Abstract
Image classification entails the important part of digital image and has been very essential in the application of remote sensing systems, thus the demand for research to find advanced algorithms and tools to solve problems experienced in classification has shown great increase in interest over the years. In this day and age, remote sensing has globally being applied with the use of current advanced satellite systems and sensors, but the need to provide analysis and decision making has been a challenge. The contribution of this dissertation is an empirical comparison (evaluation) of five machine learning (ML) techniques, in terms of classifying satellite images. The ML techniques consist of Random Forest (RF), K Nearest Neighbour (k-NN), Naïve Bayes (NB), Multi-layer Perceptron (MLP) and Support Vector Machines (SVM). The evaluation of these five techniques is based on a selection of six performance measures, such as [Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Precision, and Receiver Operating Characteristics (ROC)] Three publicly available remote sensing datasets are utilised for this task. The experimental results show that RF achieved higher accuracy rates, with robust performance, followed by MLP, k-NN, NB and SVM classifier exhibiting the worst performance. Hence, the use of ML for image analysis and pattern recognition is a promising approach.
M.Phil. (Electrical Engineering)