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Proceedings Paper

SST fusion analysis based on Kalman Filter and Spatiotemporal dimension
Author(s): Na Liu; Lingyu Xu; Yijun Xu; Jian Wang
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Paper Abstract

As for the problem of quality evaluation method of sea surface temperature (SST) observed by satellite remote sensing. An analysis model of SST is proposed based on the combination of observations at different time, different places and with different techniques. According to this model, Kalman Filter and the principle of non-negative matrix factorization can be used to fuse the SST data in temporal and spatial dimension when the data absence occurs. Through which an accurate estimation of SST observations will be made. The experiment results with SST data obtained in East China Sea in 2006, showed that the model presented in this article can obviously improve the precision of SST data estimation, which can provide accurate reference for the quality evaluation of marine information.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8783, Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing, 87830V (13 March 2013); doi: 10.1117/12.2013925
Show Author Affiliations
Na Liu, Shanghai Univ. (China)
Lingyu Xu, Shanghai Univ. (China)
Yijun Xu, Shanghai Univ. (China)
Jian Wang, Shanghai Univ. (China)


Published in SPIE Proceedings Vol. 8783:
Fifth International Conference on Machine Vision (ICMV 2012): Computer Vision, Image Analysis and Processing
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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