Single Hyperspectral Image Super-Resolution Using Admm-Adam Theory

Tzu Hsuan Lin, Chia Hsiang Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In the remote sensing field, the spatial resolution of hyperspectral images (HSIs) is poor compared to RGB and multispectral images. Hence, hyperspectral image super-resolution (HISR) has become a popular topic recently. A branch of HISR methods is based on image fusion, but these methods rely on high-spatial-resolution counterpart image (e.g., multispectral image of the same scene) that is, however, not always available. Therefore, developing single hyperspectral image super-resolution (SHISR) method is highly desired. Due to the lack of abundant high-quality HSIs (i.e., big data) in satellite remote sensing, deep learning itself would be insufficient to well solve SHISR. We solve SHISR based on the recently invented ADMM-Adam learning theory, which blends the advantages from deep learning and convex optimization, thereby allowing software engineers to solve various challenging inverse problems without big data and sophisticated regularizer. For the first time, ADMM-Adam is adopted to solve SHISR in this paper, and experimental evidences well support its superiority even just with small data.

Original languageEnglish
Title of host publicationIGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1756-1759
Number of pages4
ISBN (Electronic)9781665427920
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
Duration: 2022 Jul 172022 Jul 22

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Country/TerritoryMalaysia
CityKuala Lumpur
Period22-07-1722-07-22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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