{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:09:41Z","timestamp":1775326181890,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"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":["62101060"],"award-info":[{"award-number":["62101060"]}],"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":["4214058"],"award-info":[{"award-number":["4214058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"650 Beijing Natural Science Foundation","award":["62101060"],"award-info":[{"award-number":["62101060"]}]},{"name":"650 Beijing Natural Science Foundation","award":["4214058"],"award-info":[{"award-number":["4214058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the advancement of deep neural networks, several methods leveraging convolution neural networks (CNNs) have gained prominence in the field of remote sensing object detection. Acquiring accurate feature representations from feature maps is a critical step in CNN-based object detection methods. Previously, region of interest (RoI)-based methods have been widely used, but of late, deformable convolution network (DCN)-based approaches have started receiving considerable attention. A significant challenge in the use of DCN-based methods is the inefficient distribution patterns of sampling points, stemming from a lack of effective and flexible guidance. To address this, our study introduces Saliency-Guided RepPoints (SGR), an innovative framework designed to enhance feature representation quality in remote sensing object detection. SGR employs a dynamic dual-domain alignment (DDA) training strategy to mitigate potential misalignment issues between spatial and feature domains during the learning process. Furthermore, we propose an interpretable visualization method to assess the alignment between feature representation and classification performance in DCN-based methods, providing theoretical analysis and validation for the effectiveness of sampling points. In this study, we assessed the proposed SGR framework through a series of experiments conducted on four varied and rigorous datasets: DOTA, HRSC2016, DIOR-R, and UCAS-AOD, all of which are widely employed in remote sensing object detection. The outcomes of these experiments substantiate the effectiveness of the SGR framework, underscoring its potential to enhance the accuracy of object detection within remote sensing imagery.<\/jats:p>","DOI":"10.3390\/rs16020250","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T03:36:58Z","timestamp":1704771418000},"page":"250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["SGR: An Improved Point-Based Method for Remote Sensing Object Detection via Dual-Domain Alignment Saliency-Guided RepPoints"],"prefix":"10.3390","volume":"16","author":[{"given":"Shuhua","family":"Mai","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6473-9187","authenticated-orcid":false,"given":"Yanan","family":"You","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Yunxiang","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201315). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.eswa.2022.116793","article-title":"Remote sensing image super-resolution and object detection: Benchmark and state of the art","volume":"197","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dong, Z., Wang, M., Wang, Y., Liu, Y., Feng, Y., and Xu, W. (2022). Multi-oriented object detection in high-resolution remote sensing imagery based on convolutional neural networks with adaptive object orientation features. Remote Sens., 14.","DOI":"10.3390\/rs14040950"},{"key":"ref_6","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2015, January 7\u201315). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xie, X.X., Cheng, G., Wang, J.B., Yao, X.W., and Han, J.W. (2021, January 10\u201317). Oriented R-CNN for Object Detection. Proceedings of the 18th IEEE\/CVF International Conference on Computer Vision (ICCV), New York, NY, USA.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable convolutional networks. Proceedings of the IEEE international conference on computer vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhu, X.Z., Hu, H., Lin, S., Dai, J.F., and Soc, I.C. (2019, January 27\u201328). Deformable ConvNets v2: More Deformable, Better Results. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New York, NY, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_10","first-page":"11","article-title":"Align Deep Features for Oriented Object Detection","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cheng, B., Wei, Y., Shi, H., Feris, R., Xiong, J., and Huang, T. (2018, January 8\u201314). Revisiting rcnn: On awakening the classification power of faster rcnn. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_28"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., Lu, Q.K., and Soc, I.C. (2019, January 15\u201320). Learning RoI Transformer for Oriented Object Detection in Aerial Images. Proceedings of the 32nd IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhou, Q., and Yu, C.H. (2022). Point RCNN: An Angle-Free Framework for Rotated Object Detection. Remote Sens., 14.","DOI":"10.3390\/rs14112605"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hou, L.P., Lu, K., Xue, J., Li, Y.Q., and Assoc Advancement Artificial, I. (2022, January 23\u201325). Shape-Adaptive Selection and Measurement for Oriented Object Detection. Proceedings of the 36th AAAI Conference on Artificial Intelligence\/34th Conference on Innovative Applications of Artificial Intelligence\/12th Symposium on Educational Advances in Artificial Intelligence, Palo Alto, CA, USA.","DOI":"10.1609\/aaai.v36i1.19975"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, T., Sun, X., Zhuang, L., Dong, X., Sha, J., Zhang, B., and Zheng, K. (2023). AFRE-Net: Adaptive Feature Representation Enhancement for Arbitrary Oriented Object Detection. Remote Sens., 15.","DOI":"10.3390\/rs15204965"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hou, L.P., Lu, K., Yang, X., Li, Y.Q., and Xue, J. (2023). G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection. Remote Sens., 15.","DOI":"10.3390\/rs15030757"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Xu, C., Ding, J., Wang, J., Yang, W., Yu, H., Yu, L., and Xia, G.S. (2023, January 17\u201324). Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00707"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, S.H., Hu, H., Wang, L., and Lin, S. (2019, January 27\u201328). RepPoints: Point Set Representation for Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), New York, NY, USA.","DOI":"10.1109\/ICCV.2019.00975"},{"key":"ref_19","first-page":"5621","article-title":"Reppoints v2: Verification meets regression for object detection","volume":"33","author":"Chen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, W.T., Chen, Y.J., Hu, K.X., and Zhu, J.K. (2022, January 18\u201324). Oriented RepPoints for Aerial Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00187"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s11023-020-09539-2","article-title":"Artificial intelligence, values, and alignment","volume":"30","author":"Gabriel","year":"2020","journal-title":"Minds Mach."},{"key":"ref_22","first-page":"1","article-title":"Dual-aligned oriented detector","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5220\/0006120603240331","article-title":"A high resolution optical satellite image dataset for ship recognition and some new baselines","volume":"Volume 2","author":"Liu","year":"2017","journal-title":"International Conference on Pattern Recognition Applications and Methods"},{"key":"ref_25","first-page":"1","article-title":"Anchor-free oriented proposal generator for object detection","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, H., Chen, X., Dai, W., Fu, K., Ye, Q., and Jiao, J. (2015, January 27\u201330). Orientation robust object detection in aerial images using deep convolutional neural network. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351502"},{"key":"ref_27","first-page":"6009305","article-title":"Reppoints-Based Multi-Scale Task Enhancement Network and Sample Assignment Method For Oriented Object Detection","volume":"20","author":"Li","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Le, T.V., Van, H.N.N., Bui, D.C., Vo, P., Vo, N.D., and Nguyen, K. (2022, January 27\u201329). Empirical study of reppoints representation for object detection in aerial images. Proceedings of the 2022 IEEE Ninth International Conference on Communications and Electronics (ICCE), Nha Trang, Vietnam.","DOI":"10.1109\/ICCE55644.2022.9852099"},{"key":"ref_29","unstructured":"Xu, C., Su, H., Gao, L., Wu, J., Yan, W., and Li, J. (2021). International Forum on Digital TV and Wireless Multimedia Communications, Springer."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gao, L., Gao, H., Wang, Y., Liu, D., and Momanyi, B.M. (2023). Center-Ness and Repulsion: Constraints to Improve Remote Sensing Object Detection via RepPoints. Remote Sens., 15.","DOI":"10.3390\/rs15061479"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2020, January 23\u201328). Arbitrary-oriented object detection with circular smooth label. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Proceedings, Part VIII 16, Glasgow, UK.","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6121","DOI":"10.1109\/TGRS.2020.3014195","article-title":"OPD-Net: Prow detection based on feature enhancement and improved regression model in optical remote sensing imagery","volume":"59","author":"You","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2021, January 18\u201324). Rethinking rotated object detection with gaussian wasserstein distance loss. Proceedings of the International Conference on Machine Learning, PMLR, Virtual Event."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Zhou, Y., Wang, W., and Yan, J. (2020, January 13\u201319). Dense label encoding for boundary discontinuity free rotation detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_35","unstructured":"Yu, Y., and Da, F. (2021, January 20\u201325). Phase-shifting coder: Predicting accurate orientation in oriented object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2019, January 15\u201320). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_37","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2020, January 7\u201312). Dynamic anchor learning for arbitrary-oriented object detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, J., Song, L., Li, Z., Sun, H., Sun, J., and Zheng, N. (2020, January 13\u201319). End-to-end object detection with fully convolutional network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR46437.2021.01559"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/LGRS.2021.3115110","article-title":"Optimization for Arbitrary-Oriented Object Detection via Representation Invariance Loss","volume":"19","author":"Ming","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, X., Wang, J., Pang, J., Lyu, C., Zhang, W., Luo, P., and Chen, K. (2023, January 17\u201324). Dense Distinct Query for End-to-End Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00708"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency detection: A spectral residual approach. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and Da Silva, E.A. (2020, January 1\u20133). A survey on performance metrics for object-detection algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niteroi, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_44","unstructured":"Qian, W., Yang, X., Peng, S., Yan, J., and Guo, Y. (2023, January 7\u201314). Learning modulated loss for rotated object detection. Proceedings of the AAAI Conference on Aartificial Intelligence, Washington, DC, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J.C., Feng, Z.M., and He, T. (2021, January 23\u201325). R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object. Proceedings of the 35th AAAI Conference on Artificial Intelligence\/33rd Conference on Innovative Applications of Artificial Intelligence\/11th Symposium on Educational Advances in Artificial Intelligence, Palo Alto, CA, USA.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A context-aware detection network for objects in remote sensing imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (2018, January 18\u201323). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF international Conference on Computer Vision, Salt Lake City, UT, USA.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Li, C., Xu, C., Cui, Z., Wang, D., Zhang, T., and Yang, J. (2019, January 22\u201325). Feature-attentioned object detection in remote sensing imagery. Proceedings of the 2019 IEEE international Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803521"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, J., Ding, J., Guo, H., Cheng, W., Pan, T., and Yang, W. (2019). Mask OBB: A semantic attention-based mask oriented bounding box representation for multi-category object detection in aerial images. Remote Sens., 11.","DOI":"10.3390\/rs11242930"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1109\/TGRS.2020.3010051","article-title":"Learning center probability map for detecting objects in aerial images","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021, January 20\u201325). Redet: A rotation-equivariant detector for aerial object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_53","unstructured":"Zhou, X., Wang, D., and Kr\u00e4henb\u00fchl, P. (2019). Objects as points. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, K., Lin, W., See, J., Yu, H., Ke, Y., and Yang, C. (2020, January 23\u201328). Piou loss: Towards accurate oriented object detection in complex environments. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Part V 16.","DOI":"10.1007\/978-3-030-58558-7_12"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yang, J., Liu, Q., and Zhang, K. (2017, January 21\u201326). Stacked hourglass network for robust facial landmark localisation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.253"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020, January 13\u201319). Dynamic refinement network for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., and Ye, Q. (2021, January 20\u201325). Beyond bounding-box: Convex-hull feature adaptation for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00868"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1109\/TIP.2022.3148874","article-title":"A general Gaussian heatmap label assignment for arbitrary-oriented object detection","volume":"31","author":"Huang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_60","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/250\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:42:26Z","timestamp":1760103746000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/250"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020250"],"URL":"https:\/\/doi.org\/10.3390\/rs16020250","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]}}}