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

Abstract

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 multitemporal and multi-spectral remotely sensed images. Results for the proposed methodology are found to be encouraging.

Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

“…Complimentary to the available traditional tools of satellite image classification, novel methods of Deep Learning (DL) can now be used as an advanced approach for RS data processing. For instance, algorithms such as Multilayer Perceptron (MLPClassifier) are designed to support image partition, classification and analysis through the use of decision trees and computer vision algorithms [40][41][42]. The application of such methods in hydrological studies and coastal risk assessment creates principally new perspectives in cartography by combining RS data with programming approaches for the mapping of flooded areas.…”