Application of support vector machine for classification of multispectral data

Nurul Iman, S B and Asmala, A. and Burhanuddin , M A (2014) Application of support vector machine for classification of multispectral data. In: 7th IGRSM International Remote Sensing & GIS Conference and Exhibition, 21-22 April 2014, Kuala Lumpur, Malaysia.

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In this paper, support vector machine (SVM) is used to classify satellite remotely sensed multispectral data. The data are recorded from a Landsat-5 TM satellite with resolution of 30x30m. SVM finds the optimal separating hyperplane between classes by focusing on the training cases. The study area of Klang Valley has more than 10 land covers and classification using SVM has been done successfully without any pixel being unclassified. The training area is determined carefully by visual interpretation and with the aid of the reference map of the study area. The result obtained is then analysed for the accuracy and visual performance. Accuracy assessment is done by determination and discussion of Kappa coefficient value, overall and producer accuracy for each class (in pixels and percentage). While, visual analysis is done by comparing the classification data with the reference map. Overall the study shows that SVM is able to classify the land covers within the study area with a high accuracy.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Asmala Ahmad
Date Deposited: 27 Jun 2014 07:02
Last Modified: 28 May 2015 04:27
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