Predicting Missing Values in Spatio-Temporal Remote Sensing Data

Remotely sensed data are sparse, which means that data have missing values, for instance due to cloud cover. This is problematic for applications and signal processing algorithms that require complete data sets. To address the sparse data issue, we present a new gap-fill algorithm. The proposed method predicts each missing value separately based on data points in a spatio-temporal neighborhood around the missing data point. The computational...