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Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

  • P.-A. HerraultEmail author
  • D. Sheeren
  • M. Fauvel
  • M. Paegelow
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

In this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical ‘Map of France’. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map.

Keywords

Color Space Post Processing Step Dilatation Operator Unsupervised Classification Morphological Filter 
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.

Notes

Acknowledgments

This research was supported by the French National Research Agency (ANR JCJC MODE-RESPYR 2010 1804 01–01). P.-A. Herrault is also funded through a PRES Toulouse University and Region Midi-Pyrenees grant.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • P.-A. Herrault
    • 1
    • 2
    Email author
  • D. Sheeren
    • 1
  • M. Fauvel
    • 1
  • M. Paegelow
    • 2
  1. 1.University of ToulouseToulouseFrance
  2. 2.University of ToulouseToulouseFrance

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