Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

  • Authors

    • S L. Senthil Lekha
    • S S.Kumar
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17661
  • Kanyakumari, landsat image, Envi 5.1, QGIS.

  • Abstract

    Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper  have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and  kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy  of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district.

     

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  • How to Cite

    L. Senthil Lekha, S., & S.Kumar, S. (2018). Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques. International Journal of Engineering & Technology, 7(3.27), 82-90. https://doi.org/10.14419/ijet.v7i3.27.17661

    Received date: 2018-08-16

    Accepted date: 2018-08-16

    Published date: 2018-08-15