Semi-supervised Learning with Multilayer Perceptron for Detecting Changes of Remote Sensing Images

  • Swarnajyoti Patra
  • Susmita Ghosh
  • Ashish Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)

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.

Keywords

Change Detection Input Pattern Markov Random Fields Multilayer Perceptron Label Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Swarnajyoti Patra
    • 1
  • Susmita Ghosh
    • 1
  • Ashish Ghosh
    • 2
  1. 1.Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032India
  2. 2.Machine Intelligence Unit and Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108India

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