Machine Learning on Historic Air Photographs for Mapping Risk of Unexploded Bombs

  • Stefano Merler
  • Cesare Furlanello
  • Giuseppe Jurman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

We describe an automatic procedure for building risk maps of unexploded ordnances (UXO) based on historic air photographs. The system is based on a cost-sensitive version of AdaBoost regularized by hard point shaving techniques, and integrated by spatial smoothing. The result is a map of the spatial density of craters, an indicator of UXO risk.

Keywords

False Alarm Near Neighbor Spatial Smoothing Ground Resolution Rejection Threshold 
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 2005

Authors and Affiliations

  • Stefano Merler
    • 1
  • Cesare Furlanello
    • 1
  • Giuseppe Jurman
    • 1
  1. 1.ITC-irstTrentoItaly

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