Application Of Decision Tree Technology For Image Classification Using Remote Sensing Data

Choice of a classification algorithm is generally based upon a number of factors, among which are availability of software, ease of use, and performance, measured here by overall classification accuracy. The maximum likelihood (ML) procedure is, for many users, the algorithm of choice because of its ready availability and the fact that it does not require an extended training process. Artificial neural networks (ANNs) are now widely used by researchers, but their operational applications are hindered by the need for the user to specify the configuration of the network architecture and to provide values for a number of parameters, both of which affect performance. The ANN also requires an extended training phase.In the past few years, the use of decision tree (DTs) to classify remotely sensed data has increased. Proponents of the method claim that it has a number of advantages over the ML and ANN algorithms. The DT is computationally fast, make no statistical assumptions, and can handle data that are represented on different measurement scales. Pruning of DTs can make them smaller and more easily interpretable, while the use of treeboost and tree forest techniques can improve performance.In this paper, we present several types of decision tree classification algorithms and evaluate them on SPOT5 remote sensing data sets of Conghua area.The decision tree classification algorithms tested include a single decision tree model (CART, CHAID, exaustive CHAID & QUEST) and ensemble decision tree model(TreeBoost&Decsion Tree Forest). Classification accuracies produced by each of these decision tree algorithms are compared with both artificial neural networks and maximum likelihood classifiers. The results showed as follows:(1)Decision trees in general, and the decision tree forest in particular, produced consistently higher classification accuracies than MLX algorithms. Several factors contribute to this result, the most important being that decision trees can adapt to the noisy and nonlinear relations often observed between land cover classes and remotely sensed data. Decision trees have the further advantage of being nonparametric and therefore make no assumptions regarding the distribution of input data.(2)Most of tested decision tree algorithms perform better than ANN while some (CHAID & exhaustive CHAID) did not. But neural networks have a number of drawbacks. First, neural networks do not present an easily-understandable model. When looking at decision tree, it is easy to see that some initial variable divides the data into several categories and then other variables split the resulting child groups. This information is very useful to the researcher who...