NDLI: Self-Organizing Map for Erroneous Data Processing in Time Series Analysis

Content Provider	IEEE Xplore Digital Library
Author	Latif, B.A. Mercier, G.
Copyright Year	2006
Description	Author affiliation: Dept. ITI, Technopole Brest-Iroise, Brest (Latif, B.A.; Mercier, G.)
Abstract	In the context of this study, changes in the vegetation coverage are to be detected in the Brittany region in France, based on seasonal and regional scales. Thus, many low resolution images from MODIS satellite are considered per season. Unfortunately, the Brittany region in the west of France is often covered by clouds, and few dates per season with clear coverage are available. A nonparametric regression procedure, depending on the presence of clear small zones during the season, for predicting the value of data contaminated by weather conditions is presented. The problem is the erroneous data (pixels contaminated by clouds or shadows), which may be seen as a problem of an incomplete data as soon as an outlier detection technique is properly chosen and applied. The idea is to use the Kohonen's self organizing map (SOM) onto clear zones (free of clouds and shadow) for training. Then, incomplete data may be recovered by projecting the data onto this trained SOM. To detect pixels contaminated by clouds and shadows, the box and whisker method has been used with an overall accuracy of 97.7636% and a kappa coefficient of 0.9652.
Starting Page	196
Ending Page	199
File Size	2056956
Page Count	4
File Format	PDF
ISBN	0780395107
DOI	10.1109/IGARSS.2006.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data processing Time series analysis Clouds Vegetation mapping MODIS Image resolution Satellites Weather forecasting Organizing Pixel
Content Type	Text
Resource Type	Article

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