{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:36:57Z","timestamp":1760233017659,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"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":["41971392"],"award-info":[{"award-number":["41971392"]}],"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>Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs\u2019 low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks.<\/jats:p>","DOI":"10.3390\/rs14246352","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T02:54:02Z","timestamp":1671159242000},"page":"6352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3238-8567","authenticated-orcid":false,"given":"Kai","family":"Yan","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650500, China"}]},{"given":"Yaxin","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4607-0501","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Yunnan Normal University, Kunming 650500, China"},{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650500, China"}]},{"given":"Lin","family":"Xing","sequence":"additional","affiliation":[{"name":"The Laboratory of Pattern Recognition and Artificial Intelligence, Yunnan Normal University, Kunming 650500, China"},{"name":"School of Physics and Electronic Information, Yunnan Normal University, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.isprsjprs.2016.07.003","article-title":"Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition","volume":"119","author":"Xiao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gao, S., Li, W., Sun, K., Wei, J., Chen, Y., and Wang, X. (2022). Built-Up Area Change Detection Using Multi-Task Network with Object-Level Refinement. Remote Sens., 14.","DOI":"10.3390\/rs14040957"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.isprsjprs.2018.04.013","article-title":"A scale-invariant change detection method for land use\/cover change research","volume":"141","author":"Xing","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lv, Z., Liu, T., Zhang, P., Atli Benediktsson, J., and Chen, Y. (2018). Land cover change detection based on adaptive contextual information using bi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.20944\/preprints201804.0377.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.isprsjprs.2016.03.007","article-title":"Spatio-temporal change detection from multidimensional arrays: Detecting deforestation from MODIS time series","volume":"117","author":"Lu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.isprsjprs.2021.08.026","article-title":"An unsupervised domain adaptation approach for change detection and its application to deforestation mapping in tropical biomes","volume":"181","author":"Vega","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiang, J., Xing, Y., Wei, W., Yan, E., Xiang, J., and Mo, D. (2022). DSNUNet: An Improved Forest Change Detection Network by Combining Sentinel-1 and Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14195046"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.isprsjprs.2021.01.022","article-title":"Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide","volume":"174","author":"Niu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2022.03.011","article-title":"Change detection-based co-seismic landslide mapping through extended morphological profiles and ensemble strategy","volume":"187","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1109\/JAS.2022.106082","article-title":"SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness","volume":"9","author":"Tang","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chen, P., Guo, L., Zhang, X., Qin, K., Ma, W., and Jiao, L. (2021). Attention-Guided Siamese Fusion Network for Change Detection of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13224597"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2021.05.002","article-title":"Object-level change detection with a dual correlation attention-guided detector","volume":"177","author":"Zhang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.isprsjprs.2021.10.001","article-title":"A hierarchical self-attention augmented Laplacian pyramid expanding network for change detection in high-resolution remote sensing images","volume":"182","author":"Cheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2022.05.001","article-title":"Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery","volume":"189","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2022.02.021","article-title":"FCCDN: Feature constraint network for VHR image change detection","volume":"187","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.isprsjprs.2021.12.005","article-title":"Land-use\/land-cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery","volume":"184","author":"Zhu","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, X., He, L., Qin, K., Dang, Q., Si, H., Tang, X., and Jiao, L. (2022). SMD-Net: Siamese Multi-Scale Difference-Enhancement Network for Change Detection in Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14071580"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zheng, J., Tian, Y., Yuan, C., Yin, K., Zhang, F., Chen, F., and Chen, Q. (2022). MDESNet: Multitask Difference-Enhanced Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14153775"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, J., Zhu, S., Gao, Y., Zhang, G., and Xu, Y. (2022). Change Detection for High-Resolution Remote Sensing Images Based on a Multi-Scale Attention Siamese Network. Remote Sens., 14.","DOI":"10.3390\/rs14143464"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","article-title":"SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer","volume":"9","author":"Ma","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_23","first-page":"2017","article-title":"Spatial transformer networks","volume":"28","author":"Jaderberg","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., and Brox, T. (2015, January 7\u201313). Flownet: Learning optical flow with convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.316"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2017, January 21\u201326). Flownet 2.0: Evolution of optical flow estimation with deep networks. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ranjan, A., and Black, M.J. (2017, January 21\u201326). Optical flow estimation using a spatial pyramid network. Proceedings of the IEEE conference on computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.291"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., and Kautz, J. (2018, January 18\u201322). Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00931"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Tiulpin, A., Sattler, T., Pollefeys, M., Rahtu, E., and Kannala, J. (2019, January 7\u201311). Dgc-net: Dense geometric correspondence network. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00115"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Rocco, I., Arandjelovic, R., and Sivic, J. (2017, January 21\u201326). Convolutional neural network architecture for geometric matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.12"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xiao, X., Lian, S., Luo, Z., and Li, S. (2018, January 19\u201321). Weighted res-unet for high-quality retina vessel segmentation. Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China.","DOI":"10.1109\/ITME.2018.00080"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/JBHI.2019.2912935","article-title":"Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal","volume":"24","author":"Guan","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_33","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3450","DOI":"10.1007\/s10489-020-01961-4","article-title":"STA-Net: Spatial-temporal attention network for video salient object detection","volume":"51","author":"Bi","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_40","first-page":"1","article-title":"SNUNet-CD: A densely connected Siamese network for change detection of VHR images","volume":"19","author":"Fang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2555","DOI":"10.1109\/TPAMI.2020.2976928","article-title":"A lightweight optical flow CNN\u2014Revisiting data fidelity and regularization","volume":"43","author":"Hui","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hui, T.W., Tang, X., and Loy, C.C. (2018, January 18\u201323). Liteflownet: A lightweight convolutional neural network for optical flow estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00936"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kim, S., Min, D., Jeong, S., Kim, S., Jeon, S., and Sohn, K. (2019, January 15\u201320). Semantic attribute matching networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01262"},{"key":"ref_44","unstructured":"Rocco, I., Cimpoi, M., Arandjelovi\u0107, R., Torii, A., Pajdla, T., and Sivic, J. (2018). Neighbourhood consensus networks. Adv. Neural Inf. Process. Syst., 31."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102972","DOI":"10.1016\/j.ijdrr.2022.102972","article-title":"Multi-UAV cooperative system for search and rescue based on YOLOv5","volume":"76","author":"Xing","year":"2022","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Stankovi\u0107, M., Mirza, M.M., and Karabiyik, U. (2021). UAV forensics: DJI mini 2 case study. Drones, 5.","DOI":"10.3390\/drones5020049"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Salamh, F.E., Mirza, M.M., and Karabiyik, U. (2021). UAV forensic analysis and software tools assessment: DJI Phantom 4 and Matrice 210 as case studies. Electronics, 10.","DOI":"10.3390\/electronics10060733"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"37947","DOI":"10.1109\/ACCESS.2018.2854712","article-title":"Autonomous UAV flight control for GPS-based navigation","volume":"6","author":"Kwak","year":"2018","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1111\/phor.12259","article-title":"GPS precise point positioning for UAV photogrammetry","volume":"33","author":"Grayson","year":"2018","journal-title":"Photogramm. Rec."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Annis, A., Nardi, F., Petroselli, A., Apollonio, C., Arcangeletti, E., Tauro, F., Belli, C., Bianconi, R., and Grimaldi, S. (2020). UAV-DEMs for small-scale flood hazard mapping. Water, 12.","DOI":"10.3390\/w12061717"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1080\/01431161.2017.1285085","article-title":"Generation of accurate digital elevation models from UAV acquired low percentage overlapping images","volume":"38","author":"Ajayi","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.measurement.2015.06.010","article-title":"DEM generation with UAV Photogrammetry and accuracy analysis in Sahitler hill","volume":"73","author":"Uysal","year":"2015","journal-title":"Measurement"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1109\/LWC.2019.2937077","article-title":"Joint user association and UAV location optimization for UAV-aided communications","volume":"8","author":"Xi","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/rse2.58","article-title":"Location, location, location: Considerations when using lightweight drones in challenging environments","volume":"4","author":"Duffy","year":"2018","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 24\u201327). Focal loss for dense object detection. Proceedings of the Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_57","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_58","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote sensing image change detection with transformers","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Chen, H., Pu, F., Yang, R., Tang, R., and Xu, X. (2022). RDP-Net: Region Detail Preserving Network for Change Detection. arXiv.","DOI":"10.1109\/TGRS.2022.3227098"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Truong, P., Danelljan, M., and Timofte, R. (2020, January 13\u201319). GLU-Net: Global-local universal network for dense flow and correspondences. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00629"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6352\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:10Z","timestamp":1760146930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":62,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246352"],"URL":"https:\/\/doi.org\/10.3390\/rs14246352","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}