光学遥感图像目标检测数据集综述

袁一钦; 李浪; 姚西文; 李玲君; 冯晓绪; 程塨; 韩军伟

doi:10.11834/jrs.20233457

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专辑

光学遥感图像目标检测数据集综述
A comprehensive review of optical remote-sensing image object detection datasets
2023年27卷第12期页码：2671-2687
收稿：2023-11-01，

纸质出版：2023-12-07
DOI： 10.11834/jrs.20233457
稿件说明：

移动端阅览

袁一钦，李浪，姚西文，李玲君，冯晓绪，程塨，韩军伟.2023.光学遥感图像目标检测数据集综述.遥感学报，27（12）： 2671-2687 DOI： 10.11834/jrs.20233457.

Yuan Y Q，Li L，Yao X W，Li L J，Feng X X，Cheng G and Han J W. 2023. A comprehensive review of optical remote-sensing image object detection datasets. National Remote Sensing Bulletin， 27（12）：2671-2687 DOI： 10.11834/jrs.20233457.

摘要

近年来，随着深度学习等人工智能技术在光学遥感目标检测领域中的快速发展，大量相关研究算法不断涌现，逐渐形成了一种基于数据驱动的光学遥感图像目标检测新范式。高质量的遥感数据成为了此类范式算法研究的前置条件和必要储备，遥感数据的重要性日益凸显。迄今为止，国内外各大研究机构已相继发布了数量众多且规模不一的光学遥感图像目标检测数据集，为基于深度学习的遥感图像目标检测算法的发展奠定了研究基础。然而，当前尚未有相关学者对已发布的光学遥感图像检测数据集进行全面的归纳整理与分析，针对此问题，本文全面调研领域文献，对2008年—2023年期间已发布的公开光学遥感图像检测数据集进行整合分析，并依据不同的数据标注方式进行划分，对其中的11个典型数据集进行了全面阐述，以表格的形式对所有的数据集信息进行归纳总结，同时采用3种分析方式去描述数据集的发展情况，即：元数据分析，从数量分布、地域分布、来源分布、规模分布着手；分辨率分析，从空间分辨率与光谱分辨率着手；基本信息分析，从类别数量、图像数量、实例数量及图像宽度信息着手，有效论证了光学遥感图像目标检测数据集必然朝着高质量、大规模、多类别的方向发展。此外，针对已发布的数据集，从水平框目标检测、旋转框目标检测以及细分检测方向（小目标检测和细粒度检测）等多个角度对相关算法的应用和发展进行了概述，证实了遥感数据对目标检测算法的研究具有积极的推动作用。综上，本文将为基于深度学习的目标检测算法在遥感领域的应用提供参考。

Abstract

With the introduction of artificial-intelligence technologies such as deep learning into the field of optical remote-sensing detection

various algorithms have emerged. The use of these algorithms has gradually formed a new paradigm of data-driven optical remote-sensing image object detection. Consequently

high-quality remote-sensing data has become a prerequisite and a necessary resource for researching these paradigm algorithms. highlighting the increasing importance of remote-sensing data. To date

numerous optical remote-sensing image object detection datasets have been published by major research institutions domestically and internationally. These datasets have laid the foundation for the development of deep learning-based remote-sensing image detection tasks. However

no comprehensive summarization and analysis of the published optical remote-sensing image detection datasets have been conducted by scholars. Therefore

this paper aimed to provide a comprehensive review of the published datasets and an overview of algorithm applications. We also aimed to provide a reference for subsequent research in related fields.

This paper presents an overview and synthesis of the optical remote-sensing image object detection datasets published between 2008 and 2023. The synthesis is based on an extensive and comprehensive survey of literature in the field. By reviewing and analyzing these datasets

we enable a comprehensive understanding of the progress and trends in optical remote-sensing image object detection dataset research.

This paper categorizes the optical remote-sensing image object detection datasets published from 2008 to 2023 based on the annotation method. A comprehensive description of 11 representative datasets is provided

and all dataset information are summarized in tabular form. The analysis considers the information in the datasets themselves and also the spatial and spectral resolution of the images in the datasets. Other basic information including the number of categories

number of images

number of instances

and image-width information are also considered. This analysis effectively demonstrates the trend toward high quality

large scale

and multi-category development of object-detection datasets for optical remote-sensing images. Additionally

we provide an overview of the development and application of algorithms related to published datasets from different perspectives (e.g.

horizontal bounding box object detection and rotated bounding box object detection)

as well as a subdivision of detection directions (e.g.

small object detection and fine-grained detection). Our findings confirm the influential role of remote-sensing data in driving algorithmic advances.

In summary

we offer a comprehensive review of optical remote-sensing image object detection datasets from various perspectives. To our best knowledge

this comprehensive review is the first one on such datasets in the field. The work serves as a valuable reference for subsequent research on deep learning-based optical remote-sensing image object detection

providing insights into data availability and research directions. This study is expected to contribute to the advancement of this field by offering a solid foundation for further investigation and innovation.

关键词

Keywords

references

Bakirman T and Sertel E . 2022 . HRPlanes: high resolution airplane dataset for deep learning. arXiv preprint arXiv:2204 . 10959

Benedek C , Descombes X and Zerubia J . 2012 . Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics . IEEE Transactions on Pattern Analysis and Machine Intelligence , 34 ( 1 ): 33 - 50 [ DOI: 10.1109/TPAMI.2011.94 http://dx.doi.org/10.1109/TPAMI.2011.94 ]

Cha K , Seo J and Lee T . 2023 . A billion-scale foundation model for remote sensing images. arXiv:2304 . 05215

Chen K Y , Wu M , Liu J M and Zhang C . 2020 . FGSD: a dataset for fine-grained ship detection in high resolution satellite images. arXiv:2003 . 06832

Cheng G and Han J W . 2016a . A survey on object detection in optical remote sensing images . ISPRS Journal of Photogrammetry and Remote Sensing , 117 : 11 - 28 [ DOI: 10.1016/j.isprsjprs.2016.03.014 http://dx.doi.org/10.1016/j.isprsjprs.2016.03.014 ]

Cheng G , Han J W , Zhou P C and Guo L . 2014 . Multi-class geospatial object detection and geographic image classification based on collection of part detectors . ISPRS Journal of Photogrammetry and Remote Sensing , 98 : 119 - 132 [ DOI: 10.1016/j.isprsjprs.2014.10.002 http://dx.doi.org/10.1016/j.isprsjprs.2014.10.002 ]

Cheng G , Wang J B , Li k , Xie X X , Lang C B , Yao Y Q and Han J W . 2022 . Anchor-free oriented proposal generator for object detection . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5625411 [ DOI: 10.1109/TGRS.2022.3183022 http://dx.doi.org/10.1109/TGRS.2022.3183022 ]

Cheng G , Yuan X , Yao X W , Yan K B , Zeng Q H , Xie X X and Han J W . 2023 . Towards large-scale small object detection: survey and benchmarks. arXiv preprint arXiv:2207 . 14096

Cheng G , Zhou P C and Han J W . 2016b . Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 12 ): 7405 - 7415 [ DOI: 10.1109/TGRS.2016.2601622 http://dx.doi.org/10.1109/TGRS.2016.2601622 ]

Devaki P , Vineetha P N , Reddy C H , Bharathi P , Karimulla S and Kumar S U . 2023 . Fine-grained feature enhancement for object detection in remote sensing images . International Research Journal of Modernization in Engineering Technology and Science , 5 ( 3 ): 2112 - 2118 [ DOI: 10.56726/IRJMETS34594 http://dx.doi.org/10.56726/IRJMETS34594 ]

Ding J , Xue N , Long Y , Xia G S and Lu Q K . 2019 . Learning roi transformer for oriented object detection in aerial images // Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE: 2844 - 2853 [ DOI: 10.1109/cvpr.2019.00296 http://dx.doi.org/10.1109/cvpr.2019.00296 ]

Ding J , Xue N , Xia G S , Bai X , Yang W , Yang M Y , Belongie S , Luo J B , Datcu M , Pelillo M and Zhang L P . 2022 . Object detection in aerial images: a large-scale benchmark and challenges . IEEE Transactions on Pattern Analysis and Machine Intelligence , 44 ( 11 ): 7778 - 7796 [ DOI: 10.1109/TPAMI.2021.3117983 http://dx.doi.org/10.1109/TPAMI.2021.3117983 ]

Fu S S , He Y F , Du X F and Zhu Y . 2023 . Anchor-free object detection in remote sensing images using a variable receptive field network . EURASIP Journal on Advances in Signal Processing , 2023 ( 1 ): 53 [ DOI: 10.1186/s13634-023-01013-2 http://dx.doi.org/10.1186/s13634-023-01013-2 ]

Girshick R , Donahue J , Darrell T and Malik J . 2014 . Rich feature hierarchies for accurate object detection and semantic segmentation // Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus : IEEE: 580 - 587 [ DOI: 10.1109/CVPR.2014.81 http://dx.doi.org/10.1109/CVPR.2014.81 ]

Han J M , Ding J , Li J and Xia G S . 2022a . Align deep features for oriented object detection . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5602511 [ DOI: 10.1109/TGRS.2021.3062048 http://dx.doi.org/10.1109/TGRS.2021.3062048 ]

Han J M , Ding J , Xue N and Xia G S . 2021 . ReDet: a rotation-equivariant detector for aerial object detection // Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville : IEEE: 2785 - 2794 [ DOI: 10.1109/cvpr46437.2021.00281 http://dx.doi.org/10.1109/cvpr46437.2021.00281 ]

Han Y Q , Yang X Y , Pu T and Peng Z M . 2022b . Fine-grained recognition for oriented ship against complex scenes in optical remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5612318 [ DOI: 10.1109/TGRS.2021.3123666 http://dx.doi.org/10.1109/TGRS.2021.3123666 ]

Haroon M , Shahzad M and Fraz M M . 2020 . Multisized object detection using spaceborne optical imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 3032 - 3046 [ DOI: 10.1109/jstars.2020.3000317 http://dx.doi.org/10.1109/jstars.2020.3000317 ]

He K M , Chen X L , Xie S N , Li Y H , Dollár P and Girshick R . 2022 . Masked autoencoders are scalable vision learners // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans : IEEE: 15979 - 15988 [ DOI: 10.1109/cvpr52688.2022.01553 http://dx.doi.org/10.1109/cvpr52688.2022.01553 ]

Heitz G and Koller D . 2008 . Learning spatial context: using stuff to find things // 10th European Conference on Computer Vision . Marseille, France : Springer: 30 - 43 [ DOI: 10.1007/978-3-540-88682-2_4 http://dx.doi.org/10.1007/978-3-540-88682-2_4 ]

Hsieh M R , Lin Y L and Hsu W H . 2017 . Drone-based object counting by spatially regularized regional proposal network // Proceedings of the 2017 IEEE International Conference on Computer Vision . Venice : IEEE: 4165 - 4173 [ DOI: 10.1109/iccv.2017.446 http://dx.doi.org/10.1109/iccv.2017.446 ]

Lam D , Kuzma R , McGee K , Dooley S , Laielli M , Klaric M , Bulatov Y and McCord B . 2018 . xView: objects in context in overhead imagery. arXiv preprint arXiv:1802 . 07856

Li K , Wan G , Cheng G , Meng L Q and Han J W . 2020a . Object detection in optical remote sensing images: a survey and a new benchmark . ISPRS Journal of Photogrammetry and Remote Sensing , 159 : 296 - 307 [ DOI: 10.1016/j.isprsjprs.2019.11.023 http://dx.doi.org/10.1016/j.isprsjprs.2019.11.023 ]

Li W T , Chen Y J , Hu K X and Zhu J K . 2022a . Oriented RepPoints for aerial object detection // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans : IEEE: 1819 - 1828 [ DOI: 10.1109/cvpr52688.2022.00187 http://dx.doi.org/10.1109/cvpr52688.2022.00187 ]

Li X , Deng J Y and Fang Y . 2022b . Few-shot object detection on remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5601614 [ DOI: 10.1109/TGRS.2021.3051383 http://dx.doi.org/10.1109/TGRS.2021.3051383 ]

Li Y Y , Pei X , Huang Q , Jiao L C , Shang R H and Marturi N . 2020b . Anchor-free single stage detector in remote sensing images based on multiscale dense path aggregation feature pyramid network . IEEE Access , 8 : 63121 - 63133 [ DOI: 10.1109/ACCESS.2020.2984310 http://dx.doi.org/10.1109/ACCESS.2020.2984310 ]

Lin T Y , Goyal P , Girshick R , He K M and Dollár P . 2017 . Focal loss for dense object detection // Proceedings of the 2017 IEEE International Conference on Computer Vision . Venice : IEEE: 2999 - 3007 [ DOI: 10.1109/iccv.2017.324 http://dx.doi.org/10.1109/iccv.2017.324 ]

Lin Y T , Feng P M , Guan J , Wang W W and Chambers J . 2021 . IENet: interacting embranchment one stage anchor free detector for orientation aerial object detection. arXiv:1912 . 00969

Liu F , Chen D L , Guan Z Q Y , Zhou X C , Zhu J L and Zhou J . 2023 . RemoteCLIP: a vision language foundation model for remote sensing. arXiv:2306 . 11029

Liu J M , Li S J , Zhou C S , Cao X Y , Gao Y and Wang B . 2022 . SRAF-Net: a scene-relevant anchor-free object detection network in remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 60 : 5405914 [ DOI: 10.1109/TGRS.2021.3124959 http://dx.doi.org/10.1109/TGRS.2021.3124959 ]

Liu K and Mattyus G . 2015 . Fast multiclass vehicle detection on aerial images . IEEE Geoscience and Remote Sensing Letters , 12 ( 9 ): 1938 - 1942 [ DOI: 10.1109/LGRS.2015.2439517 http://dx.doi.org/10.1109/LGRS.2015.2439517 ]

Liu Z K , Yuan L , Weng L B and Yang Y P . 2017 . A high resolution optical satellite image dataset for ship recognition and some new baselines // Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods . Porto : SciTePress: 324 - 331 [ DOI: 10.5220/0006120603240331 http://dx.doi.org/10.5220/0006120603240331 ]

Long Y , Gong Y P , Xiao Z F and Liu Q . 2017 . Accurate object localization in remote sensing images based on convolutional neural networks . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 5 ): 2486 - 2498 [ DOI: 10.1109/TGRS.2016.2645610 http://dx.doi.org/10.1109/TGRS.2016.2645610 ]

Long Y , Xia G S , Li S Y , Yang W , Yang M Y , Zhu X X , Zhang L P and Li D R . 2021 . On creating benchmark dataset for aerial image interpretation: reviews, guidances, and million-aid . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 4205 - 4230 [ DOI: 10.1109/JSTARS.2021.3070368 http://dx.doi.org/10.1109/JSTARS.2021.3070368 ]

Ma J Q , Shao W Y , Ye H , Wang L , Wang H , Zheng Y B and Xue X Y . 2018 . Arbitrary-oriented scene text detection via rotation proposals . IEEE Transactions on Multimedia , 20 ( 11 ): 3111 - 3122 [ DOI: 10.1109/TMM.2018.2818020 http://dx.doi.org/10.1109/TMM.2018.2818020 ]

Mundhenk T N , Konjevod G , Sakla W A and Boakye K . 2016 . A large contextual dataset for classification, detection and counting of cars with deep learning // 14th European Conference on Computer Vision . Amsterdam, The Netherlands : Springer: 785 - 800 [ DOI: 10.1007/978-3-319-46487-9_48 http://dx.doi.org/10.1007/978-3-319-46487-9_48 ]

Nie G T and Huang H . 2021 . A survey of object detection in optical remote sensing images . Acta Automatica Sinica , 47 ( 8 ): 1749 - 1768

聂光涛 , 黄华 . 2021 . 光学遥感图像目标检测算法综述 . 自动化学报 , 47 ( 8 ): 1749 - 1768 [ DOI: 10.16383/j.aas.c200596 http://dx.doi.org/10.16383/j.aas.c200596 ]

Nogueira K , Cesar C , Gama P H T , Machado G L S and dos Santos J A . 2019 . A tool for bridge detection in major infrastructure works using satellite images // 2019 XV Workshop de Visão Computacional (WVC) . São Bernardo do Campo : IEEE: 72 - 77 [ DOI: 10.1109/WVC.2019.8876942 http://dx.doi.org/10.1109/WVC.2019.8876942 ]

Razakarivony S and Jurie F . 2016 . Vehicle detection in aerial imagery: a small target detection benchmark . Journal of Visual Communication and Image Representation , 34 : 187 - 203 [ DOI: 10.1016/j.jvcir.2015.11.002 http://dx.doi.org/10.1016/j.jvcir.2015.11.002 ]

Redmon J and Farhadi A . 2018 . YOLOv3: an incremental improvement. arXiv:1804 . 02767

Ren S Q , He K M , Girshick R and Sun J . 2015 . Faster R-CNN: towards real-time object detection with region proposal networks // Proceedings of the 28th International Conference on Neural Information Processing Systems . Montreal : MIT Press: 91 - 99

Shermeyer J , Hossler T , Van Etten A , Hogan D , Lewis R and Kim D . 2021 . RarePlanes: synthetic data takes flight // Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision . Waikoloa : IEEE: 207 - 217 [ DOI: 10.1109/wacv48630.2021.00025 http://dx.doi.org/10.1109/wacv48630.2021.00025 ]

Shi T J , Gong J N , Jiang S K , Zhi X Y , Bao G Z , Sun Y and Zhang W . 2023 . Complex optical remote-sensing aircraft detection dataset and benchmark . IEEE Transactions on Geoscience and Remote Sensing , 61 : 5612309 [ DOI: 10.1109/TGRS.2023.3283137 http://dx.doi.org/10.1109/TGRS.2023.3283137 ]

Shivappriya S N , Priyadarsini M J P , Stateczny A , Puttamadappa C and Parameshachari B D . 2021 . Cascade object detection and remote sensing object detection method based on trainable activation function . Remote Sensing , 13 ( 2 ): 200 [ DOI: 10.3390/rs13020200 http://dx.doi.org/10.3390/rs13020200 ]

Song J J , Miao L J , Ming Q , Zhou Z Q and Dong Y P . 2023 . Fine-grained object detection in remote sensing images via adaptive label assignment and refined-balanced feature pyramid network . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 16 : 71 - 82 [ DOI: 10.1109/JSTARS.2022.3224558 http://dx.doi.org/10.1109/JSTARS.2022.3224558 ]

Sun X , Wang P J , Lu W X , Zhu Z C , Lu X N , He Q B , Li J X , Rong X E , Yang Z J , Chang H , He Q L , Yang G , Wang R P , Lu J W and Fu K . 2023 . RingMo: a remote sensing foundation model with masked image modeling . IEEE Transactions on Geoscience and Remote Sensing , 61 : 5612822 [ DOI: 10.1109/TGRS.2022.3194732 http://dx.doi.org/10.1109/TGRS.2022.3194732 ]

Sun X , Wang P J , Yan Z Y , Xu F , Wang R P , Diao W H , Chen J , Li J H , Feng Y C , Xu T , Weinmann M , Hinz S , Wang C and Fu K . 2022 . FAIR 1 M: a benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing , 184 : 116 - 130 [ DOI: 10.1016/j.isprsjprs.2021.12.004 http://dx.doi.org/10.1016/j.isprsjprs.2021.12.004 ]

Tanner F , Colder B , Pullen C , Heagy D , Eppolito M , Carlan V , Oertel C and Sallee P . 2009 . Overhead imagery research data set—An annotated data library and tools to aid in the development of computer vision algorithms // 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) . Washington : IEEE: 1 - 8 [ DOI: 10.1109/AIPR.2009.5466304 http://dx.doi.org/10.1109/AIPR.2009.5466304 ]

Tian Z , Shen C H , Chen H and He T . 2019 . FCOS: fully convolutional one-stage object detection // Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision . Seoul : IEEE: 9626 - 9635 [ DOI: 10.1109/iccv.2019.00972 http://dx.doi.org/10.1109/iccv.2019.00972 ]

Uijlings J R R , Van De Sande K E A , Gevers T and Smeulders A W M . 2013 . Selective search for object recognition . International Journal of Computer Vision , 104 ( 2 ): 154 - 171 [ DOI: 10.1007/s11263-013-0620-5 http://dx.doi.org/10.1007/s11263-013-0620-5 ]

Wang J , Yang L and Li F . 2021a . Predicting arbitrary-oriented objects as points in remote sensing images . Remote Sensing , 13 ( 18 ): 3731 [ DOI: 10.3390/rs13183731 http://dx.doi.org/10.3390/rs13183731 ]

Wang J W , Yang W , Guo H W , Zhang R X and Xia G S . 2021b . Tiny object detection in aerial images // 2020 25th International Conference on Pattern Recognition (ICPR) . Milan : IEEE: 3791 - 3798 [ DOI: 10.1109/ICPR48806.2021.9413340 http://dx.doi.org/10.1109/ICPR48806.2021.9413340 ]

Wang J W , Yang W , Li H C , Zhang H J and Xia G S . 2021c . Learning center probability map for detecting objects in aerial images . IEEE Transactions on Geoscience and Remote Sensing , 59 ( 5 ): 4307 - 4323 [ DOI: 10.1109/TGRS.2020.3010051 http://dx.doi.org/10.1109/TGRS.2020.3010051 ]

Wang K , Wang Z , Li Z , Su A , Teng X C , Liu M H and Yu Q F . 2023 . Oriented object detection in optical remote sensing images using deep learning: a survey. arXiv preprint arXiv:2302 . 10473

Wei H R , Zhang Y , Chang Z H , Li H , Wang H Q and Sun X . 2020 . Oriented objects as pairs of middle lines . ISPRS Journal of Photogrammetry and Remote Sensing , 169 : 268 - 279 [ DOI: 10.1016/j.isprsjprs.2020.09.022 http://dx.doi.org/10.1016/j.isprsjprs.2020.09.022 ]

Xia G S , Bai X , Ding J , Zhu Z , Belongie S , Luo J B , Datcu M , Pelillo M and Zhang L P . 2018 . DOTA: a large-scale dataset for object detection in aerial images // Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE: 3974 - 3983 [ DOI: 10.1109/cvpr.2018.00418 http://dx.doi.org/10.1109/cvpr.2018.00418 ]

Xiao Z F , Liu Q , Tang G F and Zhai X F . 2015 . Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images . International Journal of Remote Sensing , 36 ( 2 ): 618 - 644 [ DOI: 10.1080/01431161.2014.999881 http://dx.doi.org/10.1080/01431161.2014.999881 ]

Xie X X , Cheng G , Wang J B , Yao X W and Han J W . 2021 . Oriented R-CNN for object detection // Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision . Montreal : IEEE: 3500 - 3509 [ DOI: 10.1109/iccv48922.2021.00350 http://dx.doi.org/10.1109/iccv48922.2021.00350 ]

Xu C , Ding J , Wang J W , Yang W , Yu H , Yu L and Xia G S . 2023 . Dynamic coarse-to-fine learning for oriented tiny object detection // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver : IEEE: 7318 - 7328 [ DOI: 10.1109/CVPR52729.2023.00707 http://dx.doi.org/10.1109/CVPR52729.2023.00707 ]

Xu C , Wang J W , Yang W , Yu H , Yu L and Xia G S . 2022a . Detecting tiny objects in aerial images: a normalized Wasserstein distance and a new benchmark . ISPRS Journal of Photogrammetry and Remote Sensing , 190 : 79 - 93 [ DOI: 10.1016/j.isprsjprs.2022.06.002 http://dx.doi.org/10.1016/j.isprsjprs.2022.06.002 ]

Xu C , Wang J W , Yang W , Yu H , Yu L and Xia G S . 2022b . RFLA: gaussian receptive field based label assignment for tiny object detection // 17th European Conference on Computer Vision . Tel Aviv : Springer: 526 - 543 [ DOI: 10.1007/978-3-031-20077-9_31 http://dx.doi.org/10.1007/978-3-031-20077-9_31 ]

Xu C , Wang J W , Yang W and Yu L . 2021 . Dot distance for tiny object detection in aerial images // Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Nashville : IEEE: 1192 - 1201 [ DOI: 10.1109/cvprw53098.2021.00130 http://dx.doi.org/10.1109/cvprw53098.2021.00130 ]

Yang M Y , Liao W T , Li X B and Rosenhahn B . 2018 . Deep learning for vehicle detection in aerial images // 2018 25th IEEE International Conference on Image Processing (ICIP) . Athens : IEEE: 3079 - 3083 [ DOI: 10.1109/ICIP.2018.8451454 http://dx.doi.org/10.1109/ICIP.2018.8451454 ]

Yang X , Yan J C , Feng Z M and He T . 2021a . R3Det: refined single-stage detector with feature refinement for rotating object // Proceedings of the 35th AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press: 3163 - 3171 [ DOI: 10.1609/aaai.v35i4.16426 http://dx.doi.org/10.1609/aaai.v35i4.16426 ]

Yang X , Yan J C , Ming Q , Wang W T , Zhang X P and Tian Q . 2021b . Rethinking rotated object detection with gaussian wasserstein distance loss // Proceedings of the 38th International Conference on Machine Learning . Virtual Event : [s.n.]: 11830 - 11841

Yang X , Zhou Y , Zhang G F , Yang J R , Wang W T , Yan J C , Zhang X P and Tian Q . 2023 . The KFIoU loss for rotated object detection. arXiv:2201 . 12558

Yao Y Q , Cheng G , Xie X X and Han J W . 2021 . Optical remote sensing image object detection based on multi-resolution feature fusion . National Remote Sensing Bulletin , 25 ( 5 ): 1124 - 1137

姚艳清 , 程塨 , 谢星星 , 韩军伟 . 2021 . 多分辨率特征融合的光学遥感图像目标检测 . 遥感学报 , 25 ( 5 ): 1124 - 1137 [ DOI: 10.11834/jrs.20210505 http://dx.doi.org/10.11834/jrs.20210505 ]

Ye Y X , Ren X Y , Zhu B , Tang T F , Tan X , Gui Y and Yao Q . 2022 . An adaptive attention fusion mechanism convolutional network for object detection in remote sensing images . Remote Sensing , 14 ( 3 ): 516 [ DOI: 10.3390/rs14030516 http://dx.doi.org/10.3390/rs14030516 ]

Yu W Q , Cheng G , Wang M J , Yao Y Q , Xie X X , Yao X W and Han J W . 2022 . MAR20: a benchmark for military aircraft recognition in remote sensing images . National Remote Sensing Bulletin : 1 - 11

禹文奇 , 程塨 , 王美君 , 姚艳清 , 谢星星 , 姚西文 , 韩军伟 . 2022 . MAR20: 遥感图像军用飞机目标识别数据集 . 遥感学报 : 1 - 11 [ DOI: 10.11834/jrs.20222139 http://dx.doi.org/10.11834/jrs.20222139 ]

Yu X H , Gong Y Q , Jiang N , Ye Q X and Han Z J . 2020 . Scale match for tiny person detection // Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision . Snowmass : IEEE: 1246 - 1254 [ DOI: 10.1109/WACV45572.2020.9093394 http://dx.doi.org/10.1109/WACV45572.2020.9093394 ]

Zhang Y L , Yuan Y , Feng Y C and Lu X Q . 2019 . Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection . IEEE Transactions on Geoscience and Remote Sensing , 57 ( 8 ): 5535 - 5548 [ DOI: 10.1109/tgrs.2019.2900302 http://dx.doi.org/10.1109/tgrs.2019.2900302 ]

Zhong Y F , Han X B and Zhang L P . 2018 . Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery . ISPRS Journal of Photogrammetry and Remote Sensing , 138 : 281 - 294 [ DOI: 10.1016/j.isprsjprs.2018.02.014 http://dx.doi.org/10.1016/j.isprsjprs.2018.02.014 ]

Zhou P C , Cheng G , Yao X W and Han J W . 2021 . Machine learning paradigms in high-resolution remote sensing image interpretation . National Remote Sensing Bulletin , 25 ( 1 ): 182 - 197

周培诚 , 程塨 , 姚西文 , 韩军伟 . 2021 . 高分辨率遥感影像解译中的机器学习范式 . 遥感学报 , 25 ( 1 ): 182 - 197 [ DOI: 10.11834/jrs.20210164 http://dx.doi.org/10.11834/jrs.20210164 ]

Zhu H G , Chen X G , Dai W Q , Fu K , Ye Q X and Jiao J B . 2015 . Orientation robust object detection in aerial images using deep convolutional neural network // 2015 IEEE International Conference on Image Processing (ICIP) . Quebec City : IEEE: 3735 - 3739 [ DOI: 10.1109/ICIP.2015.7351502 http://dx.doi.org/10.1109/ICIP.2015.7351502 ]

Zhu P F , Wen L Y , Du D W , Bian X , Fan H , Hu Q H and Ling H B . 2022 . Detection and tracking meet drones challenge . IEEE Transactions on Pattern Analysis and Machine Intelligence , 44 ( 11 ): 7380 - 7399 [ DOI: 10.1109/TPAMI.2021.3119563 http://dx.doi.org/10.1109/TPAMI.2021.3119563 ]

Zhuang S , Wang P , Jiang B R , Wang G and Wang C . 2019 . A single shot framework with multi-scale feature fusion for geospatial object detection . Remote Sensing , 11 ( 5 ): 594 [ DOI: 10.3390/rs11050594 http://dx.doi.org/10.3390/rs11050594 ]

Zou Z X and Shi Z W . 2018 . Random access memories: s new paradigm for target detection in high resolution aerial remote sensing images . IEEE Transactions on Image Processing , 27 ( 3 ): 1100 - 1111 [ DOI: 10.1109/TIP.2017.2773199 http://dx.doi.org/10.1109/TIP.2017.2773199 ]

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