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

Taketsugu Nagao, Minoru Fukumi, Norio Akamatsu, Yasue Mitsukura

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)800-806
ページ数7
ジャーナルIEEJ Transactions on Electronics, Information and Systems
125
5
DOI
出版ステータスPublished - 2005
外部発表はい

ASJC Scopus subject areas

  • 電子工学および電気工学

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