{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T14:59:00Z","timestamp":1773413940292,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,5]],"date-time":"2023-02-05T00:00:00Z","timestamp":1675555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62271017"],"award-info":[{"award-number":["62271017"]}]},{"name":"National Natural Science Foundation of China","award":["62271018"],"award-info":[{"award-number":["62271018"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62271017"],"award-info":[{"award-number":["62271017"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62271018"],"award-info":[{"award-number":["62271018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Single image super-resolution (SISR) is to reconstruct a high-resolution (HR) image from a corresponding low-resolution (LR) input. It is an effective way to solve the problem that infrared remote sensing images are usually suffering low resolution due to hardware limitations. Most previous learning-based SISR methods just use synthetic HR-LR image pairs (obtained by bicubic kernels) to learn the mapping from LR images to HR images. However, the underlying degradation in the real world is often different from the synthetic method, i.e., the real LR images are obtained through a more complex degradation kernel, which leads to the adaptation problem and poor SR performance. To handle this problem, we propose a novel closed-loop framework that can not only make full use of the learning ability of the channel attention module but also introduce the information of real images as much as possible through a closed-loop structure. Our network includes two independent generative networks for down-sampling and super-resolution, respectively, and they are connected to each other to get more information from real images. We make a comprehensive analysis of the training data, resolution level and imaging spectrum to validate the performance of our network for infrared remote sensing image super-resolution. Experiments on real infrared remote sensing images show that our method achieves superior performance in various training strategies of supervised learning, weakly supervised learning and unsupervised learning. Especially, our peak signal-to-noise ratio (PSNR) is 0.9 dB better than the second-best unsupervised super-resolution model on PROBA-V dataset.<\/jats:p>","DOI":"10.3390\/rs15040882","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T05:29:05Z","timestamp":1675661345000},"page":"882","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Closed-Loop Network for Single Infrared Remote Sensing Image Super-Resolution in Real World"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1981-8307","authenticated-orcid":false,"given":"Haopeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 102206, China"}]},{"given":"Cong","family":"Zhang","sequence":"additional","affiliation":[{"name":"AVIC DIGITAL, Beijing 100028, China"}]},{"given":"Fengying","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 102206, China"},{"name":"Beijing Key Laboratory of Digital Media, Beijing 102206, China"},{"name":"Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 102206, China"}]},{"given":"Zhiguo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Aerospace Information Engineering, School of Astronautics, Beihang University, Beijing 102206, China"},{"name":"Beijing Key Laboratory of Digital Media, Beijing 102206, China"},{"name":"Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beijing 102206, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1080\/01431161.2016.1264027","article-title":"Single-frame super-resolution in remote sensing: A practical overview","volume":"38","author":"Fernandezbeltran","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/JSTARS.2017.2773367","article-title":"Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images","volume":"11","author":"Pham","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1109\/LGRS.2017.2727515","article-title":"Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/LGRS.2017.2773118","article-title":"Unsupervised Object-Based Change Detection via a Weibull Mixture Model-Based Binarization for High-Resolution Remote Sensing Images","volume":"15","author":"Wu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","unstructured":"Bai, Y., Zhang, Y., Ding, M., and Ghanem, B. (2018). Proceedings of the European Conference on Computer Vision, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/36.917895","article-title":"Super-resolution target identification from remotely sensed images using a Hopfield neural network","volume":"39","author":"Tatem","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dai, D., Wang, Y., Chen, Y., and Van Gool, L. (2016, January 7\u201310). Is image super-resolution helpful for other vision tasks?. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477613"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zanotta, D.C., Ferreira, M.P., Zorte, M., and Shimabukuro, Y. (2014, January 13\u201318). A statistical approach for simultaneous segmentation and classification. Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947593"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.1109\/TGRS.2017.2778191","article-title":"Adaptive Super-Resolution for Remote Sensing Images Based on Sparse Representation with Global Joint Dictionary Model","volume":"56","author":"Hou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liao, R., Tao, X., Li, R., Ma, Z., and Jia, J. (2015, January 7\u201313). Video Super-Resolution via Deep Draft-Ensemble Learning. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.68"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, J., Liang, Y., Liu, J., and Huang, Z. (2017). Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images. Sensors, 17.","DOI":"10.3390\/s17092142"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, C., Ma, C., and Yang, M. (2014, January 6\u201312). Single-Image Super-Resolution: A Benchmark. Proceedings of the Europeon Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_25"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, D., Li, Z., Xia, Y., and Chen, Z. (2015, January 21\u201324). Remote sensing image super-resolution: Challenges and approaches. Proceedings of the IEEE International Conference on Digital Signal Processing, Singapore.","DOI":"10.1109\/ICDSP.2015.7251858"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Laplante, P.A. (2018). Encyclopedia of Image Processing, CRC Press.","DOI":"10.1201\/9781351032742"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1364\/JOSA.54.000931","article-title":"Diffraction and Resolving Power","volume":"54","author":"Harris","year":"1964","journal-title":"J. Opt. Soc. Am."},{"key":"ref_17","unstructured":"Glassner, A.S. (1990). Graphics Gems, Morgan Kaufmann."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1007\/s00138-014-0623-4","article-title":"Super-resolution: A comprehensive survey","volume":"25","author":"Nasrollahi","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/1049-9652(91)90045-L","article-title":"Improving resolution by image registration","volume":"53","author":"Irani","year":"1991","journal-title":"Graph. Model. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1109\/83.503915","article-title":"Extraction of high-resolution frames from video sequences","volume":"5","author":"Schultz","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1364\/JOSAA.6.001715","article-title":"High-resolution image recovery from image-plane arrays, using convex projections","volume":"6","author":"Stark","year":"1989","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_23","unstructured":"Yang, J., Wright, J., Huang, T.S., and Ma, Y. (2008, January 23\u201328). Image super-resolution as sparse representation of raw image patches. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA."},{"key":"ref_24","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010, January 24\u201330). On single image scale-up using sparse-representations. Proceedings of the Curves and Surfaces, Avignon, France."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (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_27","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., 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, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/JSTARS.2015.2417864","article-title":"Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data","volume":"8","author":"Syrris","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Timofte, R., De, V., and Gool, L.V. (2013, January 1\u20138). Anchored Neighborhood Regression for Fast Example-Based Super-Resolution. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.241"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014, January 6\u201312). Learning a Deep Convolutional Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TPAMI.2014.2321404","article-title":"A Bayesian Nonparametric Approach to Image Super-Resolution","volume":"37","author":"Polatkan","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","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, Australia.","DOI":"10.1109\/ICCV.2013.352"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Michaeli, T., and Irani, M. (2014, January 6\u201312). Blind Deblurring Using Internal Patch Recurrence. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10578-9_51"},{"key":"ref_34","unstructured":"Glasner, D., Bagon, S., and Irani, M. (October, January 29). Super-resolution from a single image. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, J., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single image super-resolution from transformed self-exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shocher, A., Cohen, N., and Irani, M. (2018, January 18\u201323). \u201cZero-Shot\u201d Super-Resolution Using Deep Internal Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00329"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1109\/TGRS.2018.2843525","article-title":"A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1109\/JSTARS.2020.2984589","article-title":"An Efficient Deep Unsupervised Superresolution Model for Remote Sensing Images","volume":"13","author":"Sheikholeslami","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liu, S., Zhang, J., Zhang, Y., Dong, C., and Lin, L. (2018, January 18\u201322). Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"ref_41","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First, Springer International Publishing."},{"key":"ref_42","unstructured":"Wang, P., Zhang, H., Zhou, F., and Jiang, Z. (August, January 28). Unsupervised Remote Sensing Image Super-Resolution Using Cycle CNN. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4250","DOI":"10.1109\/TGRS.2020.3009224","article-title":"Nonpairwise-Trained Cycle Convolutional Neural Network for Single Remote Sensing Image Super-Resolution","volume":"59","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., and Tan, M. (2020, January 13\u201319). Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"ref_45","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image Super-Resolution Using Very Deep Residual Channel Attention Networks. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s42064-019-0059-8","article-title":"Super-resolution of PROBA-V images using convolutional neural networks","volume":"3","author":"Izzo","year":"2019","journal-title":"Astrodynamics"},{"key":"ref_50","unstructured":"(2019, November 20). Kelvins-PROBA-V Super Resolution\u2014Data. Available online: https:\/\/kelvins.esa.int\/proba-v-super-resolution\/data\/."},{"key":"ref_51","unstructured":"(2019, November 20). Earth Explorer, Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., He, X., Jing, M., Fan, Y., and Zeng, X. (November, January 29). Enhanced Recursive Residual Network for Single Image Super-Resolution. Proceedings of the 2019 IEEE 13th International Conference on ASIC, Chongqing, China.","DOI":"10.1109\/ASICON47005.2019.8983465"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., and Wu, W. (2019, January 15\u201320). Feedback Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00399"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201323). Residual Dense Network for Image Super-Resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3683","DOI":"10.1109\/TIP.2016.2567075","article-title":"Fast Single Image Super-Resolution Using a New Analytical Solution for \u21132 \u2013 \u21132 Problems","volume":"25","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"He, H., and Siu, W. (2011, January 20\u201325). Single image super-resolution using Gaussian process regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CL, USA.","DOI":"10.1109\/CVPR.2011.5995713"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Soh, J.W., Cho, S., and Cho, N.I. (2020, January 13\u201319). Meta-Transfer Learning for Zero-Shot Super-Resolution. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00357"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, Z.S., Siu, W.C., Wang, L.W., Li, C.T., Cani, M.P., and Chan, Y.L. (2020, January 14\u201319). Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00229"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, Y., Dong, X., Xu, Q., Yang, J., An, W., and Guo, Y. (2021, January 19\u201325). Unsupervised Degradation Representation Learning for Blind Super-Resolution. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01044"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/882\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:24:59Z","timestamp":1760120699000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/882"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,5]]},"references-count":60,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040882"],"URL":"https:\/\/doi.org\/10.3390\/rs15040882","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,5]]}}}