Abstract
Problem of change detection of remotely sensed images using insufficient labeled patterns is the main topic of present work. Here, semi-supervised learning is integrated with an unsupervised context-sensitive change detection technique based on modified self-organizing feature map (MSOFM) network. In this method, training of the MSOFM is performed iteratively using unlabeled patterns along with a few labeled patterns. A method has been suggested to select unlabeled patterns for training. To check the effectiveness of the proposed methodology, experiments are carried out on two multitemporal remotely sensed images. Results are found to be encouraging.
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Ghosh, S., Roy, M. (2011). Modified Self-Organizing Feature Map Neural Network with Semi-supervision for Change Detection in Remotely Sensed Images. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2011. Lecture Notes in Computer Science, vol 6744. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21786-9_18
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DOI: https://doi.org/10.1007/978-3-642-21786-9_18
Publisher Name: Springer, Berlin, Heidelberg
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