Improved covariance matrix for target detection in hyperspectral imaging

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

doi:10.1109/ICSEE.2016.7806135

Improved covariance matrix for target detection in hyperspectral imaging

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

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
ISBN (Electronic)	9781509021529
DOIs	https://doi.org/10.1109/ICSEE.2016.7806135
State	Published - 4 Jan 2017
Event	2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel Duration: 16 Nov 2016 → 18 Nov 2016

Publication series

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

Conference

Conference	2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/Territory	Israel
City	Eilat
Period	16/11/16 → 18/11/16

ASJC Scopus subject areas

Computer Science Applications
Hardware and Architecture
Artificial Intelligence
Computer Networks and Communications
Electrical and Electronic Engineering

Access to Document

10.1109/ICSEE.2016.7806135

Cite this

Schvartzman, I., Maman, S., Blumberg, D. G., & Rotman, S. R. (2017). Improved covariance matrix for target detection in hyperspectral imaging. In 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 Article 7806135 (2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016). https://doi.org/10.1109/ICSEE.2016.7806135

@inproceedings{ee23e117a4634d5d8a6d9d1b57628e48,

title = "Improved covariance matrix for target detection in hyperspectral imaging",

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.",

author = "Ilan Schvartzman and Shimrit Maman and Blumberg, \{Dan G.\} and Rotman, \{Stanley R.\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 ; Conference date: 16-11-2016 Through 18-11-2016",

year = "2017",

month = jan,

day = "4",

doi = "10.1109/ICSEE.2016.7806135",

language = "English",

series = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",

booktitle = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",

}

Schvartzman, I, Maman, S, Blumberg, DG & Rotman, SR 2017, Improved covariance matrix for target detection in hyperspectral imaging. in 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016., 7806135, 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016, 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016, Eilat, Israel, 16/11/16. https://doi.org/10.1109/ICSEE.2016.7806135

Improved covariance matrix for target detection in hyperspectral imaging. / Schvartzman, Ilan; Maman, Shimrit; Blumberg, Dan G. et al.
2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016. 2017. 7806135 (2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Improved covariance matrix for target detection in hyperspectral imaging

AU - Schvartzman, Ilan

AU - Maman, Shimrit

AU - Blumberg, Dan G.

AU - Rotman, Stanley R.

PY - 2017/1/4

Y1 - 2017/1/4

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85014196853

U2 - 10.1109/ICSEE.2016.7806135

DO - 10.1109/ICSEE.2016.7806135

M3 - Conference contribution

T3 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

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

T2 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Y2 - 16 November 2016 through 18 November 2016

ER -

Improved covariance matrix for target detection in hyperspectral imaging

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this