Fuzzy neural network architecture for change detection in remotely sensed imagery

This paper aims to propose a change-detection system in remotely sensed imagery based on the combination of fuzzy sets and neural networks. Multitemporal images are directly classified into change and no-change classes using a fuzzy membership model in order to provide complete information about the change. Presently, two fuzzy models derived from the Mahalanobis distance and a fuzzy neural network (FNN) combination are proposed and compared. In order to evaluate the performance of each model, extensive experiments using different performance indicators are carried out on two SPOT HRV images covering a region of Algeria. Results obtained showed that it has a great potential for land-cover change detection since it allows the nature of change to be extracted automatically. Furthermore, the FNN-based model gives the best performance. This model allows a reduced amount of false alarms with higher change detection accuracy.