Ensemble of Multilayer Perceptrons for Change Detection in Remotely Sensed Images

Abstract

In this letter, a change detection technique using a multiple classifier system is proposed. Here, different architectures of multilayer perceptron (MLP) are used as base classifiers. An ensemble of different MLPs is utilized to increase the robustness of the system. This also avoids the problem of choosing an optimum architecture for MLP. First, the support values for each of the unlabeled patterns are estimated using different MLPs (trained with the labeled patterns). Then, each of the unlabeled patterns is assigned to a specific class by fusing the outcome of the base classifiers using different combination rules. Experiments are carried out on multitemporal and multispectral images. Results show that the proposed ensemble technique has an edge over individual base classifiers for change detection in remotely sensed images.