Using improved outlier estimation for hyperspectral target detection

Sagiv Dvash, Stanley Rotman

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

Keywords

  • Covariance estimation
  • Hyperspectral target detection
  • Matched filter
  • Outlier estimation

ASJC Scopus subject areas

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

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