Towards a Unified Formalism of Multivariate Coefficients of Variation: Application to the Analysis of Polarimetric Speckle Time Series

The main aim of this article is to unify the different formalisms of multivariate coefficients of variation, based on the advanced concepts of generalized averages - weighted or unweighted - applied to the eigenvalues of covariance matrices. We highlight the existence of an infinite number of these...

Full description

Saved in:
Bibliographic Details
Published inJournal of the Indian Society of Remote Sensing Vol. 52; no. 12; pp. 2625 - 2636
Main Authors Colin, Elise, Ossikovski, Razvigor
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.12.2024
Springer Nature B.V
Subjects
Coefficient of variation
covariance
Covariance matrix
Earth and Environmental Science
Earth Sciences
Eigenvalues
Engineering Sciences
Formalism
Multivariate analysis
Physics
polarimetry
radar
Radar detection
Radar imaging
Radar polarimetry
Remote Sensing/Photogrammetry
Research Article
Time series
time series analysis
Time-series
Polarimetry
Coefficient of variation
Dynamic speckle
speckle
polarimetry
radar
dynamic speckle
time-series
dynamic speckle coefficient of variation time-series polarimetry
sar
coefficient of variation
Online AccessGet full text

Cover

More Information
Summary:The main aim of this article is to unify the different formalisms of multivariate coefficients of variation, based on the advanced concepts of generalized averages - weighted or unweighted - applied to the eigenvalues of covariance matrices. We highlight the existence of an infinite number of these coefficients and demonstrate that they are bounded. Furthermore, we link the various coefficients of variation identified in the literature to specific instances within our unified formalism. We illustrate the utility of our method by applying it to a time series of polarimetric radar imagery, where the coefficient of variation emerges as a key tool for detecting changes or identifying permanent scatterers characterized by remarkable temporal stability. Multidimensionality arises from the diversity of polarizations. The introduction of various possible coefficients shows how their selection impacts the detection of samples exhibiting specific temporal behaviors, underlining the contribution of polarimetry to dynamic speckle analysis.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0255-660X
0974-3006
DOI:10.1007/s12524-024-02005-x