Abstract
The automatic classification of semantic classes (background, vegetation, roads, water, political boundaries, iso-contours) in raster map images still poses significant challenges. We describe and compare the results of three unsupervised classification algorithms: (1) k-means, (2) graph theoretic (GT), and (3) expectation maximization (EM). These are applied to USGS raster map images, and performance is measured in terms of the recall and precision as well as the cluster quality on a set of map images for which the ground truth is available. Across the six classes studied here, k-means achieves good clusters and an average of 78% recall and 70% precision; GT clustering achieves good clusters and 83% recall with 74% precision. Finally, EM forms very good clusters and has an average 86% recall and 71% precision.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This chapter is a modified version of “Automatic Segmentation of Semantic Classes in Raster Map Image,” [46] contributed by Thomas C. Henderson, Trevor Linton, Sergey Potupchik and Andrei Ostanin.
Bibliography
E. Ageenko and A. Podlasov. On the Restoration of Semantic Features in Raster Topographic Images. IADIS International Journal on Computer Science and Information Systems, 1(1):101–114, 2006.
D. Forsyth and J. Ponce. Computer Vision. Prentice Hall, Upper Saddle River, NJ, 2003.
T.C. Henderson and T. Linton. Raster Map Image Analysis. In Poster Session of International Conference on Document Analysis and Recognition, Catalonia, Spain, July 2009.
T.C. Henderson, T. Linton, S. Potupchik, and A. Ostanin. Automatic Segmentation of Semantic Classes in Raster Map Images. In IARP International Workshop on Graphics Recognition, La Rochelle, France, July 2009.
T. Linton. Semantic Feature Analysis in Raster Maps. Master’s thesis, University of Utah, Salt Lake City, Utah, August 2009.
A. Podlasov, E. Ageenko, and P. Fränti. Morphological Recontruction of Semantic Layers in Map Images. Journal of Electronic Imaging, 15(1), March 2006.
Y.-Y.Chiang and C.A. Knoblock. Classification of Line and Character Pixels on Raster Maps using Discrete Cosine Transformation Coefficients and Support Vector Machines. In Proceedings Intl Conference on Pattern Recognition, Washington, DC, USA, August 2006. IEEE Computer Society.
S. Zheng, J. Liu, W. Shi, and G. Shu. Road Central Contour Extraction from High Resolution Satellite Image using Tensor Voting Framework. In Proceedings of the 5th Intl Conference on Machine Learning and Cybernetics, pages 3248–3253, Guangzhou, China, August 2006.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Henderson, T.C. (2014). Other Semantic Feature Segmentation. In: Analysis of Engineering Drawings and Raster Map Images. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8167-7_8
Download citation
DOI: https://doi.org/10.1007/978-1-4419-8167-7_8
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-8166-0
Online ISBN: 978-1-4419-8167-7
eBook Packages: EngineeringEngineering (R0)