{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:31:02Z","timestamp":1769751062246,"version":"3.49.0"},"reference-count":58,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,18]],"date-time":"2021-12-18T00:00:00Z","timestamp":1639785600000},"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":["61725501"],"award-info":[{"award-number":["61725501"]}],"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>Remote sensing (RS) image change detection (CD) is a critical technique of detecting land surface changes in earth observation. Deep learning (DL)-based approaches have gained popularity and have made remarkable progress in change detection. The recent advances in DL-based methods mainly focus on enhancing the feature representation ability for performance improvement. However, deeper networks incorporated with attention-based or multiscale context-based modules involve a large number of network parameters and require more inference time. In this paper, we first proposed an effective network called 3M-CDNet that requires about 3.12 M parameters for accuracy improvement. Furthermore, a lightweight variant called 1M-CDNet, which only requires about 1.26 M parameters, was proposed for computation efficiency with the limitation of computing power. 3M-CDNet and 1M-CDNet have the same backbone network architecture but different classifiers. Specifically, the application of deformable convolutions (DConv) in the lightweight backbone made the model gain a good geometric transformation modeling capacity for change detection. The two-level feature fusion strategy was applied to improve the feature representation. In addition, the classifier that has a plain design to facilitate the inference speed applied dropout regularization to improve generalization ability. Online data augmentation (DA) was also applied to alleviate overfitting during model training. Extensive experiments have been conducted on several public datasets for performance evaluation. Ablation studies have proved the effectiveness of the core components. Experiment results demonstrate that the proposed networks achieved performance improvements compared with the state-of-the-art methods. Specifically, 3M-CDNet achieved the best F1-score on two datasets, i.e., LEVIR-CD (0.9161) and Season-Varying (0.9749). Compared with existing methods, 1M-CDNet achieved a higher F1-score, i.e., LEVIR-CD (0.9118) and Season-Varying (0.9680). In addition, the runtime of 1M-CDNet is superior to most, which exhibits a better trade-off between accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/rs13245152","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"5152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["An Efficient Lightweight Neural Network for Remote Sensing Image Change Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-9723","authenticated-orcid":false,"given":"Kaiqiang","family":"Song","sequence":"first","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"}]},{"given":"Fengzhi","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"}]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China"},{"name":"Key Laboratory of Precision Opto-Mechatronics Technology, Ministry of Education, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,18]]},"reference":[{"key":"ref_1","first-page":"4205","article-title":"On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID","volume":"14","author":"Long","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2009.10.002","article-title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","volume":"65","author":"Bouziani","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","unstructured":"Gupta, R., Hosfelt, R., Sajeev, S., Patel, N., Goodman, B., Doshi, J., Heim, E., Choset, H., and Gaston, M. (2019, January 16\u201320). xBD: A Dataset for Assessing Building Damage from Satellite Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shi, W., Min, Z., Zhang, R., Chen, S., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Int. J. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"369","DOI":"10.14358\/PERS.69.4.369","article-title":"Land-Use\/Land-Cover Change Detection Using Improved Change-Vector Analysis","volume":"69","author":"Chen","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TIP.2006.888195","article-title":"The Regularized Iteratively Reweighted MAD Method for Change Detection in Multi- and Hyperspectral Data","volume":"16","author":"Nielsen","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Q., and Liu, L. (2017). Unsupervised Change Detection for Multispectral Remote Sensing Images Using Random Walks. Int. J. Remote Sens., 9.","DOI":"10.3390\/rs9050438"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ins.2010.10.016","article-title":"Fuzzy clustering algorithms for unsupervised change detection in remote sensing images","volume":"181","author":"Ghosh","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"4002","DOI":"10.1109\/TGRS.2018.2819367","article-title":"Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery","volume":"56","author":"Lv","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Han, Y., Javed, A., Jung, S., and Liu, S. (2020). Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster\u2013Shafer Theory. Int. J. Remote Sens., 12.","DOI":"10.3390\/rs12060983"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tan, K., Zhang, Y., Wang, X., and Chen, Y. (2019). Object-Based Change Detection Using Multiple Classifiers and Multi-Scale Uncertainty Analysis. Int. J. Remote Sens., 11.","DOI":"10.3390\/rs11030359"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, X., Liu, S., Du, P., Liang, H., Xia, J., and Li, Y. (2018). Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning. Int. J. Remote Sens., 10.","DOI":"10.3390\/rs10020276"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Touazi, A., and Bouchaffra, D. (2015, January 14\u201316). A k-Nearest Neighbor approach to improve change detection from remote sensing: Application to optical aerial images. Proceedings of the 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), Marrakech, Morocco.","DOI":"10.1109\/ISDA.2015.7489208"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7998","DOI":"10.1080\/01431161.2018.1479794","article-title":"A novel change detection approach based on visual saliency and random forest from multi-temporal high-resolution remote-sensing images","volume":"39","author":"Feng","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"3416","DOI":"10.1109\/TGRS.2009.2022633","article-title":"Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model","volume":"47","author":"Benedek","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2017.07.009","article-title":"A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion","volume":"199","author":"Wu","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1109\/LGRS.2017.2766840","article-title":"Change Detection Based on Deep Features and Low Rank","volume":"14","author":"Hou","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"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":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_26","unstructured":"Daudt, R.C., Saux, B.L., 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_27","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Wanbing, G. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Int. J. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_28","first-page":"102348","article-title":"ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection","volume":"101","author":"Wang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","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_30","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_31","doi-asserted-by":"crossref","first-page":"1790","DOI":"10.1109\/TGRS.2019.2948659","article-title":"From W-Net to CDGAN: Bitemporal Change Detection via Deep Learning Techniques","volume":"58","author":"Hou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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 Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Fang, S., Li, K., Shao, J., and Li, Z. (2021). SNUNet-CD: A Densely Connected Siamese Network for Change Detection of VHR Images. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3056416"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, L., Li, Y., and Zhang, Y. (2021). HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images. Int. J. Remote Sens., 13.","DOI":"10.3390\/rs13081440"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102783","DOI":"10.1016\/j.cviu.2019.07.003","article-title":"Multitask learning for large-scale semantic change detection","volume":"187","author":"Boulch","year":"2019","journal-title":"Comput. Vis. Image. Underst."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7296","DOI":"10.1109\/TGRS.2020.3033009","article-title":"Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference","volume":"59","author":"Peng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yue, Y., Gao, W., Yun, S., Su, Q., Yin, H., and Zhang, Y. (2021). DifUnet++: A Satellite Images Change Detection Network Based on Unet++ and Differential Pyramid. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3049370"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4902","DOI":"10.1080\/01431161.2021.1906982","article-title":"NestNet: A multiscale convolutional neural network for remote sensing image change detection","volume":"42","author":"Yu","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017, January 4\u20139). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., and He, K. (2018, January 18\u201322). Non-Local Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_44","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. Int. J. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_45","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_46","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2021, January 4). Deformable DETR: Deformable Transformers for End-to-End Object Detection. Proceedings of the International Conference on Learning Representations, Vienna, Austria."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P. (2021, January 20\u201325). Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3034752","article-title":"Remote Sensing Image Change Detection With Transformers","volume":"10","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, J.-W., Wang, R., Ding, F., Liu, B., Jiao, L., and Zhang, J. (2020). A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12101619"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, R., Ding, F., Chen, J.W., Jiao, L., and Wang, L. (October, January 26). A Lightweight Convolutional Neural Network for Bitemporal Image Change Detection. Proceedings of the IGARSS 2020\u20142020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323964"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable ConvNets V2: More Deformable, Better Results. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_53","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y. (2020). YOLACT++: Better Real-time Instance Segmentation. IEEE Trans. Pattern. Anal. Mach. Intell.","DOI":"10.1109\/ICCV.2019.00925"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"565","DOI":"10.5194\/isprs-archives-XLII-2-565-2018","article-title":"Change Detection in Remote Sensing Images Using Conditional Advertisal Networks","volume":"42","author":"Lebedev","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_56","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201311). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the NeurlPS, Vancouver, BC, Canada."},{"key":"ref_57","unstructured":"Loshchilov, I., and Hutter, F. (2019, January 6\u20139). Decoupled Weight Decay Regularization. Proceedings of the ICLR, New Orleans, LA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., Wang, J., and Ma, A. (2020, January 13\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00415"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:51:43Z","timestamp":1760169103000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,18]]},"references-count":58,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245152"],"URL":"https:\/\/doi.org\/10.3390\/rs13245152","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,18]]}}}