Spatial Constrained Hyperspectral Reconstruction from RGB Inputs Using Dictionary Representation

Yunhao Geng, Shaohui Mei, Jin Tian, Yifan Zhang, Qian Du

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

16 Scopus citations

Abstract

Reconstructing hyperspectral images from RGB inputs has gained great attention recently. In dictionary representation-based hyperspectral image reconstruction, dictionary representation is first carried out in RGB space and then dictionary reconstruction is conducted in hyperspectral space for per-pixel reconstruction. However, such work mainly focuses on spectral mapping from RGB space to hyperspectral space, ignoring physical distribution of objects in the image. In this paper, spatial context of pixels is used to improve the reconstruction performance. Specially, neighboring pixels are used to constrain the dictionary representation problem in RGB space, and the Simultaneous Orthogonal Matching Pursuit (SOMP) is used to improve the performance of hyperspectral reconstruction. Experimental results on two benchmark data sets demonstrate the superiority of the proposed technique..

Original languageEnglish
Title of host publication2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3169-3172
Number of pages4
ISBN (Electronic)9781538691540
DOIs
StatePublished - Jul 2019
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

  • Dictionary learning
  • Hyperspectral
  • SOMP
  • Sparse representation

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