Authors:
Ahlem Ferchichi
;
Wadii Boulila
and
Riadh Farah
Affiliation:
Ecole Nationale des Sciences de l’Informatique, Tunisia
Keyword(s):
Remote-sensing, Land-cover Change Prediction, Decision Trees, Data Imperfection, and Artificial Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Integration of Data Warehousing and Data Mining
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
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.