書誌事項
- タイトル別名
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- Drift Ice Classification Using SAR Image Data by a Self-Organizing Neural Network
- ジコ ソシキガタ ニューラル ネットワーク ニ ヨル SAR ガゾウ デ ノ リュウヒョウ シキベツ
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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.
収録刊行物
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- 電気学会論文誌C(電子・情報・システム部門誌)
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電気学会論文誌C(電子・情報・システム部門誌) 125 (5), 800-806, 2005
一般社団法人 電気学会
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詳細情報 詳細情報について
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- CRID
- 1390282679582196608
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- NII論文ID
- 10015518326
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- NII書誌ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL書誌ID
- 7342487
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- 本文言語コード
- ja
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可