Covariance Decomposition
Covariance Decomposition is a mathematical framework within spatial covariance modeling that decomposes the overall covariance structure of spatially referenced data into additive or multiplicative components representing distinct sources of spatial dependence, such as local and global effects, anisotropy, or scale-specific variability.
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4.3K
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215
Authors
120
Institutions
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2017–2026
17
Parents
73
S. Kraut, Louis L. Scharf, L.T. McWhorter · IEEE Transactions on Signal Processing · 2001 · 719 citations · Full text
G. Fornaro, Simona Verde, Diego Reale et al. · IEEE Transactions on Geoscience and Remote Sensing · 2014 · 314 citations
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University of Leeds
Leeds, United Kingdom
3 h-index
4 publications
145 citations
Princeton University
Princeton, United States
3 h-index
8 publications
1.5K citations
University of Michigan
Ann Arbor, United States
2 h-index
3 publications
302 citations

Northern Illinois University
DeKalb, United States
2 h-index
7 publications
296 citations
Heinrich Heine University Düsseldorf
Düsseldorf, Germany
2 h-index
6 publications
279 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
2 publications
107 citations
Montreal Neurological Institute and Hospital
Montreal, Canada
2 h-index
2 publications
74 citations

Johns Hopkins University
Baltimore, United States
2 h-index
2 publications
50 citations
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4 publications · 340 citations
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2 publications · 738 citations
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