Drift ice detection using a self-organizing neural network

Minoru Fukumi, Taketsugu Nagao, Yasue Mitsukura, Rajiv Khosla

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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

本文言語English
ホスト出版物のタイトルKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
出版社Springer Verlag
ページ1268-1274
ページ数7
ISBN(印刷版)3540288945, 9783540288947
DOI
出版ステータスPublished - 2005
外部発表はい
イベント9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
継続期間: 2005 9月 142005 9月 16

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
3681 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005
国/地域Australia
CityMelbourne
Period05/9/1405/9/16

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータサイエンス一般

フィンガープリント

「Drift ice detection using a self-organizing neural network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル