Modified Self-Organizing Feature Map Neural Network with Semi-supervision for Change Detection in Remotely Sensed Images

  • Susmita Ghosh
  • Moumita Roy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


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.


Semi-supervised learning change detection fuzzy set self-organizing feature map 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Susmita Ghosh
    • 1
  • Moumita Roy
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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