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

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    • Keywords


      Remote sensing; image classification; decision tree classifier (DTC); maximum likelihood classifier (MLC); ISODATA.

    • Abstract


      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.

    • Author Affiliations


      Richa Sharma1 Aniruddha Ghosh1 P K Joshi1

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

  • Journal of Earth System Science | News

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      Posted on July 25, 2019

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