Improved covariance matrix for target detection in hyperspectral imaging

Ilan Schvartzman, Shimrit Maman, Dan G. Blumberg, Stanley R. Rotman

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

Abstract

In many image processing applications, the estimation of the covariance matrix is considered an essential step. Estimating the covariance matrix has a great influence on the success or failure of a given algorithm. Usually the covariance matrix is estimated by the sampled covariance matrix of the whole data. The problem with doing so is that anomalies that exist in the data might distort the covariance matrix. This paper presents an approach for covariance matrix estimation that is less prone to anomalies and improves the detection rate. Results on simulations and real life images are presented.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

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