Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 9
Abstract
Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.
Authors and Affiliations
Asmala Ahmad, Ummi Kalsom Mohd Hashim, Othman Mohd, Mohd Mawardy Abdullah, Hamzah Sakidin, Abd Wahid Rasib, Suliadi Firdaus Sufahani
Keywords
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- EP ID EP394226
- DOI 10.14569/IJACSA.2018.090966
- Views 101
- Downloads 0
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