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.
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.
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.
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.
Lillesand, T. M, Kiefer R. W & Chipman J. W. (2004). Remote Sensing and Image Interpretation. Singapore: John Wiley & Sons.
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.
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.
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.
Vapnik, V. N. (1998). Statistical Learning Theory. New York: Wiley Publishers Inc.
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.
Witten, I. H. &Frank E. (2005) Data Mining: Practical Machine Learning Tools and Techniques. San Francisco: Morgan Kaufmann. 120-134.
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.