{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:17:31Z","timestamp":1773238651294,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Image Quality Assessment (IQA) method is proposed. In the method, responses of two complementary network architectures in a form of extracted multi-level representations of PS images are employed as quality-aware information. Specifically, high-dimensional data are separately extracted from the layers of the networks and further processed with the Kernel Principal Component Analysis (KPCA) to obtain features used to create a PS quality model. Extensive experimental comparison of the method on the large database of PS images against the state-of-the-art techniques, including popular NR methods adapted in this study to the PS IQA, indicates its superiority in terms of typical criteria.<\/jats:p>","DOI":"10.3390\/rs14051119","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6614-1218","authenticated-orcid":false,"given":"Igor","family":"St\u0119pie\u0144","sequence":"first","affiliation":[{"name":"Doctoral School of Engineering and Technical Sciences, Rzeszow University of Technology, al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5482-6313","authenticated-orcid":false,"given":"Mariusz","family":"Oszust","sequence":"additional","affiliation":[{"name":"Department of Computer and Control Engineering, Rzeszow University of Technology, Wincentego Pola 2, 35-959 Rzeszow, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Agudelo-Medina, O.A., Benitez-Restrepo, H.D., Vivone, G., and Bovik, A. (2019). Perceptual Quality Assessment of Pan-Sharpened Images. Remote Sens., 11.","DOI":"10.3390\/rs11070877"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Govind, N.R., Rishikeshan, C.A., and Ramesh, H. (2019, January 29\u201331). Comparison of Different Pan Sharpening Techniques using Landsat 8 Imagery. 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