{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:51:09Z","timestamp":1774425069241,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"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 (NSFC)","doi-asserted-by":"publisher","award":["41971313"],"award-info":[{"award-number":["41971313"]}],"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>Single-object tracking (SOT) in satellite videos (SVs) is a promising and challenging task in the remote sensing community. In terms of the object itself and the tracking algorithm, the rotation of small-sized objects and tracking drift are common problems due to the nadir view coupled with a complex background. This article proposes a novel rotation adaptive tracker with motion constraint (RAMC) to explore how the hybridization of angle and motion information can be utilized to boost SV object tracking from two branches: rotation and translation. We decouple the rotation and translation motion patterns. The rotation phenomenon is decomposed into the translation solution to achieve adaptive rotation estimation in the rotation branch. In the translation branch, the appearance and motion information are synergized to enhance the object representations and address the tracking drift issue. Moreover, an internal shrinkage (IS) strategy is proposed to optimize the evaluation process of trackers. Extensive experiments on space-born SV datasets captured from the Jilin-1 satellite constellation and International Space Station (ISS) are conducted. The results demonstrate the superiority of the proposed method over other algorithms. With an area under the curve (AUC) of 0.785 and 0.946 in the success and precision plots, respectively, the proposed RAMC achieves optimal performance while running at real-time speed.<\/jats:p>","DOI":"10.3390\/rs14133108","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T01:48:38Z","timestamp":1656467318000},"page":"3108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6757-9051","authenticated-orcid":false,"given":"Yuzeng","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5578-4330","authenticated-orcid":false,"given":"Yuqi","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8973-3363","authenticated-orcid":false,"given":"Te","family":"Han","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Yuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}]},{"given":"Bin","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1535-9940","authenticated-orcid":false,"given":"Huihui","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"},{"name":"Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Makovski, T., Vazquez, G.A., and Jiang, Y.V. (2008). Visual Learning in Multiple-Object Tracking. PLoS ONE, 3.","DOI":"10.1371\/journal.pone.0002228"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xing, J., Ai, H., and Lao, S. (2010, January 23\u201326). Multiple Human Tracking Based on Multi-view Upper-Body Detection and Discriminative Learning. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.420"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, G., and Vela, P.A. (2015, January 7\u201312). Good Features to Track for VisuaL SLAM. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298743"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1109\/TPAMI.2013.230","article-title":"Visual Tracking: An Experimental Survey","volume":"36","author":"Smeulders","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","first-page":"850","article-title":"Fully-Convolutional Siamese Networks for Object Tracking","volume":"Volume 9914","author":"Bertinetto","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nam, H., and Han, B. (2016, January 27\u201330). Learning Multi-domain Convolutional Neural Networks for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.465"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201323). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 16\u201320). SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhong, B., Li, G., Zhang, S., and Ji, R. (2020, January 14\u201319). Siamese Box Adaptive Network for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-Speed Tracking with Kernelized Correlation Filters","volume":"37","author":"Henriques","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P.H.S. (2016, January 27\u201330). Staple: Complementary Learners for Real-Time Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.156"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Galoogahi, H.K., Fagg, A., and Lucey, S. (2017, January 22\u201329). Learning Background-Aware Correlation Filters for Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.129"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1007\/s11263-017-1061-3","article-title":"Discriminative Correlation Filter Tracker with Channel and Spatial Reliability","volume":"126","author":"Lukezic","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","unstructured":"Hong, S., You, T., Kwak, S., and Han, B. (2015, January 7\u20139). Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_16","unstructured":"Nam, H., Baek, M., and Han, B. (2016). Modeling and Propagating CNNs in a Tree Structure for Visual Tracking. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., and Torr, P.H.S. (2019, January 16\u201320). Fast Online Object Tracking and Segmentation: A Unifying Approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2015, January 11\u201318). Convolutional Features for Correlation Filter Based Visual Tracking. Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.84"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Robinson, A., Khan, F.S., and Felsberg, M. (2016, January 8\u201316). Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1_29"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Bhat, G., Khan, F.S., and Felsberg, M. (2017, January 21\u201326). ECO: Efficient Convolution Operators for Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual Object Tracking using Adaptive Correlation Filters. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_22","first-page":"702","article-title":"Exploiting the Circulant Structure of Tracking-by-Detection with Kernels","volume":"Volume 7575","author":"Henriques","year":"2012","journal-title":"European Conference on Computer Vision"},{"key":"ref_23","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Y., and Liu, G. (2016, January 25\u201328). Learning a Scale-and-Rotation Correlation Filter for Robust Visual Tracking. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532398"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rout, L., Mishra, D., and Gorthi, R. (2018, January 12\u201315). Rotation Adaptive Visual Object Tracking with Motion Consistency. Proceedings of the 18th IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahou, NV\/CA, USA.","DOI":"10.1109\/WACV.2018.00120"},{"key":"ref_26","unstructured":"Li, Y., Zhu, J., Hoi, S.C.H., Song, W., Wang, Z., Liu, H. (March, January 27). Robust Estimation of Similarity Transformation for Visual Object Tracking. Proceedings of the 33rd AAAI Conference on Artificial Intelligence\/31st Innovative Applications of Artificial Intelligence Conference\/9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Danelljan, M., Hager, G., Khan, F.S., and Felsberg, M. (2015, January 11\u201318). Learning Spatially Regularized Correlation Filters for Visual Tracking. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.490"},{"key":"ref_28","first-page":"361","article-title":"Satellite video processing and applications","volume":"34","author":"Zhang","year":"2016","journal-title":"J. Appl. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7010","DOI":"10.1109\/TGRS.2020.2978512","article-title":"Small Target Tracking in Satellite Videos Using Background Compensation","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1109\/TIP.2020.3045634","article-title":"HRSiam: High-Resolution Siamese Network, Towards Space-Borne Satellite Video Tracking","volume":"30","author":"Shao","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhu, K., Zhang, X.D., Chen, G.Z., Tan, X.L., Liao, P.Y., Wu, H.Y., Cui, X.J., Zuo, Y.A., and Lv, Z.Y. (2021). Single Object Tracking in Satellite Videos: Deep Siamese Network Incorporating an Interframe Difference Centroid Inertia Motion Model. Remote Sens., 13.","DOI":"10.3390\/rs13071298"},{"key":"ref_32","unstructured":"Yin, Z.Y., and Tang, Y.Q. (October, January 26). Analysis of Traffic Flow in Urban Area for Satellite Video. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"44069","DOI":"10.1109\/ACCESS.2021.3059487","article-title":"Research on Multiview Stereo Mapping Based on Satellite Video Images","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"652213","DOI":"10.3389\/frwa.2021.652213","article-title":"Surface Flow Velocities From Space: Particle Image Velocimetry of Satellite Video of a Large, Sediment-Laden River","volume":"3","author":"Legleiter","year":"2021","journal-title":"Front. Water"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1109\/TIP.2019.2944097","article-title":"Needles in a Haystack: Tracking City-Scale Moving Vehicles from Continuously Moving Satellite","volume":"29","author":"Ao","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1109\/TGRS.2019.2943366","article-title":"Object Tracking in Satellite Videos by Improved Correlation Filters with Motion Estimations","volume":"58","author":"Xuan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1109\/JSTARS.2019.2933488","article-title":"Object Tracking on Satellite Videos: A Correlation Filter-Based Tracking Method with Trajectory Correction by Kalman Filter","volume":"12","author":"Guo","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7860","DOI":"10.1109\/TGRS.2019.2916953","article-title":"Tracking Objects from Satellite Videos: A Velocity Feature Based Correlation Filter","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"8719","DOI":"10.1109\/TGRS.2019.2922648","article-title":"Can We Track Targets From Space? A Hybrid Kernel Correlation Filter Tracker for Satellite Video","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2021.01.058","article-title":"Rotation Adaptive Correlation Filter for Moving Object Tracking in Satellite Videos","volume":"438","author":"Xuan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, Y.S., Liao, Y.R., Lin, C.B., Jia, Y.T., Li, Z.M., and Yang, X.Y. (2022). Object Tracking in Satellite Videos Based on Correlation Filter with Multi-Feature Fusion and Motion Trajectory Compensation. Remote Sens., 14.","DOI":"10.3390\/rs14030777"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, Y.Z., Tang, Y.Q., Yin, Z.Y., Han, T., Zou, B., and Feng, H.H. (2022). Single Object Tracking in Satellite Videos: A Correlation Filter-Based Dual-Flow Tracker. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 1\u201313.","DOI":"10.1109\/JSTARS.2022.3185328"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/LGRS.2017.2776899","article-title":"Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm","volume":"15","author":"Du","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","first-page":"6","article-title":"Optical Flow Measurement Using Lucas Kanade Method","volume":"61","author":"Patel","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1109\/TPAMI.2011.236","article-title":"Motion Detail Preserving Optical Flow Estimation","volume":"34","author":"Xu","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/83.506761","article-title":"An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration","volume":"5","author":"Reddy","year":"1996","journal-title":"IEEE Trans. Image Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/TPAMI.1986.4767833","article-title":"An Investigation of Smoothness Constraints for The Estimation of Displacement Vector Fields from Image Sequences","volume":"8","author":"Nagel","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"ref_49","unstructured":"Farneb\u00e4ck, G. (July, January 29). Two-Frame Motion Estimation Based on Polynomial Expansion. Proceedings of the Scandinavian Conference on Image Analysis, Halmstad, Sweden."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lim, J., and Yang, M.-H. (2013, January 23\u201328). Online Object Tracking: A Benchmark. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.312"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1834","DOI":"10.1109\/TPAMI.2014.2388226","article-title":"Object Tracking Benchmark","volume":"37","author":"Wu","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3043","DOI":"10.1109\/JSTARS.2019.2917703","article-title":"Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker","volume":"12","author":"Du","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, Y., and Zhu, J. (2015). A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-16181-5_18"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, F., Tian, C., Zuo, W., Zhang, L., and Yang, M.H. (2018, January 18\u201323). Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00515"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Dai, K., Wang, D., Lu, H., Sun, C., and Li, J. (2019, January 15\u201320). Visual Tracking via Adaptive Spatially-Regularized Correlation Filters. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00480"},{"key":"ref_56","unstructured":"Xu, T.Y., Feng, Z.H., Wu, X.J., and Kittler, J. (November, January 27). Joint Group Feature Selection and Discriminative Filter Learning for Robust Visual Object Tracking. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, Y., Fu, C., Ding, F., Huang, Z., and Lu, G. (2020, January 13\u201319). AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01194"},{"key":"ref_58","unstructured":"Ren, S.Q., He, K.M., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the 29th Annual Conference on Neural Information Processing Systems (NIPS), Montreal, Canada."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:41Z","timestamp":1760139581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133108"],"URL":"https:\/\/doi.org\/10.3390\/rs14133108","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,28]]}}}