ew 18: e1

Research Article

A Hybrid ACO based Optimized RVM Algorithm for Land Cover Satellite Image Classification

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  • @ARTICLE{10.4108/eai.23-12-2020.167789,
        author={A. M. A. Akbar Badusha and S. Kother Mohideen},
        title={A  Hybrid  ACO  based  Optimized  RVM  Algorithm  for  Land Cover Satellite Image Classification},
        journal={EAI Endorsed Transactions on Energy Web: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={12},
        keywords={satellite Image classification, Ant Colony Optimization, Optimized RVM},
        doi={10.4108/eai.23-12-2020.167789}
    }
    
  • A. M. A. Akbar Badusha
    S. Kother Mohideen
    Year: 2020
    A Hybrid ACO based Optimized RVM Algorithm for Land Cover Satellite Image Classification
    EW
    EAI
    DOI: 10.4108/eai.23-12-2020.167789
A. M. A. Akbar Badusha1,*, S. Kother Mohideen2
  • 1: Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
  • 2: Associate Professor & Head, Department of IT, Sri Ram Nallamani Yadava College of Arts & Science, Tenkasi, Tamil Nadu, India
*Contact email: Badusha10@gmail.com

Abstract

The proposed different classification schemes based on modified RVM and Optimized RVM were implemented, and observed the classification outputs. The proposed Optimized RVM classification technique is successfully applied to the classification of the input images. The quantitative and qualitative classification results are evaluated and compared. The accuracy of the proposed modified RVM is improved by applying the Ant Colony Optimization (ACO) technique. The kernel parameter of the modified RVM is optimized using ACO, and the results got improved a little better than the modified RVM based classifier. The general agreement between all classification techniques discussed in the above sections indicates that the inclusion of ACO for RVM parameter optimization adds very accurate classification in various multispectral satellite images. The performance evaluations confirm that the proposed ACO based Optimized RVM (ACO-RVM) classifier greatly improved the accuracy of final classification outputs.