavias E (2000) ATOMI–automated reconstruction of topographic objects from aerial images using vectorized map information. Int Arch Photogr Remote Sens 33(B3):462–471Google Scholar
  4. Hu X, Zhang Z, Tao CV (2004) A robust method for semi-automatic extraction of road centerlines using a piecewise parabolic model and least square template matching. Photogr Eng Remote Sens 70(12):1393–1398CrossRefGoogle Scholar
  5. Hongjian Y, Shiqiang Z (2006) 3D building reconstruction from aerial CCD image and sparse laser sample data. Opt Lasers Eng 44(6):555–566CrossRefGoogle Scholar
  6. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331CrossRefGoogle Scholar
  7. Lafarge F, Descombes X, Zerubia J, Pierrot-Deseilligny M (2010) Structural approach for building reconstruction from a single DSM. IEEE Trans Pattern Anal Mach Intell 32(1):135–147CrossRefGoogle Scholar
  8. Lam KM, Yan H (1994) Fast greedy algorithm for active contours. Electron Lett 30(1):21–23Google Scholar
  9. Long W, Srihann S (2004) Land cover classification of SSC image: unsupervised and supervised classification using ERDAS imagine. In: IEEE international proceedings of geosci and remote sensor symposium, 2004. IGARSS’04, vol 4. IEEE, pp 2707–2712Google Scholar
  10. Peng J, Zhang D, Liu Y (2005) An improved snake model for building detection from urban aerial images. Pattern Recogn Lett 26(5):587–595CrossRefGoogle Scholar
  11. Samadzadegan F, Azizi A, Hahn M, Lucas C (2005) Automatic 3D objects recognition and reconstruction based on neuro-fuzzy modeling. ISPRS J Photogr Remote Sens 59(5):255–277CrossRefGoogle Scholar
  12. Shih FY, Zhang K (2004) Efficient contour detection based on improved snake model. Int J Pattern Recogn Artif Intell 18(02):197–209CrossRefGoogle Scholar
  13. Sohn G, Dowman IJ (2002) Terrain surface reconstruction by the use of tetrahedron model with the MDL criterion. Int Arch Photogr Remote Sens Spat Info Sci 34(3/A):336–344Google Scholar
  14. Zhang Q, Pavlic G, Chen W, Fraser R, Leblanc S, Cihlar J (2005) A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery. Comput Geosci 31(3):289–296CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Arvind Pandey
    • 1
    Email author
  • Mriganka Shekhar Sarkar
    • 1
  • Gajendra Singh
    • 1
  • Sarita Palni
    • 2
  • Nisha Chand
    • 2
  • Manish Kumar
    • 3
  1. 1.Uttarakhand Space Application CenterDehradunIndia
  2. 2.Kumaun UniversityNainitalIndia
  3. 3.Department of Geography, Kalindi CollegeUniversity of DelhiNew DelhiIndia

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