{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:41:04Z","timestamp":1760125264743,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T00:00:00Z","timestamp":1679270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"],"award-info":[{"award-number":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"]}]},{"name":"National Science Foundation of China","award":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"],"award-info":[{"award-number":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"]}]},{"name":"Natural Science Foundation of XinJiang Uygur Autonomous Region","award":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"],"award-info":[{"award-number":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"]}]},{"name":"Autonomous Region Graduate Innovation Project","award":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"],"award-info":[{"award-number":["61966035","62266043","U1803261","2021D01C077","XJ2019G069","XJ2021G062","XJ2020G074"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The purpose of the panchromatic sharpening of remote sensing images is to generate high-resolution multispectral images through software technology without increasing economic expenditure. The specific method is to fuse the spatial information of a high-resolution panchromatic image and the spectral information of a low-resolution multispectral image. This work proposes a novel model for generating high-quality multispectral images. This model uses the feature domain of the convolution neural network to fuse multispectral and panchromatic images so that the fused images can generate new features so that the final fused features can restore clear images. Because of the unique feature extraction ability of convolution neural networks, we use the core idea of convolution neural networks to extract global features. To extract the complementary features of the input image at a deeper level, we first designed two subnetworks with the same structure but different weights, and then used single-channel attention to optimize the fused features to improve the final fusion performance. We select the public data set widely used in this field to verify the validity of the model. The experimental results on the GaoFen-2 and SPOT6 data sets show that this method has a better effect in fusing multi-spectral and panchromatic images. Compared with the classical and the latest methods in this field, our model fusion obtained panchromatic sharpened images from both quantitative and qualitative analysis has achieved better results. In addition, to verify the transferability and generalization of our proposed model, we directly apply it to multispectral image sharpening, such as hyperspectral image sharpening. Experiments and tests have been carried out on Pavia Center and Botswana public hyperspectral data sets, and the results show that the model has also achieved good performance in hyperspectral data sets.<\/jats:p>","DOI":"10.3390\/s23063275","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T07:06:37Z","timestamp":1679295997000},"page":"3275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Pansharpening Model of Transferable Remote Sensing Images Based on Feature Fusion and Attention Modules"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7868-8314","authenticated-orcid":false,"given":"Hui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Xinjiang University, Urumqi 830014, China"},{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830008, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangfeng","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yibo","family":"Dou","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiwu","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830008, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830046, China"},{"name":"School of Software, Xinjiang University, Urumqi 830008, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.isprsjprs.2019.10.014","article-title":"Metaheuristic pansharpening based on symbiotic organisms search optimization","volume":"158","author":"Yilmaz","year":"2019","journal-title":"ISPRS J. 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