{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:52:08Z","timestamp":1760147528906,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"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":["62271218"],"award-info":[{"award-number":["62271218"]}],"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>In order to improve the spatial resolution of a one-dimensional aperture synthesis (1-D AS) radiometer without increasing the size of the antenna array, the method of visibility extension (VE) is proposed in this article. In the VE method, prior information about the visibility distribution of various scenes is learnt by a residual convolutional neural network (ResCNN). Specifically, the relationship between the distribution of low-frequency visibility and that of high-frequency visibility is learnt. Then, the ResCNN is used to estimate the high-frequency visibility samples from the low-frequency visibility samples obtained by the AS system. Furthermore, the low- and high-frequency visibility samples are combined to reconstruct the brightness temperature image of the scene, to enhance the spatial resolution of AS. The simulation and experiment both demonstrate that the VE method can enhance the spatial resolution of 1-D AS.<\/jats:p>","DOI":"10.3390\/rs15040941","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T02:55:54Z","timestamp":1675911354000},"page":"941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Visibility Extension of 1-D Aperture Synthesis by a Residual CNN for Spatial Resolution Enhancement"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5083-6357","authenticated-orcid":false,"given":"Guanghui","family":"Zhao","sequence":"first","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Qingxia","family":"Li","sequence":"additional","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4069-9680","authenticated-orcid":false,"given":"Zhiwei","family":"Chen","sequence":"additional","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Zhenyu","family":"Lei","sequence":"additional","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6229-2852","authenticated-orcid":false,"given":"Chengwang","family":"Xiao","sequence":"additional","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Yuhang","family":"Huang","sequence":"additional","affiliation":[{"name":"Science and Technology on Multi-Spectral Information Processing Laboratory, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"420","DOI":"10.3389\/fmars.2019.00420","article-title":"Observational Needs of Sea Surface Temperature","volume":"6","author":"Armstrong","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1007\/s10872-005-5782-5","article-title":"Merging satellite infrared and microwave SSTs: Methodology and evaluation of the new SST","volume":"60","author":"Guan","year":"2004","journal-title":"J. Oceanogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1109\/36.7685","article-title":"Interferometric synthetic aperture microwave radiometry for the remote sensing of the Earth","volume":"26","author":"Ruf","year":"1988","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1109\/36.508417","article-title":"On-board phase and modulus calibration of large aperture synthesis radiometers: Study applied to MIRAS","volume":"34","author":"Torres","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1109\/TGRS.2007.914809","article-title":"SMOS: The Payload","volume":"46","author":"McMullan","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10376","DOI":"10.1109\/TGRS.2019.2934154","article-title":"Microwave SAIR Imaging Approach Based on Deep Convolutional Neural Network","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3157870","article-title":"Image Reconstruction with Deep CNN for Mirrored Aperture Synthesis","volume":"60","author":"Xiao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xiao, C., Wang, X., Dou, H., Li, H., Lv, R., Wu, Y., Song, G., Wang, W., and Zhai, R. (2022). Non-Uniform Synthetic Aperture Radiometer Image Reconstruction Based on Deep Convolutional Neural Network. Remote Sens., 14.","DOI":"10.3390\/rs14102359"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014, January 14\u201318). Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. Proceedings of the Interspeech 2014: 15th Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-80"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/72.963769","article-title":"LSTM recurrent networks learn simple context free and context sensitive languages","volume":"12","author":"Gers","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2130001","DOI":"10.1142\/S0129065721300011","article-title":"An Experimental Review on Deep Learning Architectures for Time Series Forecasting","volume":"31","author":"Riquelme","year":"2021","journal-title":"Int. J. Neural Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.neunet.2005.01.008","article-title":"A novel information geometric approach to variable selection in MLP networks","volume":"18","author":"Eleuteri","year":"2005","journal-title":"Neural Networks"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.cogsys.2019.06.003","article-title":"Hybrid particle swarm optimization-genetic algorithm trained multi-layer perceptron for classification of human glioma from molecular brain neoplasia data","volume":"58","author":"Bhattacharjee","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100980","DOI":"10.1016\/j.aei.2019.100980","article-title":"Computer vision for behaviour-based safety in construction: A review and future directions","volume":"43","author":"Fang","year":"2019","journal-title":"Adv. Eng. Informatics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.specom.2019.01.004","article-title":"End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition","volume":"108","author":"Palaz","year":"2019","journal-title":"Speech Commun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, H.-C., Deng, Z.-Y., and Chiang, H.-H. (2020). Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization. Sensors, 20.","DOI":"10.3390\/s20216114"},{"key":"ref_19","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","first-page":"1","article-title":"Instrument Design and Early In-Orbit Performance of HY-2B Scanning Microwave Radiometer","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"Hyperband: A novel bandit-based approach to hyperparameter optimization","volume":"18","author":"Li","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_22","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_23","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"37","author":"Ioffe","year":"2015","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_24","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_25","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1002\/ima.22337","article-title":"Teeth category classifcation via seven-layer deep convolutional neural network with max pooling and global average pooling","volume":"29","author":"Li","year":"2019","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8105","DOI":"10.1109\/TGRS.2019.2918308","article-title":"Initial Results of Microwave Radiometric Imaging with Mirrored Aperture Synthesis","volume":"57","author":"Dou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Q., Dou, H., Gui, L., Chen, L., Chen, K., Wu, Y., Lei, Z., Li, Y., Lang, L., and Guo, W. (2018, January 22\u201327). MAS-V: Experimental System of Mirrored Aperture Synthesis at V BAND. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518080"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/941\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:28:42Z","timestamp":1760120922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/941"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":29,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040941"],"URL":"https:\/\/doi.org\/10.3390\/rs15040941","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,8]]}}}