Change detection in remotely sensed images using an ensemble of multilayer perceptrons

In the proposed work, a change detection technique is developed using a combination of multilayer perceptrons (MLPs). At the onset, the different MLPs are trained with the labeled patterns. Then, the support values (or, the output values) for the unlabeled patterns are obtained from these trained MLPs. At last, decision regarding the class assignment for the unlabeled patterns has been made by fusing the outcome (i.e., support values) obtained from different trained MLPs. In the present experiment, `mean rule' and `majority voting' are used as combination rules. Experiments are carried out on multi-temporal and multi-spectral remotely sensed images. Results for the proposed methodology are found to be encouraging.