Using improved outlier estimation for hyperspectral target detection

Sagiv Dvash; Stanley Rotman

doi:10.1109/ICSEE.2016.7806194

Using improved outlier estimation for hyperspectral target detection

Sagiv Dvash, Stanley Rotman

Department of Electrical & Computer Engineering

Ben-Gurion University of the Negev

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

Abstract

We present a thorough examination of different noise estimation methods for usage with target detection algorithms for hyperspectral datasets. The different algorithms were designed with two approaches for dealing with outliers: The first allows outliers to contribute beyond their actual population to the estimated distribution, while the second approach limits them. In Addition, the matched filter distribution on the eigen-direction was analyzed using PCA for each algorithm, presenting a novel way to compare and examine the behavior of target detection method.

Original language	American 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.2016.7806194
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

Keywords

Covariance estimation
Hyperspectral target detection
Matched filter
Outlier estimation

All Science Journal Classification (ASJC) codes

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

Access to Document

10.1109/ICSEE.2016.7806194

Cite this

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title = "Using improved outlier estimation for hyperspectral target detection",

abstract = "We present a thorough examination of different noise estimation methods for usage with target detection algorithms for hyperspectral datasets. The different algorithms were designed with two approaches for dealing with outliers: The first allows outliers to contribute beyond their actual population to the estimated distribution, while the second approach limits them. In Addition, the matched filter distribution on the eigen-direction was analyzed using PCA for each algorithm, presenting a novel way to compare and examine the behavior of target detection method.",

keywords = "Covariance estimation, Hyperspectral target detection, Matched filter, Outlier estimation",

author = "Sagiv Dvash and Stanley Rotman",

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.2016.7806194",

language = "American 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",

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Dvash, S & Rotman, S 2017, Using improved outlier estimation for hyperspectral target detection. in 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016., 7806194, 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.2016.7806194

Using improved outlier estimation for hyperspectral target detection. / Dvash, Sagiv; Rotman, Stanley.
2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016. 2017. 7806194 (2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016).

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

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AU - Dvash, Sagiv

AU - Rotman, Stanley

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N2 - We present a thorough examination of different noise estimation methods for usage with target detection algorithms for hyperspectral datasets. The different algorithms were designed with two approaches for dealing with outliers: The first allows outliers to contribute beyond their actual population to the estimated distribution, while the second approach limits them. In Addition, the matched filter distribution on the eigen-direction was analyzed using PCA for each algorithm, presenting a novel way to compare and examine the behavior of target detection method.

AB - We present a thorough examination of different noise estimation methods for usage with target detection algorithms for hyperspectral datasets. The different algorithms were designed with two approaches for dealing with outliers: The first allows outliers to contribute beyond their actual population to the estimated distribution, while the second approach limits them. In Addition, the matched filter distribution on the eigen-direction was analyzed using PCA for each algorithm, presenting a novel way to compare and examine the behavior of target detection method.

KW - Covariance estimation

KW - Hyperspectral target detection

KW - Matched filter

KW - Outlier estimation

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DO - 10.1109/ICSEE.2016.7806194

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 -

Using improved outlier estimation for hyperspectral target detection

Abstract

Publication series

Conference

Keywords

All Science Journal Classification (ASJC) codes

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