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.
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References
Singh, A.: Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing 10, 989–1003 (1989)
Liu, X., Lathrop, R.G.: Urban change detection based on an artificial neural network. Int. J. Remote Sensing 23, 2513–2518 (2002)
Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sensing 38, 1171–1182 (2000)
Hame, T., Heiler, I., Miguel-Ayanz, J.S.: An unsupervised change detection and recognition system for forestry. Int. J. Remote Sensing 19, 1079–1099 (1998)
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)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: A systematic survey. IEEE Trans. Image Processing 14, 294–307 (2005)
Canty, M.J.: Image Analysis, Classification and Change Detection in Remote Sensing. CRC Press, Taylor & Francis (2006)
Kasetkasem, T., Varshney, P.K.: An image change-detection algorithm based on Markov random field models. IEEE Trans. Geosci. Remote Sensing 40, 1815–1823 (2002)
Ghosh, S., Bruzzone, L., Patra, S., Bovolo, F., Ghosh, A.: A context-sensitive technique for unsupervised change detection based on Hopfield-type neural networks. IEEE Trans. Geosci. Remote Sensing 45, 778–789 (2007)
Patra, S., Ghosh, S., Ghosh, A.: Unsupervised change detection in remote-sensing images using modified self-organizing feature map neural network. In: Int. Conf. on Computing: Theory and Applications (ICCTA-2007), pp. 716–720. IEEE Computer Society Press, Los Alamitos (2007)
Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2001)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Pearson Education, Fourth Indian Reprint (2003)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, Singapore (2001)
Klir, G.J., Yuan, B.: Fuzzy sets and Fuzzy Logic - Theory and Applications. Prentice Hall, New York (1995)
Verikas, A., Gelzinis, A., Malmqvist, K.: Using unlabelled data to train a multilayer perceptron. Neural Processing Letters 14, 179–201 (2001)
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Patra, S., Ghosh, S., Ghosh, A. (2007). Semi-supervised Learning with Multilayer Perceptron for Detecting Changes of Remote Sensing Images. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_20
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DOI: https://doi.org/10.1007/978-3-540-77046-6_20
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