Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns

Remote Sensing

卷:13

Andreas Baum¹  Anders Stockmarr¹  Andri Freyr Þórðarson¹  Mónica García²  Sergio M. Vicente-Serrano³ 

[1] Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark;

[2] Department of Environmental Engineering, Technical University of Denmark, 2800 Lyngby, Denmark;

[3] IPE-CSIC, Intituto Pirenaico de Ecologia, Consejo Superior de Investigaciones Cientificas, 50059 Zaragoza, Spain;

关键词: missing data;    imputation;    data completion;    remote sensing;    tensor decomposition;    Tucker;   

DOI  :  10.3390/rs13194007

来源: DOAJ

【 摘 要 】

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%.

【 授权许可】

Unknown