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Decision tree approach for classification of remotely sensed satellite data using open source support

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In this study, an attempt has been made to develop a decision tree classification (DTC) algorithm for classification of remotely sensed satellite data (Landsat TM) using open source support. The decision tree is constructed by recursively partitioning the spectral distribution of the training dataset using WEKA, open source data mining software. The classified image is compared with the image classified using classical ISODATA clustering and Maximum Likelihood Classifier (MLC) algorithms. Classification result based on DTC method provided better visual depiction than results produced by ISODATA clustering or by MLC algorithms. The overall accuracy was found to be 90% (kappa = 0.88) using the DTC, 76.67% (kappa = 0.72) using the Maximum Likelihood and 57.5% (kappa = 0.49) using ISODATA clustering method. Based on the overall accuracy and kappa statistics, DTC was found to be more preferred classification approach than others.

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Acknowledgements

RS and PKJ acknowledge Department of Science and Technology (DST), Ministry of Science and Technology, Government of India. AG acknowledges Council of Scientific and Industrial Research (CSIR), Government of India for support. The experimental and computational facilities of TERI University have been used; support to this research is greatly acknowledged by authors. Authors also acknowledge anonymous reviewers for constructive comments and suggestions to improve quality of manuscript.

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  1. Department of Natural Resources, TERI University, New Delhi, 110 070, India

    RICHA SHARMA, ANIRUDDHA GHOSH & P K JOSHI

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  1. RICHA SHARMA
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Correspondence to P K JOSHI.

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SHARMA, R., GHOSH, A. & JOSHI, P.K. Decision tree approach for classification of remotely sensed satellite data using open source support. J Earth Syst Sci 122, 1237–1247 (2013). https://doi.org/10.1007/s12040-013-0339-2

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