{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:49Z","timestamp":1760240869727,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2019,10,19]],"date-time":"2019-10-19T00:00:00Z","timestamp":1571443200000},"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","61731001","41775030"],"award-info":[{"award-number":["61527805","61731001","41775030"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Passive multi-frequency microwave remote sensing is often plagued with the problems of low- and non-uniform spatial resolution. In order to adaptively enhance and match the spatial resolution, an accommodative spatial resolution matching (ASRM) framework, composed of the flexible degradation model, the deep residual convolutional neural network (CNN), and the adaptive feature modification (AdaFM) layers, is proposed in this paper. More specifically, a flexible degradation model, based on the imaging process of the microwave radiometer, is firstly proposed to generate suitable datasets for various levels of matching tasks. Secondly, a deep residual CNN is introduced to jointly learn the complicated degradation factors of the data, so that the resolution can be matched up to fixed levels with state of the art quality. Finally, the AdaFM layers are added to the network in order to handle arbitrary and continuous resolution matching problems between a start and an end level. Both the simulated and the microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite have been used to demonstrate the validity and the effectiveness of the method.<\/jats:p>","DOI":"10.3390\/rs11202432","type":"journal-article","created":{"date-parts":[[2019,10,21]],"date-time":"2019-10-21T03:40:29Z","timestamp":1571629229000},"page":"2432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Spatial Resolution Matching of Microwave Radiometer Data with Convolutional Neural Network"],"prefix":"10.3390","volume":"11","author":[{"given":"Yade","family":"Li","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Hu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Millimeter Wave and Terahertz Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"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":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"He","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-4560","authenticated-orcid":false,"given":"Leo","family":"Ligthart","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Delft University of Technology, 2600 GA Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,19]]},"reference":[{"key":"ref_1","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1981). Microwave Remote Sensing: Active and Passive, Volume I: Microwave Remote Sensing Fundamentals and Radiometry, Artech House."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4846","DOI":"10.1109\/TGRS.2012.2197826","article-title":"Overview of FY-3 payload and ground application system","volume":"50","author":"Yang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4552","DOI":"10.1109\/TGRS.2011.2148200","article-title":"The FengYun-3 microwave radiation imager on-orbit verification","volume":"49","author":"Yang","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4986","DOI":"10.1109\/TGRS.2012.2197003","article-title":"Environmental data records from FengYun-3B microwave radiation imager","volume":"50","author":"Yang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/36.142920","article-title":"A technique for enhancing and matching the resolution of microwave measurements from the SSM\/I instrument","volume":"30","author":"Robinson","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1007\/s11430-010-4074-0","article-title":"The development of an algorithm to enhance and match the resolution of satellite measurements from AMSR-E","volume":"54","author":"Wang","year":"2011","journal-title":"Sci. China Earth Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3481","DOI":"10.1029\/1999GL010492","article-title":"The impact of the SSM\/I antenna gain function on land surface parameter retrieval","volume":"26","author":"Drusch","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/TGRS.2015.2458851","article-title":"Estimation and correction of geolocation errors in FengYun-3C Microwave Radiation Imager Data","volume":"54","author":"Tang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1109\/36.338362","article-title":"Spatial resolution improvement of SSM\/I data with image restoration techniques","volume":"32","author":"Sethmann","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1109\/36.662726","article-title":"Spatial resolution enhancement of SSM\/I data","volume":"36","author":"Long","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hu, W., Li, Y., Zhang, W., Chen, S., Lv, X., and Ligthart, L. (2019). Spatial resolution enhancement of satellite microwave radiometer data with deep residual convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11070771"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1109\/TAP.1978.1141919","article-title":"Estimates of brightness temperatures from scanning radiometer data","volume":"26","author":"Stogryn","year":"1978","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1109\/TGRS.2005.844099","article-title":"Microwave radiometer spatial resolution enhancement","volume":"43","author":"Migliaccio","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"083656","DOI":"10.1117\/1.JRS.8.083656","article-title":"Resolution enhancement of passive microwave images from geostationary Earth orbit via a projective sphere coordinate system","volume":"8","author":"Liu","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TGRS.2013.2255614","article-title":"On the spatial resolution enhancement of microwave radiometer data in Banach spaces","volume":"52","author":"Lenti","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, W., Zhang, W., Chen, S., Lv, X., An, D., and Ligthart, L. (2018). A deconvolution technology of microwave radiometer data using convolutional neural networks. Remote Sens., 10.","DOI":"10.3390\/rs10020275"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, T., Zhang, F., Li, W., Hu, W., and Tao, R. (2019). Microwave Radiometer Data Superresolution Using Image Degradation and Residual Network. IEEE Trans. Geosci. Remote Sens., 1\u201314.","DOI":"10.1109\/TGRS.2019.2923886"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, K., Zuo, W., Gu, S., and Zhang, L. (2017, January 21\u201326). Learning deep CNN denoiser prior for image restoration. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Jiang, L., Wu, S., Hao, S., Wang, G., and Yang, J. (2018). Assessment of methods for passive microwave snow cover mapping using FY-3C\/MWRI data in China. Remote Sens., 10.","DOI":"10.3390\/rs10040524"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1175\/2010JAMC2271.1","article-title":"Special Sensor Microwave Imager (SSM\/I) intersensor calibration using a simultaneous conical overpass technique","volume":"50","author":"Yang","year":"2011","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, S., and Chen, J. (2016, January 10\u201315). Instrument performance and cross calibration of FY-3C MWRI. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729095"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1109\/TGRS.2007.909597","article-title":"Stokes antenna temperatures","volume":"46","author":"Piepmeier","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"1","article-title":"Resolution enhancement for microwave-based atmospheric sounding from geostationary orbits","volume":"43","author":"Dietrich","year":"2008","journal-title":"Radio Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gong, R., Li, W., Chen, Y., and Van Gool, L. (2019, January 16\u201320). DLOW: Domain flow for adaptation and generalization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00258"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, J., Dong, C., and Qiao, Y. (2019, January 16\u201320). Modulating image restoration with continual levels via adaptive feature modification layers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01131"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Efrat, N., Glasner, D., Apartsin, A., Nadler, B., and Levin, A. (2013, January 1\u20138). Accurate blur models vs. image priors in single image super-resolution. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.352"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Lee, K.M. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"636","DOI":"10.1109\/83.841940","article-title":"Image quality assessment based on a degradation model","volume":"9","author":"Kite","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/TCI.2016.2644865","article-title":"Loss functions for image restoration with neural networks","volume":"3","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2432\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:54Z","timestamp":1760189274000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/20\/2432"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,19]]},"references-count":35,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11202432"],"URL":"https:\/\/doi.org\/10.3390\/rs11202432","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,10,19]]}}}