Abstract
The objective of this research was the design and development of a region-based multi-scale segmentation algorithm with the integration of complex texture features, in order to provide a low level processing tool for object-oriented image analysis. The implemented algorithm is called Texture-based MSEG and can be described as a region merging procedure. The first object representation is the single pixel of the image. Through iterative pair-wise object fusions, which are made at several iterations, called passes, the final segmentation is achieved. The criterion for object merging is a homogeneity cost measure, defined as object heterogeneity, and computed based on spectral and shape features for each possible object merge. An integration of texture features to the region merging segmentation procedure was implemented through an Advanced Texture Heuristics module. Towards this texture-enhanced segmentation method, complex statistical measures of texture had to be computed based on objects, however, and not on rectangular image regions. The approach was to compute grey level co-occurrence matrices for each image object and then to compute object-based statistical features. The Advanced Texture Heuristics module, integrated new heuristics in the decision for object merging, involving similarity measures of adjacent image objects, based on the computed texture features. The algorithm was implemented in C++ and was tested on remotely sensed images of different sensors, resolutions and complexity levels. The results were satisfactory since the produced primitive objects, were comparable to those of other segmentation algorithms. A comparison between the simple algorithm and the texture-based algorithm results showed that in addition to spectral and shape features, texture features did provide good segmentation results.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Argenti F., Alparone L., Benelli G. (1990). Fast Algorithms for Texture Analysis Using Co-Occurrence Matrices. IEEE Proceedings, 137, F, 6, 1990, 443-448.
Argialas D., and Harlow C. (1990). Computational Image Interpretation Models: An Overview and a Perspective. Photogrammetric Engineering and Remote Sensing, Vol. 56, No 6, June, pp. 871-886.
Baatz M. and Schäpe A. (2000). Multiresolution Segmentation – an optimization approach for high quality multi-scale image segmentation. In: Strobl, J. et al. (eds.): Angewandte Geographische Infor-mationsverarbeitung XII. Wichmann, Heidelberg, pp. 12-23.
Benz U., Hoffman P., Willhauck G., Lingenfelder I., Heynen M., (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58 pp. 239-258.
Chen, J., Pappas, T.N., Mojsilovic, A., Rogowitz, B. (2002). Adaptive image segmentation based on color and texture. Proceedings International Conference on Image Processing. Evanston, IL, USA, 777- 780 vol.3
eCognition User Guide (2005), Definiens, Munchen. http://www.definiens-imaging.com
Fauzi M. FA. and Lewis, P. H. (2003). A Fully Unsupervised Texture Segmentation Algorithm. Harvey, R. and Bangham, J. A., Eds. Proceedings British Machine Vision Conference 2003, pages 519-528.
Haralick R.M., Shanmugan K., Dinstein I. (1973). Textural features for image classification. IEEE Trans. On Systems, Man and Cybernetics, 3(6):610-621, Nov. 1973.
Haralick R.M (1979). Statistical and Structural Approaches to Texture. Proceedings of the IEEE, Vol. 67, No. 5, May 1979, pp. 786-804.
Havlicek, J. P. and Tay, P.C. (2001). Determination of the number of texture segments using wavelets. Electronic Journal of Differential Equations, Conf. pp 61–70.
Liapis, S., Alvertos, N., Tziritas, G. (1998). Unsupervised Texture Segmentation using Discrete Wavelet Frames. IX European Signal Processing Conference, Sept. 1998, pp. 2529-2532
Materka A., Strzelecki M. (1998). Texture Analysis Methods – A Review, Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels 1998
Pal, N. R. and Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, vol. 26, pp. 1277-1294.
Sonka, M., Hlavac, V. Boyle, R., (1998). Image Processing, Analysis, and Machine Vision - 2nd Edition, PWS, Pacific Grove, CA, 800 p., ISBN 0-534-95393-X.
Schroder M., Dimai A. (1998). Texture Information in Remote Sensing Images: A Case Study. Workshop on Texture Analysis, WTA 1998, Freiburg, Germany.
Sukissian L., Kollias S., Boutalis Y. (1994). Adaptive Classification of Textured Images Using Linear Prediction and Neural Networks. Signal Processing, 36, 1994, 209-232.
Tzotsos A. and Argialas D. (2006). MSEG: A generic region-based multi-scale image segmentation algorithm for remote sensing imagery. In: Proceedings of ASPRS 2006 Annual Conference, Reno, Nevada; May 1-5, 2006.
Tzotsos A. and Argialas D. (2007). Support Vector Machine Classification for Object-Based Image Analysis. In: Object-Based Image Analysis – Spatial Concepts For Knowledge-Driven Remote Sensing Applications. Springer 2007.
Ulichney, R. (1987). Digital Halftoning. The MIT Press, Cambridge, MA.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Tzotsos, A., Iosifidis, C., Argialas, D. (2008). A hybrid texture-based and region-based multi-scale image segmentation algorithm. In: Blaschke, T., Lang, S., Hay, G.J. (eds) Object-Based Image Analysis. Lecture Notes in Geoinformation and Cartography. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77058-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-77058-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77057-2
Online ISBN: 978-3-540-77058-9
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)