Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers
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...
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| Veröffentlicht in: | International journal of electrical and computer engineering (Malacca, Malacca) Malacca), 2017-06, Vol.7 (3), p.1180 |
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| container_title | International journal of electrical and computer engineering (Malacca, Malacca) |
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| creator | Prasad, S.V.S. Savithri, T. Satya V. Murali Krishna, Iyyanki |
| description | 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. |
| doi_str_mv | 10.11591/ijece.v7i3.pp1180-1187 |
| format | Article |
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Satya ; V. Murali Krishna, Iyyanki</creator><creatorcontrib>Prasad, S.V.S. ; Savithri, T. Satya ; V. Murali Krishna, Iyyanki</creatorcontrib><description>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. 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Satya</creatorcontrib><creatorcontrib>V. Murali Krishna, Iyyanki</creatorcontrib><title>Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers</title><title>International journal of electrical and computer engineering (Malacca, Malacca)</title><description>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. 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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial neural networks Change detection Classification Classifiers Climate change Clustering Environmental monitoring High resolution Image classification Image detection Image resolution Land cover Land use management Landsat satellites Learning theory Neural networks Reliability Satellite imagery Support vector machines |
| title | Comparison of Accuracy Measures for RS Image Classification using SVM and ANN Classifiers |
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