{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T11:25:05Z","timestamp":1773660305845,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61650201"],"award-info":[{"award-number":["61650201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61650201"],"award-info":[{"award-number":["61650201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61650201"],"award-info":[{"award-number":["61650201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61650201"],"award-info":[{"award-number":["61650201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Natural Science Foundation","award":["4202025"],"award-info":[{"award-number":["4202025"]}]},{"name":"Beijing Natural Science Foundation","award":["4202025"],"award-info":[{"award-number":["4202025"]}]},{"name":"Beijing Natural Science Foundation","award":["4202025"],"award-info":[{"award-number":["4202025"]}]},{"name":"Beijing Natural Science Foundation","award":["4202025"],"award-info":[{"award-number":["4202025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10044-026-01655-6","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:43:20Z","timestamp":1773657800000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PSTNet: object detection in remote sensing images with point supervision and object templates"],"prefix":"10.1007","volume":"29","author":[{"given":"Peng","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jun","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Yuanhua","family":"Qiao","sequence":"additional","affiliation":[]},{"given":"Baixian","family":"Zou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"issue":"4","key":"1655_CR1","doi-asserted-by":"publisher","first-page":"871","DOI":"10.3390\/rs14040871","volume":"14","author":"A Shafique","year":"2022","unstructured":"Shafique A, Cao G, Khan Z, Asad M, Aslam M (2022) Deep learning-based change detection in remote sensing images: a review. Remote Sens 14(4):871","journal-title":"Remote Sens"},{"key":"1655_CR2","doi-asserted-by":"crossref","unstructured":"Yang Z, Liu S, Hu H, Wang L, Lin S (2019) Reppoints: Point set representation for object detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9657\u20139666)","DOI":"10.1109\/ICCV.2019.00975"},{"key":"1655_CR3","doi-asserted-by":"crossref","unstructured":"Xu C, Ding J, Wang J, Yang W, Yu H, Yu L, Xia GS (2023) Dynamic coarse-to-fine learning for oriented tiny object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 7318\u20137328)","DOI":"10.1109\/CVPR52729.2023.00707"},{"key":"1655_CR4","doi-asserted-by":"crossref","unstructured":"Zhang S, Chi C, Yao Y, Lei Z, Li SZ (2020) Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 9759\u20139768)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"1655_CR5","first-page":"1","volume":"60","author":"J Han","year":"2021","unstructured":"Han J, Ding J, Li J, Xia GS (2021) Align deep features for oriented object detection. IEEE Trans Geosci Remote Sens 60:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1655_CR6","doi-asserted-by":"crossref","unstructured":"Bearman A, Russakovsky O, Ferrari V, Fei-Fei L (2016), September What\u2019s the point: Semantic segmentation with point supervision. In: European conference on computer vision (pp. 549\u2013565). Cham: Springer International Publishing","DOI":"10.1007\/978-3-319-46478-7_34"},{"key":"1655_CR7","doi-asserted-by":"crossref","unstructured":"Chen P, Yu X, Han X, Hassan N, Wang K, Li J, Ye Q (2022), October Point-to-box network for accurate object detection via single point supervision. In: European Conference on Computer Vision (pp. 51\u201367). Cham: Springer Nature Switzerland","DOI":"10.1007\/978-3-031-20077-9_4"},{"key":"1655_CR8","unstructured":"Yang X, Zhang G, Li W, Wang X, Zhou Y, Yan J (2022) H2rbox: Horizontal box annotation is all you need for oriented object detection. arXiv preprint arXiv:2210.06742"},{"key":"1655_CR9","doi-asserted-by":"crossref","unstructured":"Li W, Yuan Y, Wang S, Zhu J, Li J, Liu J, Zhang L (2023) Point2mask: Point-supervised panoptic segmentation via optimal transport. In:Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 572\u2013581)","DOI":"10.1109\/ICCV51070.2023.00059"},{"key":"1655_CR10","doi-asserted-by":"crossref","unstructured":"Yu Y, Yang X, Li Q, Da F, Dai J, Qiao Y, Yan J (2024) Point2rbox: Combine knowledge from synthetic visual patterns for end-to-end oriented object detection with single point supervision. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 16783\u201316793)","DOI":"10.1109\/CVPR52733.2024.01588"},{"key":"1655_CR11","doi-asserted-by":"crossref","unstructured":"Luo J, Yang X, Yu Y, Li Q, Yan J, Li Y (2024) Pointobb: Learning oriented object detection via single point supervision. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 16730\u201316740)","DOI":"10.1109\/CVPR52733.2024.01583"},{"issue":"3","key":"1655_CR12","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1007\/s12559-024-10270-8","volume":"16","author":"J Miao","year":"2024","unstructured":"Miao J, Liu P, Chen C, Qiao Y (2024) Normal Template Mapping: An Association-Inspired Handwritten Character Recognition Model. Cogn Comput 16(3):1103\u20131112","journal-title":"Cogn Comput"},{"key":"1655_CR13","doi-asserted-by":"crossref","unstructured":"Lin TY, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117\u20132125)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"11","key":"1655_CR14","doi-asserted-by":"publisher","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","volume":"20","author":"J Ma","year":"2018","unstructured":"Ma J, Shao W, Ye H, Wang L, Wang H, Zheng Y, Xue X (2018) Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans Multimedia 20(11):3111\u20133122","journal-title":"IEEE Trans Multimedia"},{"key":"1655_CR15","doi-asserted-by":"crossref","unstructured":"Ding J, Xue N, Long Y, Xia GS, Lu Q (2019) Learning RoI transformer for oriented object detection in aerial images. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 2849\u20132858)","DOI":"10.1109\/CVPR.2019.00296"},{"key":"1655_CR16","doi-asserted-by":"crossref","unstructured":"Xie X, Cheng G, Wang J, Yao X, Han J (2021) Oriented R-CNN for object detection. In: Proceedings of the IEEE\/CVF international conference on computer vision (pp. 3520\u20133529)","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"1655_CR17","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., \u2026 Fu, K. S. Towards More Robust Detection for Small, Cluttered and Rotated Objects. arXiv 2018. arXiv preprint arXiv:1811.07126.","DOI":"10.1109\/ICCV.2019.00832"},{"issue":"12","key":"1655_CR18","doi-asserted-by":"publisher","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","volume":"57","author":"G Zhang","year":"2019","unstructured":"Zhang G, Lu S, Zhang W (2019) CAD-Net: a context-aware detection network for objects in remote sensing imagery. IEEE Trans Geosci Remote Sens 57(12):10015\u201310024","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"14","key":"1655_CR19","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.3390\/rs16142515","volume":"16","author":"W Zhao","year":"2024","unstructured":"Zhao W, Fang Z, Cao J, Ju Z (2024) SPA: Annotating Small Object with a Single Point in Remote Sensing Images. Remote Sens 16(14):2515","journal-title":"Remote Sens"},{"key":"1655_CR20","doi-asserted-by":"crossref","unstructured":"Zhu H, Xu C, Zhang R, Xu F, Yang W, Zhang H, Xia GS (2024) Tiny Object Detection with Single Point Supervision. arXiv preprint arXiv:2412.05837","DOI":"10.1016\/j.isprsjprs.2025.05.006"},{"key":"1655_CR21","first-page":"59137","volume":"36","author":"Y Yu","year":"2023","unstructured":"Yu Y, Yang X, Li Q, Zhou Y, Da F, Yan J (2023) H2RBox-v2: Incorporating symmetry for boosting horizontal box supervised oriented object detection. Adv Neural Inf Process Syst 36:59137\u201359150","journal-title":"Adv Neural Inf Process Syst"},{"key":"1655_CR22","doi-asserted-by":"crossref","unstructured":"Xia GS, Bai X, Ding J, Zhu Z et al (2018) DOTA: A large-scale dataset for object detection in aerial images. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3974\u20133983)","DOI":"10.1109\/CVPR.2018.00418"},{"key":"1655_CR23","unstructured":"Ding J, Xue N, Xia GS, Bai X, Yang W et al Object detection in aerial images: A large-scale benchmark and challenges. arxiv 2021. arxiv preprint arxiv:2102.12219"},{"key":"1655_CR24","doi-asserted-by":"crossref","unstructured":"Liu Z, Yuan L, Weng L, Yang Y (2017), February A high resolution optical satellite image dataset for ship recognition and some new baselines. In: International conference on pattern recognition applications and methods (Vol. 2, pp. 324\u2013331). SciTePress","DOI":"10.5220\/0006120603240331"},{"key":"1655_CR25","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J et al (2019) Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst, 32"},{"key":"1655_CR26","unstructured":"Chen K, Wang J, Pang J, Cao Y, Xiong Y et al (2019) MMDetection: Open mmlab detection toolbox and benchmark. arxiv preprint arxiv:1906.07155"},{"key":"1655_CR27","doi-asserted-by":"crossref","unstructured":"Zhou Y, Yang X, Zhang G, Wang J, Liu Y et al (2022), October Mmrotate: A rotated object detection benchmark using pytorch. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 7331\u20137334)","DOI":"10.1145\/3503161.3548541"},{"key":"1655_CR28","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1655_CR29","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vision 115:211\u2013252","journal-title":"Int J Comput Vision"},{"key":"1655_CR30","doi-asserted-by":"crossref","unstructured":"Chen Q, Wang Y, Yang T, Zhang X, Cheng J, Sun J (2021) You only look one-level feature. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13039\u201313048)","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"1655_CR31","doi-asserted-by":"crossref","unstructured":"Lin TY, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980\u20132988)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1655_CR32","unstructured":"Yang X, Yan J, Ming Q, Wang W, Zhang X, Tian Q (2021), July Rethinking rotated object detection with gaussian wasserstein distance loss. In International conference on machine learning (pp. 11830\u201311841). PMLR"},{"key":"1655_CR33","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T (2019) Fcos: Fully convolutional one-stage object detection. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 9627\u20139636)","DOI":"10.1109\/ICCV.2019.00972"},{"key":"1655_CR34","doi-asserted-by":"crossref","unstructured":"Zhu T, Ferenczi B, Purkait P, Drummond T, Rezatofighi H, Van Den Hengel A (2023) Knowledge combination to learn rotated detection without rotated annotation. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 15518\u201315527)","DOI":"10.1109\/CVPR52729.2023.01489"},{"key":"1655_CR35","unstructured":"Wang W, Han C, Zhou T, Liu D (2023) Visual recognition with deep nearest centroids. In Proceedings of the Eleventh International Conference on Learning Representations"},{"key":"1655_CR36","doi-asserted-by":"crossref","unstructured":"Lu Y, Wang Q, Ma S, Geng T, Chen YV, Chen H, Liu D (2023) Transflow: Transformer as flow learner. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 18063\u201318073)","DOI":"10.1109\/CVPR52729.2023.01732"},{"key":"1655_CR37","unstructured":"Liang JC, Zhou TF, Liu D, Wang W (2023) CLUSTSEG: Clustering for universal segmentation. In Proceedings of the 40th International Conference on Machine Learning (pp. 20787\u201320809)"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01655-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01655-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01655-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T10:43:33Z","timestamp":1773657813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01655-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,16]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1655"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01655-6","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,16]]},"assertion":[{"value":"2 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"69"}}