Drift ice detection using a self-organizing neural network

Authors:
Minoru Fukumi;Taketsugu Nagao;Yasue Mitsukura;Rajiv Khosla
Affiliations:
Dept. of Information Science and Intelligent Systems, University of Tokushima, Japan;Dept. of Information Science and Intelligent Systems, University of Tokushima, Japan;Faculty of Education, Okayama University, Okayama, Japan;La Trobe University, Melbourne, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

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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.