Optimizing Multiresolution Segmentation for Extracting Plastic Greenhouses from WorldView-3 Imagery

  • Manuel A. AguilarEmail author
  • Antonio Novelli
  • Abderrahim Nemamoui
  • Fernando J. Aguilar
  • Andrés García Lorca
  • Óscar González-Yebra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 76)


Multiresolution segmentation (MRS) has been pointed out as one of the most successful image segmentation algorithms within the object-based image analysis (OBIA) framework. The performance of this algorithm depends on the selection of three tuning parameters (scale, shape and compactness) and the bands combination and weighting considered. In this work, we tested MRS on a WorldView-3 bundle imagery in order to extract plastic greenhouse polygons. A recently published command line tool created to assess the quality of segmented digital images (AssesSeg), which implements a modified version of the supervised discrepancy measure named Euclidean Distance 2 (ED2), was used to select both the best aforementioned MRS parameters and the optimum image data source derived from WorldView-3 (i.e., panchromatic, multispectral and atmospherically corrected multispectral orthoimages). The best segmentation results were always attained from the atmospherically corrected multispectral WorldView-3 orthoimage.


Segmentation Multiresolution algorithm Object based image analysis WorldView-3 AssesSeg 



This work was supported by the Spanish Ministry of Economy and Competitiveness (Spain) and the European Union FEDER funds (Grant Reference AGL2014-56017-R). It takes part of the general research lines promoted by the Agrifood Campus of International Excellence ceiA3.


  1. 1.
    Carleer, A.P., Wolff, E.: Urban land cover multi-level region-based classification of VHR data by selecting relevant features. Int. J. Remote Sens. 27(6), 1035–1051 (2006)CrossRefGoogle Scholar
  2. 2.
    Stumpf, A., Kerle, N.: Object-oriented mapping of landslides using random forests. Remote Sens. Environ. 115, 2564–2577 (2011)CrossRefGoogle Scholar
  3. 3.
    Pu, R., Landry, S., Yu, Q.: Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery. Int. J. Remote Sens. 32(12), 3285–3308 (2011)CrossRefGoogle Scholar
  4. 4.
    Pu, R., Landry, S.: A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens. Environ. 124, 516–533 (2012)CrossRefGoogle Scholar
  5. 5.
    Aguilar, M.A., Saldaña, M.M., Aguilar, F.J.: GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments. Int. J. Remote Sens. 34(7), 2583–2606 (2013)CrossRefGoogle Scholar
  6. 6.
    Fernández, I., Aguilar, F.J., Aguilar, M.A., Álvarez, M.F.: Influence of data source and training size on impervious surface areas classification using VHR satellite and aerial imagery through an object-based approach. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 7(12), 4681–4691 (2014)Google Scholar
  7. 7.
    Heenkenda, M.K., Joyce, K.E., Maier, S.W.: Mangrove tree crown delineation from high-resolution imagery. Photogramm. Eng. Remote Sens. 81(6), 471–479 (2015)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS-J. Photogramm. Remote Sens. 65, 2–16 (2010)CrossRefGoogle Scholar
  10. 10.
    Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., van der Meer, F., van der Werff, H., van Coillie, F., Tiede, D.: Geographic object-based image analysis-towards a new paradigm. ISPRS-J. Photogramm. Remote Sens. 87, 180–191 (2014)CrossRefGoogle Scholar
  11. 11.
    Witharana, C., Civco, D.L.: Optimizing multi-resolution segmentation scale using empirical methods: exploring the sensitivity of the supervised discrepancy measure Euclidean Distance 2 (ED2). ISPRS-J. Photogramm. Remote Sens. 87, 108–121 (2014)CrossRefGoogle Scholar
  12. 12.
    Drǎgut, L., Csillik, O., Eisank, C., Tiede, D.: Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS-J. Photogramm. Remote Sens. 88, 119–127 (2014)CrossRefGoogle Scholar
  13. 13.
    Belgiu, M., Drǎguţ, L.: Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS-J. Photogramm. Remote Sens. 96, 67–75 (2014)CrossRefGoogle Scholar
  14. 14.
    Neubert, M., Herold, H., Meinel, G.: Assessing image segmentation quality –concepts, methods and application. In: Blaschke, T., Hay, G., Lang, S. (eds.) Object-Based Image Analysis – Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Lecture Notes in Geoinformation & Cartography, vol. 18, pp. 769–784. Springer, Berlin (2008)Google Scholar
  15. 15.
    Baatz, M., Schäpe, M.: Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (eds.) Angewandte Geographische Informations-Verarbeitung XII, pp. 12–23. Wichmann Verlag, Karlsruhe (2000)Google Scholar
  16. 16.
    Drǎgut, L., Tiede, D., Levick, S.: ESP: a tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data. ‎Int. J. Geogr. Inf. Sci. 24(6), 859–871 (2010)CrossRefGoogle Scholar
  17. 17.
    Novelli, A., Aguilar, M.A., Aguilar, F.J., Nemmaoui, A., Tarantino, E.: AssesSeg—a command line tool to quantify image segmentation quality: a test carried out in Southern Spain from satellite imagery. Remote Sens. 9, 40 (2017)CrossRefGoogle Scholar
  18. 18.
    Liu, Y., Biana, L., Menga, Y., Wanga, H., Zhanga, S., Yanga, Y., Shaoa, X., Wang, B.: Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS-J. Photogramm. Remote Sens. 68, 144–156 (2012)CrossRefGoogle Scholar
  19. 19.
    Novelli, A., Aguilar, M.A., Nemmaoui, A., Aguilar, F.J., Tarantino, E.: Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: a case study from Almería (Spain). Int. J. Appl. Earth Obs. Geoinf. 52, 403–411 (2016)CrossRefGoogle Scholar
  20. 20.
    Aguilar, M., Aguilar, F., García Lorca, A., Guirado, E., Betlej, M., Cichon, P., Nemmaoui, A., Vallario, A., Parente, C.: Assessment of multiresolution segmentation for extracting greenhouses from WorldView-2 imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XLI-B7, 145–152 (2016)CrossRefGoogle Scholar
  21. 21.
    Aguilar, M.A., Nemmaoui, A., Novelli, A., Aguilar, F.J., García Lorca, A.: Object-based greenhouse mapping using very high resolution satellite data and Landsat 8 time series. Remote Sens. 8, 513 (2016)CrossRefGoogle Scholar
  22. 22.
    Lefebvre, A., Corpetti, T., Moy, L.H.: Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory. In: Bruzzone, L. (ed.) Image and Signal Processing for Remote Sensing XVI, 78300E. SPIE, vol. 7830 (2010)Google Scholar
  23. 23.
    Witharana, C., Civco, D.L.: Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean Distance 2 (ED2). ISPRS J. Photogramm. Remote Sens. 87, 108–121 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Manuel A. Aguilar
    • 1
    Email author
  • Antonio Novelli
    • 2
  • Abderrahim Nemamoui
    • 1
  • Fernando J. Aguilar
    • 1
  • Andrés García Lorca
    • 3
  • Óscar González-Yebra
    • 1
  1. 1.Department of EngineeringUniversity of AlmeríaAlmeríaSpain
  2. 2.DICATEChPolitecnico di BariBariItaly
  3. 3.Department of GeographyUniversity of AlmeríaAlmeríaSpain

Personalised recommendations