A semi-supervised change detection for remotely sensed images using ensemble classifier

In the present work, a change detection technique in remotely sensed images (under the scarcity of labeled patterns) is proposed where an ensemble of semi-supervised classifiers is used, instead of using a single (weak) classifier. Iterative learning of multiple classifier system is carried out using the selected unlabeled patterns along with a few labeled patterns. Selection of unlabeled patterns for the next training step is done using ensemble agreement. Finally, the unlabeled patterns are assigned to a class by fusing the outcome of base classifiers using a combiner. For the present investigation, multilayer perceptron (MLP), elliptical basis function neural network (EBFNN) and fuzzy k-nearest neighbor (KNN) techniques are used as base classifiers. Experiments are carried out on multi-temporal and multi-spectral images and the results for the proposed methodology are found to be encouraging.