期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 60, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2020.3039534
关键词
Image reconstruction; Spatial resolution; Optimization; Computer science; Training; Spectral analysis; Sparse matrices; Hyperspectral image super-resolution (SR); pixel-aware refinement; unsupervised deep learning
类别
资金
- National Natural Science Foundation of China [61671385, 62071387, U19B2037]
- Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20190806160210899]
- Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University [CX2020025]
This study investigates a fusion-based hyperspectral imagery super-resolution framework with the deep image prior and unsupervised recurrence, aiming to improve accuracy and robustness.
Unsupervised fusion-based hyperspectral imagery (HSI) super-resolution (SR) is an essential task of HSI processing, which aims to reconstruct a high-resolution (HR) HSI using only an observed low-resolution HSI and a conventional HR image. Although a large number of unsupervised HSI SR methods have been proposed, the heuristic handcrafted image priors adopted by the majority of these methods restrict their capacity to capture specific characteristics of the HSI, as well as their ability to generalize to noisy observation images. In this study, we investigate a fusion-based HSI SR framework with the deep image prior, in which the deep neural network (rather than a heuristic handcrafted image prior) is exploited to capture plenty of image statistics. Within this framework, we further propose an unsupervised recurrence-based HSI SR method using pixel-aware refinement, which utilizes the intermediate reconstruction results to self-supervise unsupervised learning. Due to containing the information of the image-specific characteristic, the proposed method achieves better performance, in terms of both accuracy and robustness to noise, compared with the existing methods. Extensive experiments on four HSI data sets demonstrate the effectiveness of the proposed method.
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