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A New Scheme for Land Cover Classification in Aerial Images: Combining Extended Dependency Tree-HMM and Unsupervised Segmentation

  • Mohamed El Yazid BoudarenEmail author
  • Abdel Belaïd
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 60)

Abstract

An important challenge to any image pixels classification system is to correctly assign each pixel to its proper class without blurring edges delimiting neighboring regions. In this paper, we present an aerial image mapping approach that advantageously combines unsupervised segmentation with a supervised Markov model based recognition. The originality of the proposed system carries on three concepts: the introduction of an auto-adaptive circular-like window size while applying our stochastic classification to preserve region edges, the extension of the Dependency Tree–HMM to permit the computation of likelihood probability on windows of different shapes and sizes and a mechanism that checks the coherence of the indexing by integrating both segmentations results: from unsupervised over segmentation, regions are assigned to the predominating class with a focus on inner region pixels. To validate our approach, we achieved experiments on real world high resolution aerial images. The obtained results outperform those obtained by supervised classification alone.

Keywords

Land cover classification hidden Markov model aerial images 

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Applied Maths Lab., Military Polytechnic SchoolAlgiersAlgeria
  2. 2.LORIA Lab., Read TeamVandoeuvre-lès-NancyFrance

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