Drift Ice Classification Using SAR Image Data by a Self-Organizing Neural Network

Taketsugu Nagao, Minoru Fukumi, Norio Akamatsu, Yasue Mitsukura

Research output: Contribution to journalArticlepeer-review


This paper proposes a segmentation method of SAR (Synthetic Aperture Radar) images which uses a SOM(Self-Organizing Map). SAR images are obtained by observation using microwave sensor. They are segmented into the drift ice (thick, thin), and sea regions manually, and then features are extracted from partitioned data. However they are not necessarily effective for neural network learning because they can include incorrectly segmented data. Therefore, in particular, a multi-step SOM is used as a learning method to improve reliability of teacher data, and carries out classification. This process enable us to fix all mistook data and segment the SAR data using just data. The validity of this method was demonstrated by computer simulations using the actual SAR images.

Original languageEnglish
Pages (from-to)800-806
Number of pages7
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number5
Publication statusPublished - 2005
Externally publishedYes


  • Drift Ice
  • Neural Network
  • SAR
  • SOM
  • Self-organizing

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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