Review paper

Predicting Missing Values in Spatio-Temporal Remote Sensing Data

Volume: 56, Issue: 5, Pages: 2841 - 2853
Published: May 1, 2018
Abstract
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...
Paper Details
Title
Predicting Missing Values in Spatio-Temporal Remote Sensing Data
Published Date
May 1, 2018
Volume
56
Issue
5
Pages
2841 - 2853
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