期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 43, 期 12, 页码 4569-4607出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2022.2114109
关键词
super-resolution; Hyperion; PRISMA; machine learning; image quality evaluation
This study focuses on single-frame super-resolution (SFSR) for real-world hyperspectral images and proposes a method for comparing and validating the super-resolved outputs. The results suggest that the reconstruction and hybrid-based algorithms outperform the learning-based algorithms in producing enhanced spatial and spectral fidelity outputs.
Few studies on single-frame super-resolution (SFSR) exist for real-world hyperspectral images due to fewer training samples and paucity of freely available high-resolution ground truth for validation. Consequently, a user relies on the simulated results from test datasets. This gap is filled by super-resolving real-time Hyperion and PRISMA data of Ahmedabad in India and introducing a structure of comparing and validating super-resolved outputs. Proposing open-source panchromatic and multispectral data as visual examination references, the first-ever SFSR results on PRISMA are presented. Findings suggest reconstruction-based SFSR algorithm outperforms at scale factors (SFs) 2 and 4 for Hyperion and SFs 2, 3 and 4 for PRISMA. The hybrid SFSR algorithm stands out at SF 3 for Hyperion. External dictionaries utilized by learning-based SFSR algorithms experience degradation with increasing spatial resolution. Although image quality declines slightly at higher SFs, out of the chosen algorithms, reconstruction and hybrid-based algorithms outperform learning-based algorithms in producing enhanced spatial and spectral fidelity outputs in the present case.
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