eprintid: 10683
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/06/83
datestamp: 2023-11-09 16:37:17
lastmod: 2023-11-09 16:37:17
status_changed: 2023-11-09 16:31:58
type: article
metadata_visibility: show
creators_name: Ahmad, A.
creators_name: Hashim, U.K.M.
creators_name: Mohd, O.
creators_name: Abdullah, M.M.
creators_name: Sakidin, H.
creators_name: Rasib, A.W.
creators_name: Sufahani, S.F.
title: Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
ispublished: pub
note: cited By 18
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. © 2018 International Journal of Advanced Computer Science and Applications.
date: 2018
publisher: Science and Information Organization
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061436022&doi=10.14569%2fijacsa.2018.090966&partnerID=40&md5=6400131e494f0d2402384fa84aa378e0
id_number: 10.14569/ijacsa.2018.090966
full_text_status: none
publication: International Journal of Advanced Computer Science and Applications
volume: 9
number: 9
pagerange: 529-537
refereed: TRUE
issn: 2158107X
citation:   Ahmad, A. and Hashim, U.K.M. and Mohd, O. and Abdullah, M.M. and Sakidin, H. and Rasib, A.W. and Sufahani, S.F.  (2018) Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data.  International Journal of Advanced Computer Science and Applications, 9 (9).  pp. 529-537.  ISSN 2158107X