A Hybrid Approach to Land Cover Classification from Multi Spectral Images

  • Primo Zingaretti
  • Emanuele Frontoni
  • Eva Savina Malinverni
  • Adriano Mancini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The specific objective of this paper is to describe a new methodology for producing really exploitable results from automatic classification algorithms. Input data are basically constituted by multispectral images, integrated with textural and contextual measures. The output is constituted by an image with each pixel assigned to one out of 15 classes at the second level of the CLC legend or let unclassified (somehow a better solution than a classification error), plus a stability map that helps users to separate the regions classified with high accuracy from those whose classification result should be verified before being used.


Land cover - land use (LCLU) multispectral images pixel-based and object-based classification AdaBoost stability map 


  1. 1.
    Ball, J.E., Bruce, L.M.: Level set segmentation of remotely sensed hyperspectral images. In: Proceedings of Int. Geoscience and Remote Sensing Symposium, vol. 8, pp. 5638–5642Google Scholar
  2. 2.
    European Environment Agency (EEA), CLC 2006 technical guidelines - Technical report,
  3. 3.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences 55(1), 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Landgrebe, D.A.: Signal Theory Methods in Multispectral Remote Sensing. John Wiley & Sons, Inc., Hoboken (2003)CrossRefGoogle Scholar
  5. 5.
    Li, P., Xiao, X.: Multispectral image segmentation by a multichannel watershed-based approach. International Journal of Remote Sensing 28(19), 4429–4452 (2007)CrossRefGoogle Scholar
  6. 6.
    Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999)CrossRefzbMATHGoogle Scholar
  7. 7.
    Shackelford, A.K., Davis, C.H.: A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Transactions on Geoscience and Remote Sensing 41(10), 2354–2363 (2003)CrossRefGoogle Scholar
  8. 8.
    Sutton, C.D.: Handbook of Statistics, vol. 24, cap. 11: Classification and regression trees, bagging, and boosting. Elsevier, Amsterdam (2005)Google Scholar
  9. 9.
    Tabb, M., Ahuja, N.: Multiscale image segmentation by integrated edge and region detection. IEEE Transactions on Image Processing 6(5), 642–655 (1997)CrossRefGoogle Scholar
  10. 10.
    Wang, L., Sousa, W., Gong, P.: Integration of object-based and pixel-based classification for mangrove mapping with IKONOS imagery. Int. J. of Remote Sensing 25(24), 5655–5668 (2004)CrossRefGoogle Scholar
  11. 11.
    Wood, J.: Invariant pattern recognition: a review. Pattern Recognition 29(1), 1–17 (1996)CrossRefGoogle Scholar
  12. 12.
    Yu, Y.W., Wang, J.H.: Image segmentation based on region growing and edge detection. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, vol. 6, pp. 798–803 (1999)Google Scholar
  13. 13.
    Yuan, F., Bauer, M.E.: Mapping impervious surface area using high resolution imagery: A comarison of object-based and per pixel classification. In: Proceedings of ASPRS 2006 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Primo Zingaretti
    • 1
  • Emanuele Frontoni
    • 1
  • Eva Savina Malinverni
    • 2
  • Adriano Mancini
    • 1
  1. 1.D.I.I.G.A.Università Politecnica delle MarcheAnconaItaly
  2. 2.DARDUSUniversità Politecnica delle MarcheAnconaItaly

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