Semi-supervised Learning with Multilayer Perceptron for Detecting Changes of Remote Sensing Images
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.
KeywordsChange Detection Input Pattern Markov Random Fields Multilayer Perceptron Label Pattern
- 5.Chavez, P.S., MacKinnon, D.J.: Automatic detection of vegetation changes in the southwestern United States using remotely sensed images. Photogramm. Eng. Remote Sensing 60, 1285–1294 (1994)Google Scholar
- 7.Canty, M.J.: Image Analysis, Classification and Change Detection in Remote Sensing. CRC Press, Taylor & Francis (2006)Google Scholar
- 11.Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2001)Google Scholar
- 12.Haykin, S.: Neural Networks: A Comprehensive Foundation. Pearson Education, Fourth Indian Reprint (2003)Google Scholar