{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:54:09Z","timestamp":1772150049690,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"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":["Nos.62172411,62172404"],"award-info":[{"award-number":["Nos.62172411,62172404"]}],"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>With the rapid development of unmanned aerial vehicles (UAVs), object re-identification (Re-ID) based on the UAV platforms has attracted increasing attention, and several excellent achievements have been shown in the traditional scenarios. However, object Re-ID in aerial imagery acquired from the UAVs is still a challenging task, which is mainly due to the reason that variable locations and diverse viewpoints in UAVs platform are always resulting in more appearance ambiguities among the intra-objects and inter-objects. To address the above issues, in this paper, we proposed an adaptively attention-driven cascade part-based graph embedding framework (AAD-CPGE) for UAV object Re-ID. The AAD-CPGE aims to optimally fuse node features and their topological characteristics on the multi-scale structured graphs of parts-based objects, and then adaptively learn the most correlated information for improving the object Re-ID performance. Specifically, we first executed GCNs on the parts-based cascade node feature graphs and topological feature graphs for acquiring multi-scale structured-graph feature representations. After that, we designed a self-attention-based module for adaptive node and topological features fusion on the constructed hierarchical parts-based graphs. Finally, these learning hybrid graph-structured features with the most correlation discriminative capability were applied for object Re-ID. Several experimental verifications on three widely used UAVs-based benchmark datasets were carried out, and comparison with some state-of-the-art object Re-ID approaches validated the effectiveness and benefits of our proposed AAD-CPGE Re-ID framework.<\/jats:p>","DOI":"10.3390\/rs14061436","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T22:15:04Z","timestamp":1647468904000},"page":"1436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Adaptively Attention-Driven Cascade Part-Based Graph Embedding Framework for UAV Object Re-Identification"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8862-8828","authenticated-orcid":false,"given":"Bo","family":"Shen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, CAS, Beijing 100093, China"},{"name":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Security, Institute of Information Engineering, CAS, Beijing 100093, China"},{"name":"School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2376","DOI":"10.1109\/TMM.2019.2898753","article-title":"Incremental re-identification by cross-direction and cross-ranking adaption","volume":"21","author":"Wang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y., Lu, Q., and Zhu, H. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Byun, S., Shin, I.K., Moon, J., Kang, J., and Choi, S.I. (2021). Road Traffic Monitoring from UAV Images Using Deep Learning Networks. Remote Sens., 13.","DOI":"10.3390\/rs13204027"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1109\/LSP.2021.3086675","article-title":"Learning Dynamic Spatial-Temporal Regularization for UAV Object Tracking","volume":"28","author":"Deng","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.comcom.2020.03.017","article-title":"Unmanned aerial vehicle for internet of everything: Opportunities and challenges","volume":"155","author":"Liu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Walambe, R., Marathe, A., and Kotecha, K. (2021). Multiscale object detection from drone imagery using ensemble transfer learning. Drones, 5.","DOI":"10.3390\/drones5030066"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/TITS.2019.2896273","article-title":"Structural analysis of attributes for vehicle re-identification and retrieval","volume":"21","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s11263-020-01402-2","article-title":"Viewpoint and scale consistency reinforcement for UAV vehicle re-identification","volume":"129","author":"Teng","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ma, Y., Li, Q., Chu, L., Zhou, Y., and Xu, C. (2021). Real-time detection and spatial localization of insulators for UAV inspection based on binocular stereo vision. Remote Sens., 13.","DOI":"10.3390\/rs13020230"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jiang, S., Jiang, W., Huang, W., and Yang, L. (2017). UAV-based oblique photogrammetry for outdoor data acquisition and offsite visual inspection of transmission line. Remote Sens., 9.","DOI":"10.3390\/rs9030278"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.3390\/rs13081416","article-title":"Toward More Robust and Real-Time Unmanned Aerial Vehicle Detection and Tracking via Cross-Scale Feature Aggregation Based on the Center Keypoint","volume":"13","author":"Min","year":"2021","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"133650","DOI":"10.1109\/ACCESS.2021.3116064","article-title":"A Few-Shot Learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-Identification","volume":"9","author":"Fan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1109\/TITS.2019.2901312","article-title":"Vehicle re-identification using quadruple directional deep learning features","volume":"21","author":"Zhu","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_14","unstructured":"Zhu, J., Zeng, H., Huang, J., Liao, S., Lei, Z., Cai, C., and Zheng, L. (2019, January 20\u201326). A dual-path model with adaptive attention for vehicle re-identification. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1109\/TIP.2019.2950796","article-title":"Group-group loss-based global-regional feature learning for vehicle re-identification","volume":"29","author":"Liu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5665","DOI":"10.1007\/s10489-020-02171-8","article-title":"Multiview image generation for vehicle reidentification","volume":"51","author":"Zhu","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/TMM.2020.2977528","article-title":"Person re-identification in aerial imagery","volume":"23","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TPAMI.2018.2886878","article-title":"Unsupervised person re-identification by deep asymmetric metric embedding","volume":"42","author":"Yu","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"181943","DOI":"10.1109\/ACCESS.2020.3029180","article-title":"A Novel Pedestrian Reidentification Method Based on a Multiview Generative Adversarial Network","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 11\u201314). A benchmark and simulator for uav tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"ref_21","unstructured":"Robicquet, A., Sadeghian, A., Alahi, A., and Savarese, S. (2020, January 23\u201328). Learning social etiquette: Human trajectory prediction in crowded scenes. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK."},{"key":"ref_22","unstructured":"Zhu, P., Wen, L., Bian, X., Ling, H., and Hu, Q. (2018). Vision meets drones: A challenge. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TBC.2019.2892585","article-title":"High-level multiple-UAV cinematography tools for covering outdoor events","volume":"65","author":"Madeline","year":"2019","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 8\u201314). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kalra, I., Singh, M., Nagpal, S., Singh, R., Vatsa, M., Li, G., and Sujit, P. (2019, January 14\u201318). Dronesurf: Benchmark dataset for drone-based face recognition. Proceedings of the 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756593"},{"key":"ref_26","first-page":"1529","article-title":"Ship detection from optical remote sensing images using multi-scale analysis and Fourier HOG descriptor","volume":"13","author":"Chao","year":"2019","journal-title":"Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"400","DOI":"10.3390\/rs10030400","article-title":"Ship detection in optical remote sensing images based on saliency and a rotation-invariant descriptor","volume":"10","author":"Chao","year":"2018","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1109\/TIP.2021.3056223","article-title":"Learning Person Re-Identification Models From Videos With Weak Supervision","volume":"30","author":"Wang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2976","DOI":"10.1109\/TIP.2019.2893066","article-title":"Dynamic graph co-matching for unsupervised video-based person re-identification","volume":"29","author":"Ye","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., and Jiao, J. (2018, January 18\u201322). Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00110"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhu, X., Gong, S., and Li, W. (2018, January 18\u201322). Transferable joint attribute-identity deep learning for unsupervised person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00242"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yu, H., Zheng, W., Wu, A., Guo, S., and Lai, J. (2019, January 16\u201320). Unsupervised person re-identification by soft multilabel learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00225"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.image.2019.04.021","article-title":"vehicle re-identification in still images: Application of semi-supervised learning and re-ranking","volume":"76","author":"Wu","year":"2019","journal-title":"Signal Process. Image Commun."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4526","DOI":"10.1109\/TCSVT.2019.2948267","article-title":"Deeply associative two-stage representations learning based on labels interval extension loss and group loss for person re-identification","volume":"30","author":"Huang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"172443","DOI":"10.1109\/ACCESS.2019.2956172","article-title":"A survey of vehicle re-identification based on deep learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cui, C., Sang, N., Gao, C., and Zou, L. (December, January 28). Vehicle re-identification by fusing multiple deep neural networks. Proceedings of the 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, Canada.","DOI":"10.1109\/IPTA.2017.8310090"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1109\/TMM.2018.2796240","article-title":"Group-sensitive triplet embedding for vehicle reidentification","volume":"20","author":"Bai","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lou, D., and Zha, Z. (2017, January 10\u201314). Improving triplet-wise training of convolutional neural network for vehicle re-identification. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019491"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TIP.2019.2959653","article-title":"A multi-scale spatial-temporal attention model for person re-identification in videos","volume":"29","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_40","first-page":"1","article-title":"Attention Mask-Based Network with Simple Color Annotation for UAV Vehicle Re-Identification","volume":"19","author":"Yao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4328","DOI":"10.1109\/TIP.2019.2910408","article-title":"Two-level attention network with multi-grain ranking loss for vehicle re-identification","volume":"28","author":"Guo","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., and Chen, Y. (2018, January 2\u20137). An end-to-end deep learning architecture for graph classification. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, Z., Wei, X., Wang, P., Guo, Y., and Wu, J. (2019, January 16\u201320). Multi-label image recognition with graph convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00532"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shi, L., Zhang, Y., Cheng, J., and Lu, H. (2019, January 16\u201320). Two-stream adaptive graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01230"},{"key":"ref_45","unstructured":"Shen, Y., Li, H., Yi, S., Chen, D., and Wang, X. (2019, January 16\u201320). Masked graph attention network for person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yang, J., Zheng, W., Yang, Q., Chen, Y., and Tian, Q. (2020, January 14\u201319). Spatial-temporal graph convolutional network for video-based person re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00335"},{"key":"ref_47","first-page":"2723","article-title":"Learning to adapt invariance in memory for person re-identification","volume":"43","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, H., Yi, S., Chen, D., and Wang, X. (2018, January 8\u201314). Person re-identification with deep similarity-guided graph neural network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_30"},{"key":"ref_49","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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jiang, B., Wang, X., Zheng, A., Tang, J., and Luo, B. (2021). Ph-GCN: Person retrieval with part-based hierarchical graph convolutional network. IEEE Trans. Multimed., early access.","DOI":"10.1109\/TMM.2021.3095789"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, X., and Gupta, A. (2018, January 8\u201314). Videos as space-time region graphs. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_25"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","article-title":"Wavelets on graphs via spectral graph theory","volume":"30","author":"Hammond","year":"2011","journal-title":"Appl. Comput. Harmon. Anal."},{"key":"ref_54","unstructured":"Kopf, T.N., and Welling, X. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_55","unstructured":"Hoang, N., and Takanori, M. (2019). Revisiting graph neural networks: All we have is low-pass filters. arXiv."},{"key":"ref_56","unstructured":"Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K. (2019, January 9\u201315). Simplifying graph convolutional networks. Proceedings of the International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhu, M., Bo, D., Cui, P., Shi, C., and Pei, J. (2020, January 6\u201310). Am-GCN: Adaptive multi-channel graph convolutional networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual.","DOI":"10.1145\/3394486.3403177"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Mei, T., and Ma, H. (2016, January 11\u201314). A deep learning-based approach to progressive vehicle re-identification for urban surveillance. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_53"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, X., Liu, W., Ma, H., and Fu, H. (2016, January 11\u201315). Large-scale vehicle re-identification in urban surveillance videos. Proceedings of the 2016 IEEE International Conference on Multimedia and Expo (ICME), Seattle, WA, USA.","DOI":"10.1109\/ICME.2016.7553002"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","article-title":"Provid: Progressive and multimodal vehicle reidentification for large-scale urban surveillance","volume":"20","author":"Liu","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zheng, L., Wang, S., Zhou, W., and Tian, Q. (2014, January 20\u201323). Bayes merging of multiple vocabularies for scalable image retrieval. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.252"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., and Tian, Q. (2015, January 13\u201316). Scalable person re-identification: A benchmark. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.133"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"He, B., Li, J., Zhao, Y., and Tian, Y. (2019, January 15\u201320). Part-regularized near-duplicate vehicle re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00412"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Khorramshahi, P., Peri, N., Chen, J., and Chellappa, R. (2020, January 23\u201328). The devil is in the details: Self-supervised attention for vehicle re-identification. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58568-6_22"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhu, X., Luo, Z., Fu, P., and Ji, X. (2020, January 14\u201319). VOC-ReID: Vehicle re-identification based on vehicle-orientation-camera. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00309"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Meng, D., Li, L., Liu, X., Li, Y., Yang, S., Zha, Z., Gao, X., Wang, S., and Huang, Q. (2020, January 13\u201319). Parsing-based view-aware embedding network for vehicle re-identification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00713"},{"key":"ref_67","unstructured":"Shen, F., Zhu, J., Zhu, X., Xie, Y., and Huang, J. (2020). Exploring spatial significance via hybrid pyramidal graph network for vehicle re-identification. IEEE Trans. Intell. Transp. Syst., 1\u201312."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1436\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:37:35Z","timestamp":1760135855000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1436"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,16]]},"references-count":67,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061436"],"URL":"https:\/\/doi.org\/10.3390\/rs14061436","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,16]]}}}