A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones

Abstract

This study assesses the performance of the support vector machine image classification technique in the context of a tropical coastal zone exhibiting low to medium scale development. The overall and individual classification results of this approach were compared to the maximum likelihood classifier and the artificial neural network techniques. A 15-m spatial resolution ASTER image of Koh Tao in Thailand was used for the test, and support vector machine was found to offer only limited improvements in classification accuracy over the other methodologies. The support vector machine did, however, show promise in dealing with the difficult challenge of separating human infrastructure such as buildings from other land cover types such as coastal rock and sandy beach which have very similar spectral signatures. The medium resolution ASTER image also proved highly suited to classifying coastal landscapes with this mix of land cover types. Additional research is needed to assess the full potential of the support vector machine in a weighted or layered classification, and to explore potential applications of this classification tool in other tropical environments.