Semi-supervised learning with multilayer perceptron for detecting changes of remote sensing images

Authors:
Swarnajyoti Patra;Susmita Ghosh;Ashish Ghosh
Affiliations:
Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India;Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India
Venue:
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Year:
2007

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Abstract

A context-sensitive change-detection technique based on semi-superv-ised learning with multilayer perceptron is proposed. In order to take contextual information into account, input patterns are generated considering each pixel of the difference image along with its neighbors. A heuristic technique is suggested to identify a few initial labeled patterns without using ground truth information. The network is initially trained using these labeled data. The unlabeled patterns are iteratively processed by the already trained perceptron to obtain a soft class label. Experimental results, carried out on two multispectral and multitemporal remote sensing images, confirm the effectiveness of the proposed approach.