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

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  • MODISデータを平均値時系列として用いるカルマンフィルタに基づくASTER/VNIR欠測データの予測
  • 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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Abstract

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.

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