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S.V.S. Prasad
MLRIT, Hyderabad, India
Computer Science & IT Electrical & Electronics Engineering

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Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers S.V.S. Prasad; T. Satya Savithri; Iyyanki V. Murali Krishna
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (500.944 KB) | DOI: 10.11591/ijece.v7i3.pp1180-1187

Abstract

The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images.
Co-Authors Iyyanki V. Murali Krishna T. Satya Savithri