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Remote Sensing Technology and Application
    
Data Fusion and Visualization of Multi-time OceanColor Remote Sensing Data
Zhang Xiaofeng1,Lü Xiaoqi1,2,Zhang Xinxue1,Zhang Jikai1,Wang Yueming1,Gu Yu1,2,Fan Yu1
(1.School of Information Engineering,Inner Mongolia University of Science and Technology,Key Laboratory of Pattern Recognition and Intelligent Image Processing in Inner MongoliaAutonomous Region,Inner Mongolia Autonomous Region,Baotou 014010,China;
2.School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China)
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Abstract  In order to solve the problem that the ocean sensor data such as MODIS and SeaWiFs are easy to emptied,an algorithm combining multi\|time data with multiple fitting methods is proposed.The vacancy value is estimated by the least squares fitting method.To set a threshold named A according to the date of different time in corresponding position.When the effective value is larger than A that the vacancy value is estimated by the method of least squares fitting,when the effective value is smaller than A that the linear interpolation method is used to estimate the vacancy value.There is no valid value the bilinear interpolation method is used for the second repair with the valid data of the adjacent position.The experimental results show that the algorithm has a smooth visual effect when the threshold selection is appropriate,and the error of the data analysis results is relatively small,then the global data can be well restored and the effectiveness of data coverage will be improved.
Key words:  Data fusion      Sea color remote sensing      Multi-sensor      Marine chlorophyll     
Received:  11 November 2017     
ZTFLH:  TP391.41  
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Zhang Xiaofeng
Lü Xiaoqi
Zhang Xinxue
Zhang Jikai
Wang Yueming
Gu Yu
Fan Yu

Cite this article: 

Zhang Xiaofeng, Lü Xiaoqi, Zhang Xinxue, Zhang Jikai, Wang Yueming, Gu Yu, Fan Yu. Data Fusion and Visualization of Multi-time OceanColor Remote Sensing Data. Remote Sensing Technology and Application, 2018, 33(5): 873-880.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.5.0873     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I5/873

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