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

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.