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Article:Classification of RS data using Decision Tree Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 23 - Number 3 |
| Year of Publication: 2011 |
| Authors: Pooja A P, Jayanth J, Dr Shivaprakash Koliwad |
10.5120/2872-3729
|
Pooja A P, Jayanth J, Dr Shivaprakash Koliwad . Article:Classification of RS data using Decision Tree Approach. International Journal of Computer Applications. 23, 3 ( June 2011), 7-11. DOI=10.5120/2872-3729
Abstract
The traditional hard classification techniques are parametric in nature and they expect data to follow a Gaussian distribution, they have been found to be performing poorly on high resolution satellite images, as classes in these images tend to exhibit extensive overlapping in spectral space. This produces spectral confusion among the classes and results in inaccurate classified images. A major drawback of such classifiers lies in the difficulty of integrating ancillary data, which follows a non Gaussian distribution nature. Ancillary data provides extra spectral and spatial knowledge, which improves the classification accuracy. Classification done using such knowledge is known as knowledge base classification. The present study explores a non-parametric decision tree classifier to extract knowledge from the spatial data in the form of classification rules. The classified image overall accuracy was found to be 86.66% using the Decision Tree method and with kappa values .8133 respectively.
References
- Mahesh Pal & Paul M Mather, “Decision Tree Based Classification of Remotely Sensed Data”, centre for remote imaging, sensing and processing (CRISP), November 2001.
- Paul M Mather, “Computer Processing of Remotely-Sensed Images- An Introduction”, Wiley Publications, 3rd edition, 2004.
- D.LU & Q.Weng, “A Survey of Image Classification Methods and Techniques for Improving Classification Performance”, International Journal of Remote Sensing, Vol.28, No.5, 10 March 2007, 823-870.
- Andras Bardosy & Luis Samaniego “Fuzzy Rule-Based Classification of Remotely Sensed Imagery”, IEEE Transactions on Geoscience and Remote Sensing, Vol.40, No. 2, February 2002.
- Robert A Schowngerdt, “Remote Sensing Models & Methods for Image Processing”, 2nd Edition, Elsevier Publications, 2006.
- Rasoul Safavian & David Landgrebe, “A survey of Decision Tree Classifier Methodology”, IEEE Transactions on Systems, Man and Cybernetics, Vol.21, No.3, May/June 1991, pp.660-674.
- Mahesh Pal & Paul M Mather, “A comparison of Decision Tree and Back Propagation Neural Network Classifiers for Land Use Classification”, Geo science and remote sensing symposium, 2002, IEEE International proceedings, vol. 1, pp. 503-505.
- C. Apte & S. Weiss, “Data Mining with Decision Trees and Decision Rules, future generation computer systems”, November 1997, pp. 197-210.
- J R Otukei & T Blaschke, “Land Cover Change Assessment Using Decision Trees, support vector machines and maximum likelihood classification algorithms A survey of image classification methods and techniques for improving classification performance”, International Journal of Applied Earth Observation and Geo information 12S, pp.S27-S31, 2010.
- Brandt Tso & Paul M Mather, “Classification Methods for Remotely Sensed Data”, 2nd edition, CRC Press 2009.
- J R Quinlan, “Decision trees and decision making”, IEEE transactions on Systems, Man and Cybernetics, Vol.20, No.2, March/April 1990, pp.339-346.
- Sam Drazin & Matt Montag, Decision Tree analysis using WEKA, university of Miami.
- Lior Rokach & Oded Maimon, “Top Down Induction of Decision Trees Classifiers- A Survey”, IEEE Transactions on systems, Man and Cybernetics-Part C: Applications and Reviews, vol 35, No. 4, pp. 476-487, November 2005.
- Florina Esposito, Donato Malerba & Giovanni Semeraro, “A Comparitive Analysis of Methods for Pruning Decision Trees”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 5 pp. 476-491, May 1997.
- H. Ian Witten and Eibe Frank, “Data Mining: Practical machine learning tools and techniques”, Elsevier, 2nd edition,2005.
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