Hyperspectral super-resolution technique using histogram matching and endmember optimization

Byunghyun Kim, Soojin Cho

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In most hyperspectral super-resolution (HSR) methods, which are techniques used to improve the resolution of hyperspectral images (HSIs), the HSI and the target RGB image are assumed to have identical fields of view. However, because implementing these identical fields of view is difficult in practical applications, in this paper, we propose a HSR method that is applicable when an HSI and a target RGB image have different spatial information. The proposed HSR method first creates a low-resolution RGB image from a given HSI. Next, a histogram matching is performed on a high-resolution RGB image and a low-resolution RGB image obtained from an HSI. Finally, the proposed method optimizes endmember abundance of the high-resolution HSI towards the histogram-matched high-resolution RGB image. The entire procedure is evaluated using an open HSI dataset, the Harvard dataset, by adding spatial mismatch to the dataset. The spatial mismatch is implemented by shear transformation and cutting off the upper and left sides of the target RGB image. The proposed method achieved a lower error rate across the entire dataset, confirming its capability for super-resolution using images that have different fields of view.

Original languageEnglish
Article number4444
JournalApplied Sciences (Switzerland)
Volume9
Issue number20
DOIs
StatePublished - 1 Oct 2019

Keywords

  • Histogram equalization
  • Hyperspectral image
  • Super-resolution

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