Learning-Based Fuzzy Fusion of Multiple Classifiers for Object-Oriented Classification of High Resolution Images

  • Rajeswari Balasubramaniam
  • Gorthi R. K. Sai Subrahmanyam
  • Rama Rao Nidamanuri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)


In remote-sensing, multi-classifier systems (MCS) have found its use for efficient pixel level image classification. Current challenge faced by the RS community is, classification of very high resolution (VHR) satellite/aerial images. Despite the abundance of data, certain inherent difficulties affect the performance of existing pixel-based models. Hence, the trend for classification of VHR imagery has shifted to object-oriented image analysis (OOIA) which work at object level. We propose a shift of paradigm to object-oriented MCS (OOMCS) for efficient classification of VHR imagery. Our system uses the modern computer vision concept of superpixels for the segmentation stage in OOIA. To this end, we construct a learning-based decision fusion method for integrating the decisions from the MCS at superpixel level for the classification task. Upon detailed experimentation, we show that our method exceeds in performance with respect to a variety of traditional OOIA decision systems. Our method has also empirically outperformed under conditions of two typical artefacts, namely unbalanced samples and high intra-class variance.


Multi-classifier system Object-oriented image analysis Segmentation Superpixels Classification Fusion 



The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [3].


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Blaschke, T., Lang, S., Lorup, E., Strobl, J., Zeil, P.: Object-oriented image processing in an integrated gis/remote sensing environment and perspectives for environmental applications. Environmental information for planning, politics and the public 2, 555–570 (2000)Google Scholar
  3. 3.
    Cramer, M.: The dgpf-test on digital airborne camera evaluation–overview and test design. Photogrammetrie-Fernerkundung-Geoinformation 2010(2), 73–82 (2010)CrossRefGoogle Scholar
  4. 4.
    Fulkerson, B., Vedaldi, A., Soatto, S., et al.: Class segmentation and object localization with superpixel neighborhoods. In: ICCV. vol. 9, pp. 670–677. Citeseer (2009)Google Scholar
  5. 5.
    Nussbaum, S., Menz, G.: eCognition Image Analysis Software, pp. 29–39. Springer Netherlands, Dordrecht (2008)Google Scholar
  6. 6.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms. (2008)
  7. 7.
    Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. In: Computer vision–ECCV 2008, pp. 705–718. Springer (2008)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Rajeswari Balasubramaniam
    • 1
  • Gorthi R. K. Sai Subrahmanyam
    • 2
  • Rama Rao Nidamanuri
    • 1
  1. 1.Indian Institute of Space Science and TechnologyThiruvananthapuramIndia
  2. 2.Indian Institute of TechnologyTirupatiIndia

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