An Approach based on Adaptive Decision Tree for Land Cover Change Prediction in Satellite Images

Abstract

Decision tree (DT) prediction algorithms have significant potential for remote sensing data prediction. This paper presents an advanced approach for land-cover change prediction in remote-sensing imagery. Several methods for decision tree change prediction have been considered: probabilistic DT, belief DT, fuzzy DT, and possibilistic DT. The aim of this study is to provide an approach based on adaptive DT to predict land cover changes and to take into account several types of imperfection related to satellite images such as: uncertainty, imprecision, vagueness, conflict, ambiguity, etc. The proposed approach applies an artificial neural network (ANN) model to choose the appropriate gain formula to be applied on each DT node. The considered approach is validated using satellite images representing the Saint-Paul region, commune of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone.