TY - GEN
T1 - Single Hyperspectral Image Super-Resolution Using Admm-Adam Theory
AU - Lin, Tzu Hsuan
AU - Lin, Chia Hsiang
N1 - Funding Information:
This study was supported partly by the Einstein Program (Young Scholar Fellowship Program) of Ministry of Science and Technology (MOST), Taiwan, under Grant MOST 110-2636-E-006-026; and partly by the Higher Education Sprout Project of Ministry of Education (MOE) to the Headquarters of University Advancement at National Cheng Kung University (NCKU).
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
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U2 - 10.1109/IGARSS46834.2022.9883334
DO - 10.1109/IGARSS46834.2022.9883334
M3 - Conference contribution
AN - SCOPUS:85140380152
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1756
EP - 1759
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -