Reconstruction From Multispectral to Hyperspectral Image Using Spectral Library-Based Dictionary Learning

Xiaolin Han*, Jing Yu, Jiqiang Luo, Weidong Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

High-spatial hyperspectral (HH) image reconstruction using both high-spatial multispectral (HM) image and low-spatial hyperspectral (LH) image over the same scene is widely used in many real applications. Nevertheless, the pair of HM image and LH image over the same scene is hard to obtain. To solve this problem, a new HH image reconstruction method using spectral library-based dictionary learning (named as HIRSL) is proposed in this paper, only from one HM image. The above reconstruction problem is formulated in the framework of sparse representation, as an estimation of the band matching matrix, the spectral dictionary, and the sparse coefficients. More specifically, a band matching method is proposed for mapping the common spectral library to a specific spectral library corresponding to the reconstructed HH image in spectral domain. Then, an efficient spectral dictionary learning method is proposed for the construction of spectral dictionary using the matched specific spectral library, which avoids the dependence of the LH image over the same scene. Finally, the sparse coefficients of the HM image with respect to the learned spectral dictionary are estimated using the alternating direction method of multipliers without nonnegative constraint. Comparison results on simulated and real data sets with the relative state-of-the-art methods demonstrate that even only using one HM image, our proposed method achieves a comparable reconstruction quality of high-spatial hyperspectral image both in spatial and spectral domains.

Original languageEnglish
Article number8465699
Pages (from-to)1325-1335
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number3
DOIs
Publication statusPublished - Mar 2019
Externally publishedYes

Keywords

  • Hyperspectral image reconstruction
  • sparse representation
  • spectral dictionary learning
  • spectral library

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