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
Related Articles
- EP ID EP394226
- DOI 10.14569/IJACSA.2018.090966
- Views 100
- Downloads 0
Response Prediction for Chronic HCV Genotype 4 Patients to DAAs
Hepatitis C virus (HCV) is a major cause of chronic liver disease, end stage liver disease and liver cancer in Egypt. Genotype 4 is the prevalent genotype in Egypt and has recently spread to Southern Europe particularly...
Video Compression by Memetic Algorithm
Memetic Algorithm by hybridization of Standard Particle Swarm Optimization and Global Local Best Particle Swarm Optimization is proposed in this paper. This technique is used to reduce number of computations of video co...
Efficient Smart Emergency Response System for Fire Hazards using IoT
The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Sma...
The Modelling Process of a Paper Folding Problem in GeoGebra 3D
In this research; a problem situation, which requires the ability of thinking in three dimensions, was developed by the researchers. As the purpose of this paper is producing a modeling task suggestion, the problem was v...
Particle Swarm Optimization for Calibrating and Optimizing Xinanjiang Model Parameters
The Xinanjiang model, a conceptual hydrological model is well known and widely used in China since 1970s. Therefore, most of the parameters in Xinanjiang model have been calibrated and pre-set according to different clim...