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Geodesy and Cartography

The Journal of Committee on Geodesy of Polish Academy of Sciences

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A Visual mining based framework for classification accuracy estimation

Pattathal Vijayakumar Arun
Published Online: 2013-12-31 | DOI: https://doi.org/10.2478/geocart-2013-0008


Classification techniques have been widely used in different remote sensing applications and correct classification of mixed pixels is a tedious task. Traditional approaches adopt various statistical parameters, however does not facilitate effective visualisation. Data mining tools are proving very helpful in the classification process. We propose a visual mining based frame work for accuracy assessment of classification techniques using open source tools such as WEKA and PREFUSE. These tools in integration can provide an efficient approach for getting information about improvements in the classification accuracy and helps in refining training data set. We have illustrated framework for investigating the effects of various resampling methods on classification accuracy and found that bilinear (BL) is best suited for preserving radiometric characteristics. We have also investigated the optimal number of folds required for effective analysis of LISS-IV images.


Techniki klasyfikacji są szeroko wykorzystywane w różnych aplikacjach teledetekcyjnych, w których poprawna klasyfikacja pikseli stanowi poważne wyzwanie. Podejście tradycyjne wykorzystujące różnego rodzaju parametry statystyczne nie zapewnia efektywnej wizualizacji. Wielce obiecujące wydaje się zastosowanie do klasyfikacji narzędzi do eksploracji danych. W artykule zaproponowano podejście bazujące na wizualnej analizie eksploracyjnej, wykorzystujące takie narzędzia typu open source jak WEKA i PREFUSE. Wymienione narzędzia ułatwiają korektę pół treningowych i efektywnie wspomagają poprawę dokładności klasyfikacji. Działanie metody sprawdzono wykorzystując wpływ różnych metod resampling na zachowanie dokładności radiometrycznej i uzyskując najlepsze wyniki dla metody bilinearnej (BL).

Keywords : Data mining; Remote sensing; Decision tree; Image classification; Visualization; WEKA; PREFUSE

  • Bouckaert, R. R., Frank E., Hall M. A., Holmes G., Pfahringer B., Reutemann P. & Witten I. H. (2010). WEKA-Experiences with a Java Open-Source Project. Journal of Machine Learning Research, vol.11, 2533-2541.Google Scholar

  • Durbha, S. S. & King R. L. (2005). Semantics-enabled framework for knowledge discovery from Earth observation data archives. IEEE Transaction on Geoscience and Remote Sensing, vol. 43(11), 2563-2572.Google Scholar

  • Keim, D. A., Panse C. & Sips M. (2003). PixelMaps: A New Visual Data Mining Approach for Analyzing Large Spatial Data Sets. In Proceedings of Third IEEE International Conference on Data Mining (ICDM’03), 19-22 November, 2003, IEEE CS Press, (pp. 565-568). Melbourne, Florida, USA: Institute of Electrical and Electronics Engineers (IEEE). DOI: ieeecomputersociety.org/10.1109/ ICDM.2003.1250978.Google Scholar

  • Lillesand, T. M, Kiefer R. W & Chipman J. W. (2004). Remote Sensing and Image Interpretation. Singapore: John Wiley & Sons.Google Scholar

  • Liu, Y. & Salvendy G. (2007). Design and evaluation of visualization support to facilitate decision trees classification. International Journal of Human- Computer Studies, vol. 65(2), 95-110. DOI: 10.1016/j.ijhcs.2006.07.005.Web of ScienceCrossrefGoogle Scholar

  • Lu, D. & Weng Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, vol. 28(5), 823-870.CrossrefGoogle Scholar

  • Nghi, Dang Huu & Chi - Mai Luong. (2008). An object-oriented classification techniques for high resolution satellite imagery. GeoInformatics for Spatial-Infrastructure Development in Earth and Allied Sciences (GIS-IDEAS), 230-240.Google Scholar

  • Vapnik, V. N. (1998). Statistical Learning Theory. New York: Wiley Publishers Inc.Google Scholar

  • Wilkinson, G. G. (2005). Results and implications of a study of fi fteen years of satellite image classification experiments. IEEE Transactions on Geoscience and Remote Sensing vol. 43, 433-440.Google Scholar

  • Witten, I. H. &Frank E. (2005) Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann. 120-134.Google Scholar

  • Zhang, J., Gruenwald L. & Gertz M. (2009). VDM-RS: A visual data mining system for exploring and classifying remotely sensed images. Journal of Computers and Geosciences, 1188-1192. Google Scholar

About the article

Published Online: 2013-12-31

Published in Print: 2013-12-01

Citation Information: Geodesy and Cartography, Volume 62, Issue 2, Pages 113–121, ISSN (Print) 2080-6736, DOI: https://doi.org/10.2478/geocart-2013-0008.

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