{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:29:55Z","timestamp":1770917395660,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62076223"],"award-info":[{"award-number":["62076223"]}]},{"name":"National Natural Science Foundation of China","award":["24ZX005"],"award-info":[{"award-number":["24ZX005"]}]},{"name":"National Natural Science Foundation of China","award":["232102211018"],"award-info":[{"award-number":["232102211018"]}]},{"name":"Key Research Project of Henan Province Universities","award":["62076223"],"award-info":[{"award-number":["62076223"]}]},{"name":"Key Research Project of Henan Province Universities","award":["24ZX005"],"award-info":[{"award-number":["24ZX005"]}]},{"name":"Key Research Project of Henan Province Universities","award":["232102211018"],"award-info":[{"award-number":["232102211018"]}]},{"name":"Key Science and Technology Program of Henan Province","award":["62076223"],"award-info":[{"award-number":["62076223"]}]},{"name":"Key Science and Technology Program of Henan Province","award":["24ZX005"],"award-info":[{"award-number":["24ZX005"]}]},{"name":"Key Science and Technology Program of Henan Province","award":["232102211018"],"award-info":[{"award-number":["232102211018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to detect high-value targets by solely utilizing image-level category labels; however, two problems have not been well addressed by existing methods. Firstly, the seed instances (SIs) are mined solely relying on the category score (CS) of each proposal, which is inclined to concentrate on the most salient parts of the object; furthermore, they are unreliable because the robustness of the CS is not sufficient due to the fact that the inter-category similarity and intra-category diversity are more serious in RSIs. Secondly, the localization accuracy is limited by the proposals generated by the selective search or edge box algorithm. To address the first problem, a segment anything model (SAM)-induced seed instance-mining (SSIM) module is proposed, which mines the SIs according to the object quality score, which indicates the comprehensive characteristic of the category and the completeness of the object. To handle the second problem, a SAM-based pseudo-ground truth-mining (SPGTM) module is proposed to mine the pseudo-ground truth (PGT) instances, for which the localization is more accurate than traditional proposals by fully making use of the advantages of SAM, and the object-detection heads are trained by the PGT instances in a fully supervised manner. The ablation studies show the effectiveness of the SSIM and SPGTM modules. Comprehensive comparisons with 15 WSOD methods demonstrate the superiority of our method on two RSI datasets.<\/jats:p>","DOI":"10.3390\/rs16091532","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T08:18:27Z","timestamp":1714119507000},"page":"1532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised Object Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4328-6411","authenticated-orcid":false,"given":"Xiaoliang","family":"Qian","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Chenyang","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"given":"Zhiwu","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8770-3862","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Washington, DC, USA.","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","first-page":"1","article-title":"Building a Bridge of Bounding Box Regression Between Oriented and Horizontal Object Detection in Remote Sensing Images","volume":"61","author":"Qian","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","first-page":"1","article-title":"Robust Few-Shot Aerial Image Object Detection via Unbiased Proposals Filtration","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"1","article-title":"SFRNet: Fine-Grained Oriented Object Recognition via Separate Feature Refinement","volume":"61","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"15171","DOI":"10.1109\/TPAMI.2023.3319634","article-title":"Mutual-Assistance Learning for Object Detection","volume":"45","author":"Xie","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112106","DOI":"10.1007\/s11432-022-3718-5","article-title":"Fewer is more: Efficient object detection in large aerial images","volume":"67","author":"Xie","year":"2024","journal-title":"Sci. China Inf. Sci."},{"key":"ref_8","first-page":"1","article-title":"MidNet: An anchor-and-angle-free detector for oriented ship detection in aerial images","volume":"61","author":"Liang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"1","article-title":"Mining High-Quality Pseudoinstance Soft Labels for Weakly Supervised Object Detection in Remote Sensing Images","volume":"61","author":"Qian","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1902","DOI":"10.1109\/JSTARS.2022.3150843","article-title":"Incorporating the completeness and difficulty of proposals into weakly supervised object detection in remote sensing images","volume":"15","author":"Qian","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","first-page":"103301","article-title":"Semantic segmentation guided pseudo label mining and instance re-detection for weakly supervised object detection in remote sensing images","volume":"119","author":"Qian","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7497","DOI":"10.1109\/JSTARS.2023.3304411","article-title":"Multiscale Image Splitting Based Feature Enhancement and Instance Difficulty Aware Training for Weakly Supervised Object Detection in Remote Sensing Images","volume":"16","author":"Qian","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"1","article-title":"Attention Erasing and Instance Sampling for Weakly Supervised Object Detection","volume":"62","author":"Xie","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TNNLS.2022.3178180","article-title":"Enhanced spatial feature learning for weakly supervised object detection","volume":"35","author":"Wu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TMM.2021.3125130","article-title":"Multiple instance detection networks with adaptive instance refinement","volume":"25","author":"Wu","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIP.2023.3343112","article-title":"Weakly Supervised Semantic Segmentation via Alternate Self-Dual Teaching","volume":"72","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5395","DOI":"10.1109\/TNNLS.2022.3204337","article-title":"Generalized weakly supervised object localization","volume":"35","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1109\/JSTARS.2020.3009352","article-title":"Channel-attention-based DenseNet network for remote sensing image scene classification","volume":"60","author":"Tong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/JSTARS.2022.3141826","article-title":"GCSANet: A global context spatial attention deep learning network for remote sensing scene classification","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tekumalla, R., and Banda, J.M. (2022, January 17\u201320). TweetDIS: A large twitter dataset for natural disasters built using weak supervision. Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan.","DOI":"10.1109\/BigData55660.2022.10020214"},{"key":"ref_21","first-page":"1","article-title":"Deep learning with weak supervision for disaster scene description in low-altitude imagery","volume":"60","author":"Tao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/JSTARS.2020.3037225","article-title":"SRARNet: A unified framework for joint superresolution and aircraft recognition","volume":"14","author":"Tang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230846","article-title":"Multi-object tracking in satellite videos with graph-based multitask modeling","volume":"60","author":"He","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","first-page":"2500518","article-title":"Dynamic Low-Rank and Sparse Priors Constrained Deep Autoencoders for Hyperspectral Anomaly Detection","volume":"73","author":"Lin","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1109\/JSTARS.2022.3229834","article-title":"Hyperspectral Anomaly Detection via Sparse Representation and Collaborative Representation","volume":"16","author":"Lin","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JSTARS.2022.3214508","article-title":"Dual Collaborative Constraints Regularized Low-Rank and Sparse Representation via Robust Dictionaries Construction for Hyperspectral Anomaly Detection","volume":"16","author":"Lin","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","first-page":"5002016","article-title":"Deep Self-Representation Learning Framework for Hyperspectral Anomaly Detection","volume":"73","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","first-page":"1","article-title":"Two-Stream Isolation Forest Based on Deep Features for Hyperspectral Anomaly Detection","volume":"20","author":"Cheng","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2023.3283403","article-title":"Multiple Instance Complementary Detection and Difficulty Evaluation for Weakly Supervised Object Detection in Remote Sensing Images","volume":"20","author":"Huo","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bilen, H., and Vedaldi, A. (2016, January 27\u201330). Weakly supervised deep detection networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.311"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tang, P., Wang, X., Bai, X., and Liu, W. (2017, January 21\u201326). Multiple instance detection network with online instance classifier refinement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.326"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ren, Z., Yu, Z., Yang, X., Liu, M.Y., Lee, Y.J., Schwing, A.G., and Kautz, J. (2020, January 13\u201319). Instance-aware, context-focused, and memory-efficient weakly supervised object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01061"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/TMM.2022.3198018","article-title":"Fi-wsod: Foreground information guided weakly supervised object detection","volume":"25","author":"Yin","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_34","first-page":"1","article-title":"Multiple instance graph learning for weakly supervised remote sensing object detection","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wei, Y., Shen, Z., Cheng, B., Shi, H., Xiong, J., Feng, J., and Huang, T. (2018, January 8\u201314). Ts2c: Tight box mining with surrounding segmentation context for weakly supervised object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_27"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/TPAMI.2018.2876304","article-title":"PCL: Proposal Cluster Learning for Weakly Supervised Object Detection","volume":"42","author":"Tang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","first-page":"1","article-title":"Self-guided proposal generation for weakly supervised object detection","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6502","DOI":"10.1109\/TCSVT.2022.3168547","article-title":"CBASH: Combined backbone and advanced selection heads with object semantic proposals for weakly supervised object detection","volume":"32","author":"Xia","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TIP.2022.3231744","article-title":"Selecting high-quality proposals for weakly supervised object detection with bottom-up aggregated attention and phase-aware loss","volume":"32","author":"Wu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"8002","DOI":"10.1109\/TGRS.2020.2985989","article-title":"Progressive contextual instance refinement for weakly supervised object detection in remote sensing images","volume":"58","author":"Feng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6946","DOI":"10.1109\/TGRS.2020.3030990","article-title":"TCANet: Triple context-aware network for weakly supervised object detection in remote sensing images","volume":"59","author":"Feng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zitnick, C.L., and Doll\u00e1r, P. (2014, January 6\u201312). Edge boxes: Locating object proposals from edges. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13, 2014.","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"ref_44","first-page":"11482","article-title":"Object instance mining for weakly supervised object detection","volume":"34","author":"Lin","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TIP.2023.3251026","article-title":"SDANet: Semantic-embedded density adaptive network for moving vehicle detection in satellite videos","volume":"32","author":"Feng","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","first-page":"1","article-title":"MR-selection: A meta-reinforcement learning approach for zero-shot hyperspectral band selection","volume":"61","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1109\/TMM.2022.3167805","article-title":"Co-Saliency Detection Guided by Group Weakly Supervised Learning","volume":"25","author":"Qian","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_48","first-page":"1","article-title":"Multi-complementary generative adversarial networks with contrastive learning for hyperspectral image classification","volume":"61","author":"Feng","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Qian, X., Zhang, N., and Wang, W. (2023). Smooth giou loss for oriented object detection in remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15051259"},{"key":"ref_50","unstructured":"Seo, J., Bae, W., Sutherland, D.J., Noh, J., and Kim, D. (2022). Proceedings of the Computer Vision\u2014ECCV 2022, Springer Nature."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 15\u201317). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TGRS.2017.2778300","article-title":"Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11263-012-0538-3","article-title":"Weakly supervised localization and learning with generic knowledge","volume":"100","author":"Deselaers","year":"2012","journal-title":"Int. J. Comput. Vis."},{"key":"ref_59","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_60","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (2017, January 21\u201326). Learning non-maximum suppression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.685"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Feng, X., Yao, X., Cheng, G., and Han, J. (2022, January 18\u201324). Weakly Supervised Rotation-Invariant Aerial Object Detection Network. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01375"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Chen, Z., Fu, Z., Jiang, R., Chen, Y., and Hua, X.S. (2020, January 13\u201319). Slv: Spatial likelihood voting for weakly supervised object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01301"},{"key":"ref_64","first-page":"1","article-title":"SAENet: Self-Supervised Adversarial and Equivariant Network for Weakly Supervised Object Detection in Remote Sensing Images","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yang, K., Zhang, P., Qiao, P., Wang, Z., Dai, H., Shen, T., Li, D., and Dou, Y. (2020, January 14\u201319). Rethinking Segmentation Guidance for Weakly Supervised Object Detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00481"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.isprsjprs.2023.01.011","article-title":"MOL: Towards accurate weakly supervised remote sensing object detection via Multi-view nOisy Learning","volume":"196","author":"Wang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","first-page":"1","article-title":"Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection","volume":"72","author":"Chen","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, G., Zhang, X., Peng, Z., Tang, X., Zhou, H., and Jiao, L. (2022). Absolute wrong makes better: Boosting weakly supervised object detection via negative deterministic information. arXiv.","DOI":"10.24963\/ijcai.2022\/192"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wan, F., Wei, P., Jiao, J., Han, Z., and Ye, Q. (2018, January 18\u201322). Min-entropy latent model for weakly supervised object detection. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00141"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/TGRS.2020.2991407","article-title":"Automatic weakly supervised object detection from high spatial resolution remote sensing images via dynamic curriculum learning","volume":"59","author":"Yao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1532\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:33:48Z","timestamp":1760106828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/9\/1532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":70,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16091532"],"URL":"https:\/\/doi.org\/10.3390\/rs16091532","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}