Improved Contextual Classifiers of Multispectral Image Data
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概要
- 論文の詳細を見る
Contextual classification of multispectral image data in remote sensing is discussed and concretely two improved contextual classifiers are proposed. The first is the extended adaptive classifier which partitions an image successively into homogeneously distributed square regions and applies a collective classification decision to each region. The second is the accelerated probabilistic relaxation which updates a classification result fast by adopting a pixelwise stopping rule. The evaluation experiment with a pseudo LANDSAT multispectral image shows that the proposed methods give higher classification accuracies than the compound decision method known as a standard contextual classifier.
- 社団法人電子情報通信学会の論文
- 1994-09-25
著者
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鈴木 仁
東京慈恵会医科大学
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TANBA SUMIO
Faculty of Science and Technology, Hirosaki University
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Tanba Sumio
Faculty Of Engineering Iwate University
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WATANABE Takashi
Faculty of Information, Shizuoka University
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Suzuki Hitoshi
Product Engineering Department, NEC Corporation
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Yokoyama Ryuzo
Faculty of Engineering, Iwate University
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Yokoyama Ryuzo
Faculty Of Engineering Iwate University
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Watanabe Takashi
Faculty Of Engineering Iwate University
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Watanabe Takashi
Faculty Mechanical Engineering Nagoya University
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