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Automated Extraction of Linear Features from Aerial Imagery Using Kohonen Learning and GIS Data

  • Peter Doucette
  • Peggy Agouris
  • Mohamad Musavi
  • Anthony Stefanidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1737)

Abstract

An approach to semi-automated linear feature extraction from aerial imagery is introduced in which Kohonen’s self-organizing map (SOM) algorithm is integrated with existing GIS data. The SOM belongs to a distinct class of neural networks which is characterized by competitive and unsupervised learning. Using radiometrically classified image pixels as input, appropriate SOM network topologies are modeled to extract underlying spatial structures contained in the input patterns. Coarse-resolution GIS vector data is used for network weight and topology initialization when extracting specific feature components. The Kohonen learning rule updates the synaptic weight vectors of winning neural units that represent 2-D vector shape vertices. Experiments with high-resolution hyperspectral imagery demonstrate a robust ability to extract centerline information when presented with coarse input.

Keywords

Linear Feature Probabilistic Neural Network Active Contour Model Neighborhood Function Pacific Northwest National Laboratory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Peter Doucette
    • 1
  • Peggy Agouris
    • 1
  • Mohamad Musavi
    • 2
  • Anthony Stefanidis
    • 1
  1. 1.Department of Spatial Information Science & EngineeringUniversity of Maine Boardman HallOrono
  2. 2.Department of Electrical and Computer EngineeringUniversity of MaineOrono

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