{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:04:25Z","timestamp":1784631865799,"version":"3.55.0"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,30]],"date-time":"2019-03-30T00:00:00Z","timestamp":1553904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61527805"],"award-info":[{"award-number":["61527805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61731001"],"award-info":[{"award-number":["61731001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41775030"],"award-info":[{"award-number":["41775030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"111 Project of China","award":["B14010"],"award-info":[{"award-number":["B14010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite microwave radiometer data is affected by many degradation factors during the imaging process, such as the sampling interval, antenna pattern and scan mode, etc., leading to spatial resolution reduction. In this paper, a deep residual convolutional neural network (CNN) is proposed to solve these degradation problems by learning the end-to-end mapping between low-and high-resolution images. Unlike traditional methods that handle each degradation factor separately, our network jointly learns both the sampling interval limitation and the comprehensive degeneration factors, including the antenna pattern, receiver sensitivity and scan mode, during the training process. Moreover, due to the powerful mapping capability of the deep residual CNN, our method achieves better resolution enhancement results both quantitatively and qualitatively than the methods in literature. The microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite has been used to demonstrate the validity and the effectiveness of the method.<\/jats:p>","DOI":"10.3390\/rs11070771","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T03:21:26Z","timestamp":1554175286000},"page":"771","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Spatial Resolution Enhancement of Satellite Microwave Radiometer Data with Deep Residual Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Weidong","family":"Hu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0632-2898","authenticated-orcid":false,"given":"Yade","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lv","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Ligthart","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Delft University of Technology, 2600 GA Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,30]]},"reference":[{"key":"ref_1","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. 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