PARAMETER TESTS FOR IMAGE SEGMENTATION OF AN AGRICULTURAL REGION

Year 2013, Volume: 3 Issue: 2, 515 - 524, 01.12.2013

Z. Damla Uça Avcı

Abstract

Segmentation, mainly the operation of object extraction from the total image, is the initial and a very important step of
object-based image processing since it directly affects the performance of the processing, quality of the product and the
accuracy of the results. In this study, the segmentation parameters of Definiens, widely used GEOBIA software, were tested
by comparing the segments obtained by various combinations of values. As the study area, an agricultural region was
selected and the results were evaluated in scope of extracting the field boundaries. The tests were conducted by producing 27
segmented images of the SPOT 4 data acquired on April 26th, 2007. The performances for the given conditions (combination
of criteria) were compared to each other and the criteria of value selection that can be used for different aims were outlined.
The results can be used in further studies for estimating the optimum values in accordance with the purpose.

Keywords

image segmentation, segmentation parameters, parameter tests, agricultural remote sensing

References

• S. Karakış, A. M. Marangoz, G. Büyüksalih, “Analysis Ecognition Software Using High Resolution Quickbird MS Imagery”, ISPRS Workshop on Topographic Mapping from Space, Ankara, Turkey, February 14th-16th, 2006. Parameters in
• Y. Zhang, T. Maxwell, H. Tong, V. Dey, “Development of a Supervised Software Tool For Automated Segmentation Parameters For Ecognition”, ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, July 5–7, IAPRS, Vol. XXXVIII, Part 7B, Of Optimal K. K. Singh, A. Singh, “A Study of Image Segmentation Algorithms for Different Types of Images”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5, September 2010.
• Wang, Y. H., Tutorial: Image Segmentation. A. “Performance Assessment and Review of Image Segmentation Techniques for Natural Images”, Current Trends in Technology and Science, ISSN : 2279-053. Volume : II, Issue : V, 2013. S. Overview, Comprehensive
• Saraf, Y., Algorithms for Image Segmentation, Thesis, Birla Institute of Technology and Science, Pilani, Rajasthan, 2006.
• Maxwell, T., Object-Oriented Classification: Classification Imagery and a Fuzzy Approach to Improving Image Segmentation Geodesy and Geomatics Engineering, UNB, 2005. Technical Report,
• T. Lübker, G. Schaab, “Optimization of Parameter Segmentation in Geobia”, Workshop, High-Resolution Earth Imaging for Geospatial Information, Germany, 2009. for Multilevel ISPRS Hannover June 2-5, Hannover,
• A. Darwish, K. Leukert, W. Reinhardt, “Image Segmentation for the Purpose of Object-Based Classification”, Geoscience and Remote Sensing Symposium, IGARSS '03 Proceedings, IEEE International Volume:3, 2039 – 2041, 21-25 July,
• J. Tian, D. M. Chen, “Optimization in Multi- Scale Segmentation of High-Resolution Satellite Images for Artificial Feature Recognition”, International Journal of Remote Sensing, Vol. 28, No. 20, 4625–4644, 2007.
• Baatz, M., Schäpe, M., Multiresolution Segmentation - An Optimization Approach for High Quality Multi-Scale Image Segmentation. In: Strobl, J., Blaschke, T., Griesebner, G. (Eds.), Angewandte Verarbeitung XII, Wichmann Verlag, Karlsruhe, p. 23, 2000. Informations
• D. Flanders, M. Hall-Beyer, J. Pereverzoff, “Preliminary Evaluation of eCognition Object- Based Software for Cut Block Delineation and Feature Extraction”, Canadian Journal of Remote Sensing, 29 (4), p. 441-452, 2003.
• U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, M. Heynen, “Multiresolution, Object- Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information”, ISPRS Journal of Photogrammetry and Remote Sensing, 58 (3-4), p. 258, 2004.
• S. Lang, D. Tiede, Definiens Developer, GIS Business 9/2007, p. 34- 37, 2007. www.tigem.gov.tr (Web page of The General Directorate of Agricultural Enterprises) resolution-and-spectral-bands
• Definiens A. G., E-Cognition User/Reference Guide 4, 2004.
• M. Baatz, A. Schäpe, “Object-Oriented and Multi-Scale Networks”, Symposium on Operationalization of Remote Sensing, August 16-20, 1999. Enschede. ITC, 1999.
• Drǎguţ, L., Tiede, D., Levick, S. R., ESP: A Tool Multiresolution Image Segmentation of Remotely Sensed Geographical Information Science, 24:6, 859-871, Scale Parameter for Data, International Journal of Meinel, G. and Neubert, M., A Comparison of Segmentation Programs for High Resolution Remote Sensing Data, Proceedings of 20th ISPRS Congress, Istanbul, 2004.
• K. Haris, S. Efstratiadis, N. Maglaveras, A. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging”, IEEE texture annealing framework”, IEEE Baatz, M., Benz, U., Denghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., Willhauck, G., 2004, eCognition Professional: User Guide 4
• (Munich: Definiens-Imaging), 2004.
• Zhang, Y. J., “Evaluation and Comparison of Different Recognition Letters, Vol. 18, No 10, pp. 963-974. Algorithms”, Pattern Glasbey, C.A. and Horgan, G.W., Image Analysis for the Biological Sciences, Chapter 4, Wiley, 1995.