Journal article
Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns
Technical University of Denmark1
Statistics and Data Analysis, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Department of Environmental Engineering, Technical University of Denmark4
Climate & Monitoring, Department of Environmental Engineering, Technical University of Denmark5
Air, Land & Water Resources, Department of Environmental Engineering, Technical University of Denmark6
Centro de Investigaciones Biologicas7
Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information.
EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used.
In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data.
The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%
Language: | English |
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Publisher: | MDPI AG |
Year: | 2021 |
Pages: | 4007 |
ISSN: | 20724292 |
Types: | Journal article |
DOI: | 10.3390/rs13194007 |
ORCIDs: | 0000-0002-6812-3299 , Baum, Andreas , García, Mónica and Stockmarr, Anders |
Data completion Imputation Machine learning Missing data Multiway analysis Remote sensing Tensor decomposition Tucker
Q Science data completion imputation missing data remote sensing tensor decomposition