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Background Guided Target Detection for Hyperspectral Image

  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

Target detection has become an increasingly important research topic in hyperspectral image (HSI) processing with most existing detecting algorithms focus on the information of required targets while fail to utilize the background of input data. In this paper, we propose a novel target detection method for HSI, which deals with the detection problem from a new contextual perspective. Based on an initial detection result, the background of HSI is extracted by introducing the joint convolutional analysis and synthesis (JCAS) sparse representation method. Then, the initial result is corrected through a background guided approach, false alarm is greatly reduced. Experimental results show that the proposed method performs well in hyperspectral target detection.

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages568-571
Number of pages4
ISBN (Electronic)9781538691540
DOIs
StatePublished - Jul 2019
Externally publishedYes
Event39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan
Duration: 28 Jul 20192 Aug 2019

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/TerritoryJapan
CityYokohama
Period28/07/192/08/19

Keywords

  • Hyperspectral image
  • JCAS sparse representation
  • background guided
  • target detection

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