Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data

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.

@article{Ahmad2018,
  title     = {Comparative Analysis of Support Vector Machine, Maximum Likelihood and Neural Network Classification on Multispectral Remote Sensing Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {9},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Asmala Ahmad and Ummi Kalsom Mohd Hashim and Othman Mohd and Mohd Mawardy Abdullah and Hamzah Sakidin and Abd Wahid Rasib and Suliadi Firdaus Sufahani},
  doi       = {10.14569/IJACSA.2018.090966},
  url       = {https://doi.org/10.14569/IJACSA.2018.090966}
}