{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:37:12Z","timestamp":1776400632020,"version":"3.51.2"},"reference-count":66,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Shanghai Aerospace Science and Technology Innovation Fund","award":["SAST2019-048"],"award-info":[{"award-number":["SAST2019-048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection is an important task in identifying land cover change in different periods. In synthetic aperture radar (SAR) images, the inherent speckle noise leads to false changed points, and this affects the performance of change detection. To improve the accuracy of change detection, a novel automatic SAR image change detection algorithm based on saliency detection and convolutional-wavelet neural networks is proposed. The log-ratio operator is adopted to generate the difference image, and the speckle reducing anisotropic diffusion is used to enhance the original multitemporal SAR images and the difference image. To reduce the influence of speckle noise, the salient area that probably belongs to the changed object is obtained from the difference image. The saliency analysis step can remove small noise regions by thresholding the saliency map, and interest regions can be preserved. Then an enhanced difference image is generated by combing the binarized saliency map and two input images. A hierarchical fuzzy c-means model is applied to the enhanced difference image to classify pixels into the changed, unchanged, and intermediate regions. The convolutional-wavelet neural networks are used to generate the final change map. Experimental results on five SAR data sets indicated the proposed approach provided good performance in change detection compared to state-of-the-art relative techniques, and the values of the metrics computed by the proposed method caused significant improvement.<\/jats:p>","DOI":"10.3390\/rs13183697","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"3697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Change Detection from SAR Images Based on Convolutional Neural Networks Guided by Saliency Enhancement"],"prefix":"10.3390","volume":"13","author":[{"given":"Liangliang","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-4024","authenticated-orcid":false,"given":"Hongbing","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhong","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2686","DOI":"10.1080\/01431161.2020.1862437","article-title":"Building change detection in very high-resolution remote sensing image based on pseudo-orthorectification","volume":"42","author":"Chen","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2246","DOI":"10.1080\/2150704X.2020.1805134","article-title":"Building change detection from multi-source remote sensing images based on multi-feature fusion and extreme learning machine","volume":"42","author":"Wang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"016506","DOI":"10.1117\/1.JRS.15.016506","article-title":"Fast change detection method for remote sensing image based on method of connected area labeling and spectral clustering algorithm","volume":"15","author":"Huo","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4673","DOI":"10.1109\/ACCESS.2020.3047915","article-title":"Change detection method of high resolution remote sensing image based on DS evidence theory feature fusion","volume":"9","author":"Zhao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dong, H., Ma, W., Jiao, L., Liu, F., and Li, L. (2021). A multiscale self-attention deep clustering for change detection in SAR images. IEEE Trans. Geosci. Remote Sens., 1\u201316.","DOI":"10.1109\/TGRS.2021.3073562"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jung, J., and Yun, S.H. (2020). Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response: A case study for the 2018 Hokkaido Landslides. Remote Sens., 12.","DOI":"10.3390\/rs12020265"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8890","DOI":"10.1109\/TGRS.2019.2923643","article-title":"Unsupervised change detection based on a unified framework for weighted collaborative repre-sentation with RDDL and fuzzy clustering","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TIP.2009.2029593","article-title":"Iterative weighted maximum likelihood denoising with probabilistic patch-based weights","volume":"18","author":"Deledalle","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1109\/TIP.2002.804276","article-title":"Speckle reducing anisotropic diffusion","volume":"11","author":"Yu","year":"2002","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1109\/JSTARS.2019.2907655","article-title":"Hybrid SAR speckle reduction using complex wavelet shrinkage and non-local PCA-based filtering","volume":"12","author":"Farhadiani","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s12524-016-0607-0","article-title":"A novel approach of despeckling SAR images using nonlocal means filtering","volume":"45","author":"Devapal","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1109\/TGRS.2020.2999634","article-title":"Hyperspectral Image restoration using adaptive anisotropy total variation and nuclear norms","volume":"59","author":"Hu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/TGRS.2011.2161586","article-title":"A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage","volume":"50","author":"Parrilli","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Shi, J., Yang, X., Wang, C., Kumar, D., Wei, S., and Zhang, X. (2019). Deep multi-scale recurrent network for synthetic aperture radar images despeckling. Remote Sens., 11.","DOI":"10.3390\/rs11212462"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2277512","article-title":"A tutorial on speckle reduction in synthetic aperture radar images","volume":"1","author":"Argenti","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_16","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":"Zheng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7673","DOI":"10.1080\/01431161.2014.975378","article-title":"Change detection in synthetic aperture radar images based on non-local means with ratio similarity measurement","volume":"35","author":"Su","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lou, X., Jia, Z., Yang, J., and Kasabov, N. (2019). Change detection in SAR images based on the ROF model semi-implicit denoising method. Sensors, 19.","DOI":"10.3390\/s19051179"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/2150704X.2016.1258125","article-title":"Change detection in SAR images based on the logarithmic transformation and total variation de-noising method","volume":"8","author":"Wang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43970","DOI":"10.1109\/ACCESS.2019.2908282","article-title":"SAR image change detection based on mathematical morphology and the k-means clustering algorithm","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, C., Ji, Y., Chen, J., Deng, Y., Chen, J., and Jie, Y. (2020). Combining segmentation network and nonsubsampled contourlet transform for automatic marine raft aquaculture area extraction from sentinel-1 images. Remote Sens., 12.","DOI":"10.3390\/rs12244182"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12389","DOI":"10.1007\/s11042-020-10462-y","article-title":"A novel multiscale transform decomposition based multi-focus image fusion framework","volume":"80","author":"Li","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, L., and Ma, H. (2021). Pulse coupled neural network-based multimodal medical image fusion via guided filtering and WSEML in NSCT domain. Entropy, 23.","DOI":"10.3390\/e23050591"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s11042-020-09745-1","article-title":"Improved partial differential equation-based total variation approach to non-subsampled con-tourlet transform for medical image denoising","volume":"80","author":"Kollem","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"24303","DOI":"10.1007\/s11042-020-09154-4","article-title":"A novel approach for multi-focus image fusion based on SF-PAPCNN and ISML in NSST domain","volume":"79","author":"Li","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, L., and Ma, H. (2021). Saliency-Guided nonsubsampled shearlet transform for multisource remote sensing image fusion. Sensors, 21.","DOI":"10.3390\/s21051756"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, S., and Zhang, H. (2020). Multibaseline interferometric phase denoising based on kurtosis in the NSST domain. Sensors, 20.","DOI":"10.3390\/s20020551"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhang, Y., Jia, Z., Yang, J., and Kasabov, N. (2017). Remote sensing image change detection based on nsct-hmt model and its application. Sensors, 17.","DOI":"10.3390\/s17061295"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1109\/LGRS.2011.2182632","article-title":"Multitemporal image change detection using a detail-enhancing approach with nonsubsampled con-tourlet transform","volume":"9","author":"Li","year":"2012","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1080\/22797254.2018.1491804","article-title":"SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain","volume":"51","author":"Zhou","year":"2018","journal-title":"Eur. J. Remote. Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"016517","DOI":"10.1117\/1.JRS.14.016517","article-title":"Synthetic aperture radar image change detection based on Kalman filter and nonlocal means filter in the nonsubsampled shearlet transform domain","volume":"14","author":"Shen","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_32","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 com-pressed projection","volume":"7","author":"Hou","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4701","DOI":"10.1109\/JSTARS.2018.2866540","article-title":"SAR image change detection using saliency extraction and shearlet transform","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TIP.2011.2170702","article-title":"Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering","volume":"21","author":"Gong","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"7365","DOI":"10.1109\/TGRS.2019.2913095","article-title":"Saliency-Guided deep neural networks for SAR image change detection","volume":"57","author":"Geng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.patcog.2016.07.040","article-title":"Unsupervised saliency-guided SAR image change detection","volume":"61","author":"Zheng","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_38","first-page":"1061548","article-title":"Change detection for synthetic aperture radar images based on pattern and intensity distinctiveness analysis","volume":"Volume 10615","author":"Wang","year":"2018","journal-title":"Ninth International Conference on Graphic and Image Processing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9606","DOI":"10.1080\/01431161.2020.1826066","article-title":"A saliency-guided neighbourhood ratio model for automatic change detection of SAR images","volume":"41","author":"Majidi","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/LGRS.2018.2876616","article-title":"SAR image change detection using PCANet guided by saliency detection","volume":"16","author":"Li","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.1109\/TGRS.2006.876288","article-title":"Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude im-agery","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1109\/TGRS.2010.2066979","article-title":"Multitemporal image change detection using undecimated discrete wavelet transform and active contours","volume":"49","author":"Celik","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"4276","DOI":"10.1080\/01431161.2016.1210838","article-title":"Superpixel-based active contour model for unsupervised change detection from satellite images","volume":"37","author":"Hao","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/LGRS.2016.2550666","article-title":"SAR Image change detection based on multiple kernel k-means clustering with local-neighborhood information","volume":"13","author":"Jia","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"046017","DOI":"10.1117\/1.JRS.10.046017","article-title":"Synthetic aperture radar image change detection based on improved bilateral filtering and fuzzy C mean","volume":"10","author":"Shang","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2020.03.002","article-title":"A method to improve the accuracy of SAR image change detection by using an image enhancement method","volume":"163","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, X., Liu, G., Zhang, C., Atkinson, P.M., Tan, X., Jian, X., Zhou, X., and Li, Y. (2020). Two-Phase object-based deep learning for multi-temporal SAR image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12030548"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4517","DOI":"10.1109\/JSTARS.2019.2953128","article-title":"Change detection from synthetic aperture radar images based on channel weighting-based deep cascade network","volume":"12","author":"Gao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5017","DOI":"10.1109\/TIP.2015.2475625","article-title":"PCANet: A simple deep learning baseline for image classification?","volume":"24","author":"Chan","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LGRS.2016.2611001","article-title":"Automatic change detection in synthetic aperture radar images based on PCANet","volume":"13","author":"Gao","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.044004","article-title":"Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network","volume":"11","author":"Liu","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","article-title":"SAR Image segmentation based on convolutional-wavelet neural network and markov random field","volume":"64","author":"Duan","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/LGRS.2019.2895656","article-title":"Sea ice change detection in sar images based on convolutional-wavelet neural networks","volume":"16","author":"Gao","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.infrared.2017.02.005","article-title":"Infrared and visible image fusion based on visual saliency map and weighted least square optimi-zation","volume":"82","author":"Ma","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"046019","DOI":"10.1117\/1.JRS.10.046019","article-title":"Change detection from synthetic aperture radar images based on neighborhood-based ratio and ex-treme learning machine","volume":"10","author":"Gao","year":"2016","journal-title":"J. Appl. Remote. Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TIP.2020.3034027","article-title":"Patch-Based dual-tree complex wavelet transform for kinship recognition","volume":"30","author":"Goyal","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1109\/LGRS.2009.2025059","article-title":"Unsupervised change detection in satellite images using principal component analysis and k-means clustering","volume":"6","author":"Celik","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_58","first-page":"412","article-title":"Change detection in remotely sensed images based on modified log ratio and fuzzy clustering","volume":"84","author":"Sharma","year":"2017","journal-title":"Blockchain Technol. Innov. Bus. Process."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1109\/LGRS.2015.2484220","article-title":"Gabor Feature based unsupervised change detection of multitemporal SAR images based on two-level clustering","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1109\/LGRS.2016.2606119","article-title":"Logarithmic mean-based thresholding for SAR image change detection","volume":"13","author":"Sumaiya","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Gao, Y., Gao, F., Dong, J., and Wang, S. (2018, January 22\u201327). Sea ice change detection in SAR images based on collaborative representation. Proceedings of the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519461"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"He, Y., Jia, Z., Yang, J., and Kasabov, N. (2021). Multispectral image change detection based on single-band slow feature analysis. Remote Sens., 13.","DOI":"10.3390\/rs13152969"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Yang, L., Chen, Y., Song, S., Li, F., and Huang, G. (2021). Deep Siamese networks based change detection with remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13173394"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shao, P., Shi, W., Liu, Z., and Dong, T. (2021). Unsupervised change detection using fuzzy topology-based majority voting. Remote Sens., 13.","DOI":"10.3390\/rs13163171"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","unstructured":"Wu, T., Luo, J., Zhou, Y., Wang, C., Xi, J., and Fang, J. (2020). Geo-object-based land cover map update for high-spatial-resolution remote sensing images via change detection and label transfer. Remote Sens., 12.","DOI":"10.3390\/rs12010174"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3697\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:27Z","timestamp":1760166027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3697"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,16]]},"references-count":66,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183697"],"URL":"https:\/\/doi.org\/10.3390\/rs13183697","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,16]]}}}