Agriculture Parcel Boundary Detection from Remotely Sensed Images

  • Ganesh KhadangaEmail author
  • Kamal Jain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)


The object-based image analysis (OBIA) is extensively used nowadays for classification of high-resolution satellite images (HRSI). In OBIA, the analysis is based on a group of pixels known as objects. It differs from the traditional pixels-based methodology, where individual pixels are analyzed. In OBIA, the image analysis consists of image segmentation, object attribution, and classification. The segmentation process thus identifies a group of pixels and are known as objects. These objects are taken for further analysis. Thus segmentation is an important step in OBIA. In order to find out the boundary of agriculture parcels, a two-step process is followed. First, the segmentation of the images is done using the statistical region merging (SRM) technique. Then the boundary information and center of the segmentation are found out using MATLAB. The best fit segment was found out using trial and errors. The extracted boundary information is very encouraging and it matches the parcel boundaries recorded in revenue registers. The completeness and precision analysis of the plots are also quite satisfactory.


MATLAB Segmentation Cadastral parcel OBIA HRSI 


  1. 1.
    Baatz, M., Schäpe, A.: Multiresolution segmentation—an optimisation approach for high quality multi-scale image segmentation. AGIT Symposium, Salzburg (2000) Google Scholar
  2. 2.
    Babawuro, U., Beiji, Z.: Satellite imagery cadastral features extractions using image processing algorithms: a viable option for cadastral science. Int J Comput Sci Issues (IJCSI) 9(4), 30 (2012)Google Scholar
  3. 3.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65, 2–16 (2010)CrossRefGoogle Scholar
  4. 4.
    Blaschke, T., Strobl, J.: What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. Zeitschrift fu¨r Geoinformations systeme 6, 12–17 (2001)Google Scholar
  5. 5.
    Castilla, G., Hay, G.J.: Image-objects and Geographic Objects. In: Blaschke, T., Lang, S., Hay, G. (eds.) Object-Based Image Analysis, pp. 91–110. Springer, Heidelberg, Berlin, New York (2008)CrossRefGoogle Scholar
  6. 6.
    Dey, V., Zhang, Y., Zhongm, M.: A review of image segmentation techniques with remote sensing perspective. In: ISPRS, Vienna, Austria, vol. XXXVIII, July 2010Google Scholar
  7. 7.
    eCognition User and Reference ManualGoogle Scholar
  8. 8.
    Fockelmann, R.: Agricultural parcel detection with Definiens eCognition. Earth observation Case Study, GAF, Germany (2001)Google Scholar
  9. 9.
    Hay, G.J., Castilla, G.: Object-based image analysis: strength, weakness, opportunities, and threats (SWOT). In: 1st International Conference on Object-Based Image Analysis (OBIA 2006), Salzburg, Austria, 4–5 July 2006 Google Scholar
  10. 10.
    Haitao, L., Haiyan, G., Yanshun, H., Jinghui, Y.: An efficient multiscale SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule) segmentation method for high-resolution remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2(2) (2009)Google Scholar
  11. 11.
    Jung, R.W.: Deriving GIS-ready thematic mapping information from remotely sensed maps using Object-Oriented Image Analysis Techniques. In: ASPRS/MAPPS Conference, San Antonia, Texas (2009)Google Scholar
  12. 12.
    Marpu, P.R., Neubert, M., Herold, H., Niemeyer, I.: Enhanced evaluation of image segmentation results. J. Spat. Sci. 55(1), 55–68 (2010)CrossRefGoogle Scholar
  13. 13.
    Navulur, K.: Multispectral Image Analysis Using the Object-Oriented Paradigm. CRC Press (2007)Google Scholar
  14. 14.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11) (2004)Google Scholar
  15. 15.
    Singh, P.P., Garg, R.D.: A Hybrid approach for information extraction from high resolution satellite imagery. Int. J. Image Graph. 13(2), 1340007(1–16) (2013)Google Scholar
  16. 16.
    Singh, P.P., Garg, R.D.: Information extraction from high resolution satellite imagery using integration technique. In: Intelligent Interactive Technologies and Multi-media, CCIS, vol. 276, pp. 262–271 (2013)Google Scholar
  17. 17.
    Singh, P.P., Garg, R.D.: Land use and land cover classification using satellite imagery: a hybrid classifier and neural network approach. In: Proceedings of International Conference on Advances in Modeling, Optimization and Computing, IIT Roorkee, pp. 753–762 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Civil Engineering Department, Geomatics GroupIndian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations