書誌事項
- タイトル別名
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- Prediction of Missing ASTER/VNIR Data Based on Kalman Filter Using Simultaneously Acquired MODIS Data as a Mean Value of Time Series Data in Revision Process of Filter Status
- MODIS データ オ ヘイキンチ ジケイレツ ト シテ モチイル カルマン フィルタ ニ モトズク ASTER VNIR ケツソク データ ノ ヨソク
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抄録
A prediction method based on Kalman filter using mean value of time series data derived from the other source is proposed. As an example of the proposed method, prediction of missing ASTER/VNIR data based on Kalman filter using simultaneously acquired MODIS data as a mean value of time series data in revision of filter status is attempted together with a comparative study of prediction errors for both conventional Kalman filter and the proposed modified Kalman filter which utilizes mean value of time series data derived from the other sources. Experimental data shows that 4 to 111% of prediction error reduction can be achieved by the proposed modified Kalman filter in comparison to the conventional Kalman filter. It is found that the reduction rate depends on the mean value accuracy of time series data derived from the other data sources.
収録刊行物
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- 日本リモートセンシング学会誌
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日本リモートセンシング学会誌 30 (3), 141-148, 2010
一般社団法人 日本リモートセンシング学会
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詳細情報 詳細情報について
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- CRID
- 1390282679642955392
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- NII論文ID
- 10026874472
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- NII書誌ID
- AN10035665
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- ISSN
- 18831184
- 02897911
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- NDL書誌ID
- 10750419
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可