Shrinkage estimators for covariance matrices in spectral remote sensing

James Theiler

doi:10.1117/12.3013942

7 June 2024 Shrinkage estimators for covariance matrices in spectral remote sensing

James Theiler

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 1303102 (2024) https://doi.org/10.1117/12.3013942
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

This talk describes investigations of shrinkage parameter estimates for covariance matrices used in spectral processing of remote sensing imagery, such as for target, anomaly, or change detection. These estimates are derived in the context of cross-validated fiting of Gaussian likelihood models to the non-target background distribution. Here, the utility of these estimates is evaluated for Gaussian and non-Gaussian distributions. An alternative criterion, based on matched-filter detection of “generic” targets, is derived and compared to the estimated likelihood criterion as a way to choose the shrinkage parameter.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

James Theiler "Shrinkage estimators for covariance matrices in spectral remote sensing", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 1303102 (7 June 2024); https://doi.org/10.1117/12.3013942

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