{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T04:01:17Z","timestamp":1749355277129,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665983","type":"print"},{"value":"9789819665969","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6596-9_10","type":"book-chapter","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:31:15Z","timestamp":1749274275000},"page":"138-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AA-RPN: Adaptive Anchor-Based Region Proposal Network for\u00a0Remote Sensing Object Detection"],"prefix":"10.1007","author":[{"given":"Shuishui","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0398-3618","authenticated-orcid":false,"given":"Qingxuan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4690-5780","authenticated-orcid":false,"given":"Nick Jin Sean","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-7773","authenticated-orcid":false,"given":"Albert","family":"Bifet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"issue":"4","key":"10_CR1","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1109\/MGRS.2023.3312347","volume":"11","author":"X Zhang","year":"2023","unstructured":"Zhang, X., et al.: Remote sensing object detection meets deep learning: a metareview of challenges and advances. IEEE Geosci. Remote Sens. Mag. 11(4), 8\u201344 (2023)","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., Xia, G.S.: Redet: a rotation-equivariant detector for aerial object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2786\u20132795 (2021)","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Pu, Y., et al.: Adaptive rotated convolution for rotated object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6589\u20136600 (2023)","DOI":"10.1109\/ICCV51070.2023.00606"},{"key":"10_CR4","unstructured":"Lyu, C., et al.: Rtmdet: an empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784 (2022)"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.M., Yang, J., Li, X.: Large selective kernel network for remote sensing object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16794\u201316805 (2023)","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"10_CR6","first-page":"1","volume":"61","author":"D Wang","year":"2022","unstructured":"Wang, D., et al.: Advancing plain vision transformer toward remote sensing foundation model. IEEE Trans. Geosci. Remote Sens. 61, 1\u201315 (2022)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: MTP: advancing remote sensing foundation model via multi-task pretraining. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2024)","DOI":"10.1109\/JSTARS.2024.3408154"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: 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 (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"10_CR10","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"10_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"W Liu","year":"2016","unstructured":"Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21\u201337. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_2"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Xia, G.S., et al.: 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 (2018)","DOI":"10.1109\/CVPR.2018.00418"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., Lu, Q.: 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 (2019)","DOI":"10.1109\/CVPR.2019.00296"},{"issue":"4","key":"10_CR15","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","volume":"43","author":"Y Xu","year":"2020","unstructured":"Xu, Y., et al.: Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Trans. Pattern Anal. Mach. Intell. 43(4), 1452\u20131459 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., Han, J.: Oriented R-CNN for object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3520\u20133529 (2021)","DOI":"10.1109\/ICCV48922.2021.00350"},{"issue":"23","key":"10_CR17","doi-asserted-by":"publisher","first-page":"5499","DOI":"10.3390\/rs15235499","volume":"15","author":"Z Li","year":"2023","unstructured":"Li, Z., Hou, B., Wu, Z., Ren, B., Yang, C.: FCOSR: a simple anchor-free rotated detector for aerial object detection. Remote Sens. 15(23), 5499 (2023)","journal-title":"Remote Sens."},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Hou, L., Lu, K., Xue, J., Li, Y.: Shape-adaptive selection and measurement for oriented object detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 923\u2013932 (2022)","DOI":"10.1609\/aaai.v36i1.19975"},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1109\/TIP.2022.3148874","volume":"31","author":"Z Huang","year":"2022","unstructured":"Huang, Z., Li, W., Xia, X.G., Tao, R.: A general gaussian heatmap label assignment for arbitrary-oriented object detection. IEEE Trans. Image Process. 31, 1895\u20131910 (2022)","journal-title":"IEEE Trans. Image Process."},{"key":"10_CR20","unstructured":"Zhu, B., et al.: Autoassign: differentiable label assignment for dense object detection. arXiv preprint arXiv:2007.03496 (2020)"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., Huang, W.: Tood: task-aligned one-stage object detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3490\u20133499. IEEE Computer Society (2021)","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, Q., et al.: M2det: a single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 9259\u20139266 (2019)","DOI":"10.1609\/aaai.v33i01.33019259"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"10_CR26","doi-asserted-by":"publisher","first-page":"4587","DOI":"10.1109\/TIP.2021.3072811","volume":"30","author":"Z Li","year":"2021","unstructured":"Li, Z., Lang, C., Liew, J.H., Li, Y., Hou, Q., Feng, J.: Cross-layer feature pyramid network for salient object detection. IEEE Trans. Image Process. 30, 4587\u20134598 (2021)","journal-title":"IEEE Trans. Image Process."},{"issue":"8","key":"10_CR27","doi-asserted-by":"publisher","first-page":"1074","DOI":"10.1109\/LGRS.2016.2565705","volume":"13","author":"Z Liu","year":"2016","unstructured":"Liu, Z., Wang, H., Weng, L., Yang, Y.: Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Geosci. Remote Sens. Lett. 13(8), 1074\u20131078 (2016)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Muhammad, M.B., Yeasin, M.: Eigen-cam: class activation map using principal components. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20137. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9206626"},{"key":"10_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2022.3149780","volume":"60","author":"G Cheng","year":"2022","unstructured":"Cheng, G., et al.: Dual-aligned oriented detector. IEEE Trans. Geosci. Remote Sens. 60, 1\u201311 (2022). https:\/\/doi.org\/10.1109\/TGRS.2022.3149780","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Learning oriented object detection via naive geometric computing. IEEE Trans. Neural Netw. Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3242323"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6596-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:31:28Z","timestamp":1749274288000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6596-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665983","9789819665969"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6596-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}