Parametric adaptive signal detection for hyperspectral imaging

Hongbin Li, James H. Michels

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this paper, we introduce a class at training-efficient adaptive signal detectors that exploit a parametric model taking into account the non-stationarity of H SI data in the spectral dimension. A maximum likelihood (ML) estimator is presented for estimation of the parameters associated with the proposed parametric model. Several important issues are discussed, including model order selection, training screening, and time-series based whitening and detection, which are intrinsic parts of the proposed parametric adaptive detectors. Experimental results using real HSI data reveal that the proposed parametric detectors are more training-efficient and outperform conventional covariance-matrix based detectors when the training size is limited.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesV1197-V1200
StatePublished - 2006
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 14 May 200619 May 2006

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
Country/TerritoryFrance
CityToulouse
Period14/05/0619/05/06

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