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Universities","award":["202306960040"],"award-info":[{"award-number":["202306960040"]}]},{"name":"China Scholarship Council","award":["2019YFA0706604"],"award-info":[{"award-number":["2019YFA0706604"]}]},{"name":"China Scholarship Council","award":["62205260"],"award-info":[{"award-number":["62205260"]}]},{"name":"China Scholarship Council","award":["62293483"],"award-info":[{"award-number":["62293483"]}]},{"name":"China Scholarship Council","award":["202201000001"],"award-info":[{"award-number":["202201000001"]}]},{"name":"China Scholarship Council","award":["2022K0904"],"award-info":[{"award-number":["2022K0904"]}]},{"name":"China Scholarship Council","award":["YJSJ24006"],"award-info":[{"award-number":["YJSJ24006"]}]},{"name":"China Scholarship Council","award":["202306960040"],"award-info":[{"award-number":["202306960040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of different models. In this paper, we propose a novel no-reference HSI quality assessment metric via ranking feature learning (R-NHSIQA), which calculates the Wasserstein distance between the distribution of the deep features of the reconstructed HSIs and the benchmark distribution. Additionally, by introducing the spectral self-attention mechanism, we propose a Spectral Transformer (S-Transformer) to extract the spatial-spectral representative deep features of HSIs. Furthermore, to extract quality-sensitive deep features, we use quality ranking as a pre-training task to enhance the representation capability of the S-Transformer. Finally, we introduce the Wasserstein distance to measure the distance between the distribution of the deep features and the benchmark distribution, improving the assessment capacity of our method, even with non-overlapping distributions. The experimental results demonstrate that the proposed metric yields consistent results with multiple full-reference image quality assessment (FR-IQA) metrics, validating the idea that the proposed metric can serve as a substitute for FR-IQA metrics in real-world tasks.<\/jats:p>","DOI":"10.3390\/rs16101657","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T09:58:56Z","timestamp":1715162336000},"page":"1657","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9727-0157","authenticated-orcid":false,"given":"Yuyan","family":"Li","sequence":"first","affiliation":[{"name":"Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubo","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710068, China"},{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyong","family":"Li","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710068, China"},{"name":"Pazhou Lab, Huangpu 510555, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Xue","sequence":"additional","affiliation":[{"name":"Beijing Institute of Space Mechanics and Electricity, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dahua","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Borengasser, M., Hungate, W.S., and Watkins, R. 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