Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms

Land cover change assessment is one of the main applications of remote sensed data. A number of pixel based classification algorithms have been developed over the past years for the analysis of remotely sensed data. The most notable include the maximum likelihood classifier (MLC), support vector machines (SVMs) and the decision trees (DTs). The DTs in particular offer advantages not provided by other approaches. They are computationally fast and make no statistical assumptions regarding the distribution of data. The challenge to using DTs lies in the determination of the “best” tree structure and the decision boundaries. Recent developments in the field of data mining have however, provided an alternative for overcoming the above shortcomings. In this study, we analysed the potential of DTs as one technique for data mining for the analysis of the 1986 and 2001 Landsat TM and ETM+ datasets, respectively. The results were compared with those obtained using SVMs, and MLC. Overall, acceptable accuracies of over 85% were obtained in all the cases. In general, the DTs performed better than both MLC and SVMs.