Single Hyperspectral Image Super-Resolution Using Admm-Adam Theory

Tzu Hsuan Lin, Chia Hsiang Lin

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1756-1759
頁數4
ISBN(電子)9781665427920
DOIs
出版狀態Published - 2022
事件2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 - Kuala Lumpur, Malaysia
持續時間: 2022 7月 172022 7月 22

出版系列

名字International Geoscience and Remote Sensing Symposium (IGARSS)
2022-July

Conference

Conference2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
國家/地區Malaysia
城市Kuala Lumpur
期間22-07-1722-07-22

All Science Journal Classification (ASJC) codes

  • 電腦科學應用
  • 地球與行星科學(全部)

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