TY  - JOUR
SP  - 529
IS  - 9
N1  - cited By 18
A1  - Ahmad, A.
A1  - Hashim, U.K.M.
A1  - Mohd, O.
A1  - Abdullah, M.M.
A1  - Sakidin, H.
A1  - Rasib, A.W.
A1  - Sufahani, S.F.
Y1  - 2018///
SN  - 2158107X
TI  - Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data
ID  - scholars10683
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061436022&doi=10.14569%2fijacsa.2018.090966&partnerID=40&md5=6400131e494f0d2402384fa84aa378e0
PB  - Science and Information Organization
EP  - 537
AV  - none
VL  - 9
JF  - International Journal of Advanced Computer Science and Applications
N2  - 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.
ER  -