Drift ice detection using a self-organizing neural network

Minoru Fukumi, Taketsugu Nagao, Yasue Mitsukura, Rajiv Khosla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationKnowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings
Pages1268-1274
Number of pages7
Publication statusPublished - 2005 Dec 1
Event9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 - Melbourne, Australia
Duration: 2005 Sep 142005 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3681 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

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

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Fukumi, M., Nagao, T., Mitsukura, Y., & Khosla, R. (2005). Drift ice detection using a self-organizing neural network. In Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings (pp. 1268-1274). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3681 LNAI).