書誌事項
- タイトル別名
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- Land cover classification of satellite images using cooperative learning neural networks.
- タダン ニューラル ネットワーク ニ ヨル ジンコウ エイセイ ガゾウ ノ ト
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抄録
Maximum likelihood classifier that is often used in classification of satellite images assumes the distribution of each class to Gaussian. Such linear classifier can classify correctly when the case that classification probability of each class is exclusive. Remotely sensed data, however, belong to several classes and have non-linear separable condition. To improve the classification accuracy of non-linear separable data, the application of the single-step multi-layer back propagation neural networks have been studied by many researchers. In this paper, multi-step multi-layer neural networks, so called cooperative learning neural networks, are proposed to classify the non-linear separable satellite data.<BR>The cooperative learning neural network consists of extraction networks for each class and an unification network which unifies the extracted values. The unification network is also used for unification of different environments such as time-series data or neighboring regions. The result of the classification of LANDSAT TM data of Nagoya city using the cooperative learning neural network is introduced. Classified image is compared with the detailed digital land cover information (TDT-112) and the images classified using single-step multi-layer neural network, maximum likelihood classifier and fuzzy set reasoning. As the result of the comparison, the cooperative learning neural network classify the remote sensing data more exactly than the other methods.
収録刊行物
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- 写真測量とリモートセンシング
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写真測量とリモートセンシング 34 (1), 71-80, 1995
一般社団法人 日本写真測量学会
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詳細情報 詳細情報について
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- CRID
- 1390282679054545024
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- NII論文ID
- 10002049162
- 10003873517
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- NII書誌ID
- AN00111450
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- ISSN
- 18839061
- 02855844
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- NDL書誌ID
- 3595489
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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
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- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可