{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:08:32Z","timestamp":1760231312757,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,5]],"date-time":"2022-09-05T00:00:00Z","timestamp":1662336000000},"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":["42001364"],"award-info":[{"award-number":["42001364"]}],"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>Prompt and precise acknowledgement of surface change around subways is of considerable significance in urban rail protection and local environmental management. Research has proven the considerable potential of synthetic aperture radar (SAR) images for detecting such information; however, previous studies have mostly focused on change intensity using single Difference images (DIs), e.g., difference value DI (DVDI) and mean value DI (MVDI). With the aim of more accurate information with respect to surface changes around subways, in this study, we proposed a novel SAR detection method that involved three steps: (1) the calculation of three single DIs, (2) the combination of the single DIs and (3) the delineation of the changed area. Compared to existing detection methods, the proposed method represents three major improvements. First, both the intensity information and phase information were applied by combining the DVDI, MVDI and coherent difference images (CDIs). Secondly, a local energy weight (LEW) approach was proposed to combine single DIs instead of the normally used equal weights. Because the changed area often comprises continuous rather than discrete pixels, a combined DI with the LEW (\u201cCoDI-LEW\u201d hereafter) fully considers the attributes of adjacent pixels and enhances the signal-to-noise ratio of SAR images. Thirdly, the FCM algorithm, instead of the widely used threshold methods, was applied to distinguish changed areas from unchanged areas. An experimental comparison with several existing detection methods showed that the proposed method could delineate changed areas with higher accuracy in terms of both quality and quantity. Furthermore, it can effectively execute detection under diverse surface change conditions with good feasibility and applicability.<\/jats:p>","DOI":"10.3390\/rs14174419","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4419","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Unsupervised Change Detection around Subways Based on SAR Combined Difference Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Aihui","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"given":"Jie","family":"Dai","sequence":"additional","affiliation":[{"name":"Shandong Rail Transit Survey and Design Company Limited, Jinan 250101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8054-8951","authenticated-orcid":false,"given":"Sisi","family":"Yu","sequence":"additional","affiliation":[{"name":"Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China"},{"name":"Sino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, China"}]},{"given":"Baolei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1576-6610","authenticated-orcid":false,"given":"Qiaoyun","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Life Science, Faculty of Science, University of Technology Sydney, Sydney, NSW 2007, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9579-8849","authenticated-orcid":false,"given":"Huanxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Environment, Shandong Normal University, Jinan 250358, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102988","DOI":"10.1016\/j.cities.2020.102988","article-title":"Determinants of transit-oriented development efficiency focusing on an integrated subway, bus and shared-bicycle system: Application of Simar-Wilson's two-stage approach","volume":"108","author":"Tamakloe","year":"2021","journal-title":"Cities"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, H., Feng, G., Xu, B., Yu, Y., Li, Z., Du, Y., and Zhu, J. (2017). Deriving spatio-temporal development of ground subsidence due to subway construction and operation in delta regions with PS-InSAR data: A case study in Guangzhou, China. Remote Sens., 9.","DOI":"10.3390\/rs9101004"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.jtrangeo.2015.08.002","article-title":"Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul","volume":"48","author":"Jun","year":"2015","journal-title":"J. Transp. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.landusepol.2004.11.003","article-title":"A TOD planning model to review the regulation of allowable development densities around subway stations","volume":"23","author":"Lin","year":"2006","journal-title":"Land Use Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2256","DOI":"10.1109\/TGRS.2020.3004353","article-title":"Super-Resolution Mapping Based on Spatial\u2013Spectral Correlation for Spectral Imagery","volume":"59","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6805510","DOI":"10.1109\/JPHOT.2016.2625801","article-title":"Adaptive Target Profile Acquiring Method for Photon Counting 3-D Imaging Lidar","volume":"8","author":"Ye","year":"2016","journal-title":"IEEE Photonics J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/2150704X.2016.1233371","article-title":"Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: A case study in northern Ontario","volume":"8","author":"Ahmed","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1080\/10106049.2019.1622600","article-title":"Urban expansion in the megacity since 1970s: A case study in Mumbai","volume":"36","author":"Yu","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shi, L., Leichtle, T., Wurm, M., and Taubenb\u00f6ck, H. (2022). The \u201cghost neighborhood\u201d phenomenon in China\u2014geographic locations and intra-urban spatial patterns. Environ. Plan. B Urban Anal. City Sci.","DOI":"10.1177\/23998083221092775"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5296\/emsd.v8i1.13917","article-title":"Urban green space degradation: An experience of Kuala Lumpur City","volume":"8","author":"Kasim","year":"2018","journal-title":"Environ. Manag. Sustain. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s41324-018-0205-z","article-title":"Airspace map design to implement customer-friendly service on unmanned aerial vehicles","volume":"27","author":"Kwon","year":"2018","journal-title":"Spat. Inf. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11803-022-2074-7","article-title":"A review of the research and application of deep learning-based computer vision in structural damage detection","volume":"21","author":"Lingxin","year":"2022","journal-title":"Earthq. Eng. Eng. Vib."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1007\/s12145-020-00498-x","article-title":"Automatic extraction of urban land information from unmanned aerial vehicle (UAV) data","volume":"13","author":"Shukla","year":"2020","journal-title":"Earth Sci. Inform."},{"key":"ref_14","first-page":"178","article-title":"Integration of optical and SAR remote sensing images for crop-type mapping based on a novel object-oriented feature selection method","volume":"13","author":"Cui","year":"2020","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-1569-2020","article-title":"High-Resolution Sar Coherent Change Detection in Urban Environment","volume":"43","author":"Manzoni","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"122","DOI":"10.3390\/rs1030122","article-title":"Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications","volume":"1","author":"Alberga","year":"2009","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2013.03.006","article-title":"Change detection from remotely sensed images: From pixel-based to object-based approaches","volume":"80","author":"Hussain","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/LGRS.2012.2191387","article-title":"Wavelet fusion on ratio images for change detection in SAR images","volume":"9","author":"Ma","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bergamasco, L., Saha, S., Bovolo, F., and Bruzzone, L. (2019). Unsupervised change-detection based on convolutional-autoencoder feature extraction. Image and Signal Processing for Remote Sensing XXV, Proceedings of the SPIE Remote Sensing, Strasbourg, France, 9\u201312 September 2019, SPIE.","DOI":"10.1117\/12.2533812"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A robust fuzzy local information C-means clustering algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","first-page":"1","article-title":"Dynamic graph-level neural network for SAR image change detection","volume":"19","author":"Wang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2021.07.007","article-title":"A deep translation (GAN) based change detection network for optical and SAR remote sensing images","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/TNNLS.2015.2435783","article-title":"Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks","volume":"27","author":"Gong","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, T., Li, Y., and Xu, L. (2016, January 17\u201320). Dual-channel convolutional neural network for change detection of multitemporal SAR images. Proceedings of the 2016 International Conference on Orange Technologies (ICOT), Melbourne, Australia.","DOI":"10.1109\/ICOT.2016.8278979"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4506205","DOI":"10.1109\/LGRS.2022.3161931","article-title":"Large-Difference-Scale Target Detection Using a Revised Bhattacharyya Distance in SAR Images","volume":"19","author":"Tang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","first-page":"4506005","article-title":"Transferable SAR Image Classification Crossing Different Satellites under Open Set Condition","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1109\/LGRS.2020.2985340","article-title":"Semisupervised change detection using graph convolutional network","volume":"18","author":"Saha","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qi, X., Liao, R., Jia, J., Fidler, S., and Urtasun, R. (2017, January 22\u201329). 3d graph neural networks for rgbd semantic segmentation. Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.556"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wanab, L., Maab, L., Guoad, J., Liuac, M., and Ab, D.Y. (2021, January 11\u201316). Slow Feature Analysis Based on Convolutional Neural Network for SAR Image Change Detection. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553912"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Muhammad, U., Wang, W., Chattha, S.P., and Ali, S. (2018, January 20\u201324). Pre-trained VGGNet architecture for remote-sensing image scene classification. Proceedings of the 24th International Conference on Pattern Recognition (ICPR), Beijing, China.","DOI":"10.1109\/ICPR.2018.8545591"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chaib, S., Yao, H., Gu, Y., and Amrani, M. (2017, January 19\u201322). Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models. Proceedings of the Ninth International Conference on Digital Image Processing (ICDIP 2017), Hong Kong, China.","DOI":"10.1117\/12.2281755"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17489","DOI":"10.1007\/s11042-017-5314-5","article-title":"Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval","volume":"77","author":"Ge","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1109\/TGRS.2005.861007","article-title":"Unsupervised change detection on SAR images using fuzzy hidden Markov chains","volume":"44","author":"Carincotte","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s12524-017-0740-4","article-title":"Unsupervised Change Detection of SAR Images Based on an Improved NSST Algorithm","volume":"46","author":"Chen","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/22797254.2020.1852606","article-title":"Change detection in SAR images based on iterative Otsu","volume":"53","author":"Xu","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.rse.2016.09.009","article-title":"Monitoring activity at the Daguangbao mega-landslide (China) using Sentinel-1 TOPS time series interferometry","volume":"186","author":"Dai","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TGRS.2004.842441","article-title":"An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images","volume":"43","author":"Bazi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/LGRS.2011.2167211","article-title":"A Neighborhood-Based Ratio Approach for Change Detection in SAR Images","volume":"9","author":"Gong","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1109\/TSP.2018.2883011","article-title":"New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images","volume":"67","author":"Mian","year":"2019","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1007\/s12524-020-01275-5","article-title":"Aerial Close-Range Photogrammetry to Quantify Deformations of the Pile Retaining Walls","volume":"49","author":"Ekinci","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.1109\/JSTARS.2014.2328344","article-title":"Unsupervised Change Detection in SAR Image Based on Gauss-Log Ratio Image Fusion and Compressed Projection","volume":"7","author":"Hou","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"403095","DOI":"10.1155\/2014\/403095","article-title":"Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images","volume":"2014","author":"Ma","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection","volume":"11","author":"Yaoguo","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/LGRS.2020.2994163","article-title":"SAR Images Change Detection Based on Self-Adaptive Network Architecture","volume":"18","author":"Shi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shi, J., Liu, X., Yang, S., Lei, Y., and Tian, D. (2021). An initialization friendly Gaussian mixture model based multi-objective clustering method for SAR images change detection. J. Ambient Intell. Humaniz. Comput.","DOI":"10.1007\/s12652-020-02584-w"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/2150704X.2019.1594430","article-title":"A novel change detection method combined with registration for SAR images","volume":"10","author":"Song","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, K., Fu, X., Lv, X., and Yuan, J. (2021). Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR. Remote Sens., 13.","DOI":"10.3390\/rs13030471"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"026030","DOI":"10.1117\/1.JRS.10.026030","article-title":"Small-scale loess landslide monitoring with small baseline subsets interferometric synthetic aperture radar technique\u2014case study of Xingyuan landslide, Shaanxi, China","volume":"10","author":"Zhao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.5194\/isprs-archives-XLIII-B3-2020-1697-2020","article-title":"Sentinel-1 Based Flood Mapping Using Interferometric Coherence and Intensity Change Detection Approach","volume":"43","author":"Papila","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Malmgren-Hansen, D., Sohnesen, T., Fisker, P., and Baez, J. (2020). Sentinel-1 Change Detection Analysis for Cyclone Damage Assessment in Urban Environments. Remote Sens., 12.","DOI":"10.3390\/rs12152409"},{"key":"ref_52","first-page":"520","article-title":"Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles","volume":"57","author":"Freeman","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"721","DOI":"10.5721\/EuJRS20134643","article-title":"On the use of temporal series of L-and X-band SAR data for soil moisture retrieval. Capitanata plain case study","volume":"46","author":"Balenzano","year":"2013","journal-title":"Eur. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1109\/JSTARS.2010.2052916","article-title":"Dense temporal series of C-and L-band SAR data for soil moisture retrieval over agricultural crops","volume":"4","author":"Balenzano","year":"2010","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","first-page":"689","article-title":"Analysis of InSAR Coherence Loss Caused by Soil Moisture Variation","volume":"4","author":"Yin","year":"2015","journal-title":"J. Radars"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/42.996338","article-title":"A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data","volume":"21","author":"Ahmed","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.patcog.2006.07.011","article-title":"Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation","volume":"40","author":"Cai","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/TFUZZ.2013.2286993","article-title":"Accelerating Fuzzy-C Means Using an Estimated Subsample Size","volume":"22","author":"Parker","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1080\/2150704X.2014.912766","article-title":"A novel dynamic threshold method for unsupervised change detection from remotely sensed images","volume":"5","author":"He","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/S0034-4257(97)00112-0","article-title":"A comparison of four algorithms for change detection in an urban environment","volume":"63","author":"Ridd","year":"1998","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4419\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:23:53Z","timestamp":1760142233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4419"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,5]]},"references-count":61,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174419"],"URL":"https:\/\/doi.org\/10.3390\/rs14174419","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,9,5]]}}}