Semi-supervised Hopfield-Type Neural Network for change detection in remotely sensed images

In this article, we propose a change detection technique using semi-supervised Hopfield-Type Neural Network (HTNN). The purpose of the work is to show the usefulness of semi-supervision over existing unsupervised/fully supervised methods when we have only a few labeled samples. Here, training of HTNN is performed iteratively using a few labeled patterns along with a number of unlabeled patterns. A method has been suggested to propagate the label information using a kind of K-nearest neighbor approach. To check the effectiveness of the proposed method, experiments are carried out on multi-temporal remotely sensed images. Results are compared with other state of the art techniques and found to be significantly better.