{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T20:55:41Z","timestamp":1769633741864,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"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":["No. 62271159"],"award-info":[{"award-number":["No. 62271159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62071136"],"award-info":[{"award-number":["No. 62071136"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62002083"],"award-info":[{"award-number":["No. 62002083"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61971153"],"award-info":[{"award-number":["No. 61971153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["YQ2022F002"],"award-info":[{"award-number":["YQ2022F002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LBH-Q20085"],"award-info":[{"award-number":["LBH-Q20085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LBH-Z20051"],"award-info":[{"award-number":["LBH-Z20051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["3072022QBZ0805"],"award-info":[{"award-number":["3072022QBZ0805"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["3072021CFT0801"],"award-info":[{"award-number":["3072021CFT0801"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["3072022CF0808"],"award-info":[{"award-number":["3072022CF0808"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["No. 62271159"],"award-info":[{"award-number":["No. 62271159"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["No. 62071136"],"award-info":[{"award-number":["No. 62071136"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["No. 62002083"],"award-info":[{"award-number":["No. 62002083"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["No. 61971153"],"award-info":[{"award-number":["No. 61971153"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["YQ2022F002"],"award-info":[{"award-number":["YQ2022F002"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["LBH-Q20085"],"award-info":[{"award-number":["LBH-Q20085"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["LBH-Z20051"],"award-info":[{"award-number":["LBH-Z20051"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["3072022QBZ0805"],"award-info":[{"award-number":["3072022QBZ0805"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["3072021CFT0801"],"award-info":[{"award-number":["3072021CFT0801"]}]},{"name":"Heilongjiang Outstanding Youth Foundation","award":["3072022CF0808"],"award-info":[{"award-number":["3072022CF0808"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["No. 62271159"],"award-info":[{"award-number":["No. 62271159"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["No. 62071136"],"award-info":[{"award-number":["No. 62071136"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["No. 62002083"],"award-info":[{"award-number":["No. 62002083"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["No. 61971153"],"award-info":[{"award-number":["No. 61971153"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["YQ2022F002"],"award-info":[{"award-number":["YQ2022F002"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["LBH-Q20085"],"award-info":[{"award-number":["LBH-Q20085"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["LBH-Z20051"],"award-info":[{"award-number":["LBH-Z20051"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["3072022QBZ0805"],"award-info":[{"award-number":["3072022QBZ0805"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["3072021CFT0801"],"award-info":[{"award-number":["3072021CFT0801"]}]},{"name":"Heilongjiang Postdoctoral Foundation","award":["3072022CF0808"],"award-info":[{"award-number":["3072022CF0808"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["No. 62271159"],"award-info":[{"award-number":["No. 62271159"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["No. 62071136"],"award-info":[{"award-number":["No. 62071136"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["No. 62002083"],"award-info":[{"award-number":["No. 62002083"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["No. 61971153"],"award-info":[{"award-number":["No. 61971153"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["YQ2022F002"],"award-info":[{"award-number":["YQ2022F002"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["LBH-Q20085"],"award-info":[{"award-number":["LBH-Q20085"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["LBH-Z20051"],"award-info":[{"award-number":["LBH-Z20051"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["3072022QBZ0805"],"award-info":[{"award-number":["3072022QBZ0805"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["3072021CFT0801"],"award-info":[{"award-number":["3072021CFT0801"]}]},{"name":"Fundamental Research Funds for the Central Universities Grant","award":["3072022CF0808"],"award-info":[{"award-number":["3072022CF0808"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral video with spatial and spectral information has great potential to improve object tracking performance. However, the limited hyperspectral training samples hinder the development of hyperspectral object tracking. Since hyperspectral data has multiple bands, from which any three bands can be extracted to form pseudocolor images, we propose a Transformer-based multimodality information transfer network (TMTNet), aiming to improve the tracking performance by efficiently transferring the information of multimodality data composed of RGB and hyperspectral in the hyperspectral tracking process. The multimodality information needed to be transferred mainly includes the RGB and hyperspectral multimodality fusion information and the RGB modality information. Specifically, we construct two subnetworks to transfer the multimodality fusion information and the robust RGB visual information, respectively. Among them, the multimodality fusion information transfer subnetwork is designed based on the dual Siamese branch structure. The subnetwork employs the pretrained RGB tracking model as the RGB branch to guide the training of the hyperspectral branch with little training samples. The RGB modality information transfer subnetwork is designed based on a pretrained RGB tracking model with good performance to improve the tracking network\u2019s generalization and accuracy in unknown complex scenes. In addition, we design an information interaction module based on Transformer in the multimodality fusion information transfer subnetwork. The module can fuse multimodality information by capturing the potential interaction between different modalities. We also add a spatial optimization module to TMTNet, which further optimizes the object position predicted by the subject network by fully retaining and utilizing detailed spatial information. Experimental results on the only available hyperspectral tracking benchmark dataset show that the proposed TMTNet tracker outperforms the advanced trackers, demonstrating the effectiveness of this method.<\/jats:p>","DOI":"10.3390\/rs15041107","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1107","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["TMTNet: A Transformer-Based Multimodality Information Transfer Network for Hyperspectral Object Tracking"],"prefix":"10.3390","volume":"15","author":[{"given":"Chunhui","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjiao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9601-536X","authenticated-orcid":false,"given":"Nan","family":"Su","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2648-7227","authenticated-orcid":false,"given":"Congan","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Information Fusion, Naval Aviation University, Yantai 264000, China"},{"name":"Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0751-7726","authenticated-orcid":false,"given":"Yiming","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7308-9590","authenticated-orcid":false,"given":"Shou","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3719","DOI":"10.1109\/TIP.2020.2965302","article-title":"Material Based Object Tracking in Hyperspectral Videos","volume":"29","author":"Xiong","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., Xiong, F., Zhou, J., Wang, J., Lu, J., and Qian, Y. (2020, January 25\u201328). BAE-Net: A Band Attention Aware Ensemble Network for Hyperspectral Object Tracking. Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/ICIP40778.2020.9191105"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, Z., Ye, X., Xiong, F., Lu, J., Zhou, J., and Qian, Y. (2021, January 24\u201326). Spectral-Spatial-Temporal Attention Network for Hyperspectral Tracking. Proceedings of the 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS52202.2021.9484032"},{"key":"ref_4","first-page":"5612011","article-title":"An Enhanced SiamMask Network for Coastal Ship Tracking","volume":"60","author":"Yang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5064","DOI":"10.1109\/TGRS.2011.2158005","article-title":"Motion Tracking of Discontinuous Sea Ice","volume":"49","author":"Thomas","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fan, H., and Ling, H. (2019, January 15\u201320). Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00814"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4567","DOI":"10.1109\/TGRS.2017.2694159","article-title":"Object Tracking by Hierarchical Decomposition of Hyperspectral Video Sequences: Application to Chemical Gas Plume Tracking","volume":"55","author":"Tochon","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qian, K., Zhou, J., Xiong, F., Zhou, H., and Du, J. (2018). Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter. arXiv.","DOI":"10.1007\/978-3-030-04375-9_26"},{"key":"ref_9","unstructured":"Nguyen, H.V., Banerjee, A., and Chellappa, R. (2010, January 13\u201318). Tracking via object reflectance using a hyperspectral video camera. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition\u2014Workshops, San Francisco, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Uzkent, B., Hoffman, M.J., and Vodacek, A. (July, January 26). Real-Time Vehicle Tracking in Aerial Video Using Hyperspectral Features. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.181"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fang, H., Liao, Z., Wang, X., Chang, Y., and Yan, L. (2023). Differentiated Attention Guided Network Over Hierarchical and Aggregated Features for Intelligent UAV Surveillance. IEEE Trans. Ind. Inform., 1\u201312.","DOI":"10.1109\/TII.2022.3232777"},{"key":"ref_12","first-page":"1","article-title":"Infrared Small UAV Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion","volume":"71","author":"Fang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"7116","DOI":"10.1109\/TIP.2022.3216995","article-title":"SiamHYPER: Learning a Hyperspectral Object Tracker From an RGB-Based Tracker","volume":"31","author":"Liu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Z., Xiong, F., Lu, J., Zhou, J., and Qian, Y. (2022, January 23\u201327). Material-Guided Siamese Fusion Network for Hyperspectral Object Tracking. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9746089"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, X., Shu, M., Li, G., Sun, C., Liu, Z., and Zhong, Y. (2021, January 24\u201326). An Anchor-Free Siamese Target Tracking Network for Hyperspectral Video. Proceedings of the 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS52202.2021.9483958"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, N., Xiao, G., Zhang, X., and Bavirisetti, D.P. (2018, January 24\u201326). Relative object tracking algorithm based on convolutional neural network for visible and infrared video sequences. Proceedings of the ICVR 2018: 2018 4th International Conference on Virtual Reality, Hong Kong, China.","DOI":"10.1145\/3198910.3198918"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ye, P., Qiao, D., Zhao, J., Peng, S., and Xiao, G. (2019, January 2\u20135). Object Fusion Tracking Based on Visible and Infrared Images Using Fully Convolutional Siamese Networks. Proceedings of the 2019 22th International Conference on Information Fusion (FUSION), Ottawa, ON, Canada.","DOI":"10.23919\/FUSION43075.2019.9011253"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115756","DOI":"10.1016\/j.image.2019.115756","article-title":"DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion","volume":"84","author":"Zhang","year":"2020","journal-title":"Signal Process. Image Commun."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Satar, B., Hongyuan, Z., Bresson, X., and Lim, J.H. (2021, January 19\u201322). Semantic Role Aware Correlation Transformer For Text To Video Retrieval. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506267"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Wang, S., Huang, Z., and Chen, K. (2019, January 22\u201325). Text Recognition in Images Based on Transformer with Hierarchical Attention. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803203"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Le, T., Nguyen, H.T., and Nguyen, M.L. (2021, January 19\u201322). Vision And Text Transformer For Predicting Answerability On Visual Question Answering. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506796"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Su, N., Liu, H., Zhao, C., Yan, Y., Wang, J., and He, J. (2022, January 13\u201316). A Transformer-Based Three-Branch Siamese Network For Hyperspectral Object Tracking. Proceedings of the 2022 12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Rome, Italy.","DOI":"10.1109\/WHISPERS56178.2022.9955082"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, D., Wang, J., Cui, Y., Wang, Z., and Chen, S. (2020, January 13\u201319). SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00630"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhong, B., Li, G., Zhang, S., and Ji, R. (2020, January 13\u201319). Siamese Box Adaptive Network for Visual Tracking. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00670"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, D., Shao, Y., Cui, Y., Wang, Z., Zhang, L., and Shen, C. (2021, January 20\u201325). Graph Attention Tracking. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00942"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fang, H., Wang, X., Liao, Z., Chang, Y., and Yan, L. (2021, January 11\u201317). A Real-time Anti-distractor Infrared UAV Tracker with Channel Feature Refinement Module. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00144"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, M., Li, B., and Chen, H. (2017, January 17\u201320). Multi-scale 3D deep convolutional neural network for hyperspectral image classification. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297014"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 15\u201320). SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., and Lu, H. (2021, January 20\u201325). Transformer Tracking. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"ref_30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lanchantin, J., Wang, T., Ordonez, V., and Qi, Y. (2021, January 20\u201325). General Multi-label Image Classification with Transformers. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01621"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., Zheng, C., Huang, X., and Zhang, Z. (2021). Exploiting Multi-view Part-wise Correlation via an Efficient Transformer for Vehicle Re-Identification. IEEE Trans. Multimedia.","DOI":"10.1109\/TMM.2021.3134839"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zou, C., Wang, B., Hu, Y., Liu, J., Wu, Q., Zhao, Y., Li, B., Zhang, C., Zhang, C., and Wei, Y. (2021, January 20\u201325). End-to-End Human Object Interaction Detection with HOI Transformer. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01165"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yan, B., Zhang, X., Wang, D., Lu, H., and Yang, X. (2021, January 20\u201325). Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box Estimation. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00525"},{"key":"ref_35","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 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., and Torr, P.H. (2019, January 15\u201320). Fast Online Object Tracking and Segmentation: A Unifying Approach. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Xu, J., Liu, L., Zhu, F., and Shao, L. (November, January 27). RANet: Ranking Attention Network for Fast Video Object Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00408"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","article-title":"CornerNet: Detecting Objects as Paired Keypoints","volume":"128","author":"Law","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Luke\u017ei\u010d, A., Matas, J., and Kristan, M. (2020, January 13\u201319). D3S\u2014A Discriminative Single Shot Segmentation Tracker. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00716"},{"key":"ref_40","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 Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_41","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 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.733"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Li, C., Luo, B., Tang, J., and Wang, X. (2019, January 21\u201325). Dense Feature Aggregation and Pruning for RGBT Tracking. Proceedings of the Proceedings of the 27th ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350928"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, L., Danelljan, M., Gonzalez-Garcia, A., van de Weijer, J., and Shahbaz Khan, F. (2019, January 27\u201328). Multi-Modal Fusion for End-to-End RGB-T Tracking. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00278"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1107\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:39:49Z","timestamp":1760121589000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1107"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,17]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041107"],"URL":"https:\/\/doi.org\/10.3390\/rs15041107","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,17]]}}}