Background characterization for subpixel target detection

Stanley Rotman, Sapir Ben-Yakar, Dan Blumberg

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

Abstract

When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general guidelines for when segmentation is useful.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1344-1346
Number of pages3
ISBN (Electronic)9781509049516
DOIs
StatePublished - 1 Dec 2017
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/TerritoryUnited States
CityFort Worth
Period23/07/1728/07/17

Keywords

  • Hyperspectral
  • Segmentation
  • Target Detection

Fingerprint

Dive into the research topics of 'Background characterization for subpixel target detection'. Together they form a unique fingerprint.

Cite this