{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:58:37Z","timestamp":1768273117966,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T00:00:00Z","timestamp":1574726400000},"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":["41801323"],"award-info":[{"award-number":["41801323"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701511"],"award-info":[{"award-number":["41701511"]}],"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>This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan\u2013Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3390\/rs11232787","type":"journal-article","created":{"date-parts":[[2019,11,26]],"date-time":"2019-11-26T10:57:27Z","timestamp":1574765847000},"page":"2787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan\u2013Vese Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6127-4801","authenticated-orcid":false,"given":"Xiaokang","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Land Survey and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"},{"name":"Hubei Soil and Water Conservation Engineering Research Center, Hubei Water Resources Research Institute, Wuhan 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Land Survey and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019An University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4433","DOI":"10.1080\/01431160600675895","article-title":"Satellite radar and optical remote sensing for earthquake damage detection: Results from different case studies","volume":"27","author":"Stramondo","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1126\/science.1111772","article-title":"Global consequences of land use","volume":"309","author":"Foley","year":"2005","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Review ArticleDigital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2015.01.006","article-title":"A critical synthesis of remotely sensed optical image change detection techniques","volume":"160","author":"Tewkesbury","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9554","DOI":"10.1109\/TGRS.2019.2927659","article-title":"Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images","volume":"57","author":"Lv","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TGRS.2018.2886643","article-title":"Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images","volume":"57","author":"Saha","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.3390\/rs3081743","article-title":"Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems\u2014An Overview","volume":"3","author":"Blaschke","year":"2011","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2006.07.019","article-title":"An automated binary change detection model using a calibration approach","volume":"106","author":"Im","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1080\/0143116021000021189","article-title":"Post-classification change detection with data from different sensors: Some accuracy considerations","volume":"24","author":"Serra","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/36.843009","article-title":"Automatic analysis of the difference image for unsupervised change detection","volume":"38","author":"Bruzzone","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1080\/22797254.2017.1308236","article-title":"Level set incorporated with an improved MRF model for unsupervised change detection for satellite images","volume":"50","author":"Zhang","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lv, Z., Liu, T., Wan, Y., Benediktsson, J.A., and Zhang, X. (2018). Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10030472"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2017.2763182","article-title":"Object-Based Change Detection for VHR Images Based on Multiscale Uncertainty Analysis","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","first-page":"15","article-title":"Unsupervised change detection in VHR remote sensing imagery\u2014An object-based clustering approach in a dynamic urban environment","volume":"54","author":"Leichtle","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., Chen, Z., and Chen, D. (2016). Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sens., 8.","DOI":"10.3390\/rs8090761"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1080\/01431161.2016.1232871","article-title":"Object-oriented change detection method based on adaptive multi-method combination for remote-sensing images","volume":"37","author":"Cai","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1109\/LGRS.2014.2386878","article-title":"Object-Based Change Detection of Very High Resolution Satellite Imagery Using the Cross-Sharpening of Multitemporal Data","volume":"12","author":"Wang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shao, P., Shi, W., He, P., Hao, M., and Zhang, X. (2016). Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm. Remote Sens., 8.","DOI":"10.3390\/rs8030264"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.rse.2012.05.027","article-title":"Multitemporal change detection of urban trees using localized region-based active contours in VHR images","volume":"124","author":"Ardila","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6255","DOI":"10.1080\/01431161.2014.951740","article-title":"Automatic change detection in high-resolution remote-sensing images by means of level set evolution and support vector machine classification","volume":"35","author":"Cao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","article-title":"Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method","volume":"175","author":"Li","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1080\/2150704X.2017.1317929","article-title":"Level set evolution with local uncertainty constraints for unsupervised change detection","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/LGRS.2014.2352264","article-title":"A local statistical fuzzy active contour model for change detection","volume":"12","author":"Li","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1109\/JSTARS.2018.2803784","article-title":"Landslide Inventory Mapping From Bitemporal High-Resolution Remote Sensing Images Using Change Detection and Multiscale Segmentation","volume":"11","author":"Lv","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/01431160601075582","article-title":"Object-based change detection using correlation image analysis and image segmentation","volume":"29","author":"Im","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","first-page":"77","article-title":"Supervised change detection in VHR images using contextual information and support vector machines","volume":"20","author":"Volpi","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/TGRS.2008.916643","article-title":"A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure","volume":"46","author":"Bovolo","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/LGRS.2009.2028438","article-title":"Fast Object-Level Change Detection for VHR Images","volume":"7","author":"Huo","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3525","DOI":"10.1109\/JSTARS.2014.2330808","article-title":"Concurrent Self-Organizing Maps for Supervised\/Unsupervised Change Detection in Remote Sensing Images","volume":"7","author":"Neagoe","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2013.09.010","article-title":"Semi-supervised change detection using modified self-organizing feature map neural network","volume":"15","author":"Ghosh","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_38","first-page":"345","article-title":"Completion of the 2011 National Land Cover Database for the conterminous United States\u2013representing a decade of land cover change information","volume":"81","author":"Homer","year":"2015","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.rse.2005.09.008","article-title":"A change detection model based on neighborhood correlation image analysis and decision tree classification","volume":"99","author":"Im","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource remote sensing data classification based on convolutional neural network","volume":"56","author":"Xu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wu, H., and Prasad, S. (2017). Convolutional Recurrent Neural Networks for Hyperspectral Data Classification. Remote Sens., 9.","DOI":"10.3390\/rs9030298"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/LGRS.2018.2889307","article-title":"Landslide Inventory Mapping from Bitemporal Images Using Deep Convolutional Neural Networks","volume":"16","author":"Lei","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A. (2019, January 22\u201325). Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803050"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, W., Cheng, D., Yin, P., Yang, M., Li, E., Xie, M., and Zhang, L. (2019). Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010049"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., and Zhang, Y. (2018). Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10071119"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1109\/TGRS.2017.2707528","article-title":"Forest Change Detection in Incomplete Satellite Images with Deep Neural Networks","volume":"55","author":"Khan","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","article-title":"Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images","volume":"116","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1109\/TGRS.2017.2650198","article-title":"Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images","volume":"55","author":"Gong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","unstructured":"Xie, J., Xu, L., and Chen, E. (2012, January 3\u20136). Image denoising and inpainting with deep neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_54","first-page":"3371","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1109\/JSTARS.2017.2672736","article-title":"Object-Based Land-Cover Supervised Classification for Very-High-Resolution UAV Images Using Stacked Denoising Autoencoders","volume":"10","author":"Zhang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vincent, P., LaRochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lv, Z., Liu, T., Benediktsson, J.A., Lei, T., and Wan, Y. (2018). Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10111809"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gu, H., Han, Y., Yang, Y., Li, H., Liu, Z., Soergel, U., Blaschke, T., and Cui, S. (2018). An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10040590"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3027","DOI":"10.1109\/TFUZZ.2018.2796074","article-title":"Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering","volume":"26","author":"Lei","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Lei, T., Xue, D., Lv, Z., Li, S., Zhang, Y., and Nandi, A.K. (2018). Unsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution images. Remote Sens., 10.","DOI":"10.3390\/rs10091381"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"36600","DOI":"10.1109\/ACCESS.2019.2902613","article-title":"Multiscale superpixel segmentation with deep features for change detection","volume":"7","author":"Lei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1109\/TGRS.2011.2168230","article-title":"Unsupervised change detection of satellite images using local gradual descent","volume":"50","author":"Yetgin","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"7846","DOI":"10.3390\/rs70607846","article-title":"Validation of land cover products using reliability evaluation methods","volume":"7","author":"Shi","year":"2015","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, X., Shi, W., and Lv, Z. (2019). Uncertainty Assessment in Multitemporal Land Use\/Cover Mapping with Classification System Semantic Heterogeneity. Remote Sens., 11.","DOI":"10.3390\/rs11212509"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2787\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:37:30Z","timestamp":1760189850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/23\/2787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,26]]},"references-count":64,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["rs11232787"],"URL":"https:\/\/doi.org\/10.3390\/rs11232787","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,26]]}}}