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Received：11 November 2022，
Revised：2023-02-16，
Published：16 September 2023
移动端阅览
航空遥感图像目标检测旨在定位和识别遥感图像中感兴趣的目标，是航空遥感图像智能解译的关键技术，在情报侦察、灾害救援和资源勘探等领域具有重要应用价值。然而由于航空遥感图像具有尺寸大、目标小且密集、目标呈任意角度分布、目标易被遮挡、目标类别不均衡以及背景复杂等诸多特点，航空遥感图像目标检测目前仍然是极具挑战的任务。基于深度卷积神经网络的航空遥感图像目标检测方法因具有精度高、处理速度快等优点，受到了越来越多的关注。为推进基于深度学习的航空遥感图像目标检测技术的发展，本文对当前主流遥感图像目标检测方法，特别是2020—2022年提出的检测方法，进行了系统梳理和总结。首先梳理了基于深度学习目标检测方法的研究发展演化过程，然后对基于卷积神经网络和基于Transformer目标检测方法中的代表性算法进行分析总结，再后针对不同遥感图象应用场景的改进方法思路进行归纳，分析了典型算法的思路和特点，介绍了现有的公开航空遥感图像目标检测数据集，给出了典型算法的实验比较结果，最后给出现阶段航空遥感图像目标检测研究中所存在的问题，并对未来研究及发展趋势进行了展望。
Given the successful development of aerospace technology， high-resolution remote-sensing images have been used in daily research. The earlier low-resolution images limit researchers’ interpretation of image information. In comparison， today’s high-resolution remote sensing images contain rich geographic and entity detail features. They are also rich in spatial structure and semantic information. Thus， they can greatly promote the development of research in this field. Aerial remote sensing image object detection aims to provide the category and location of the target of interest in aerial remote sensing images and present evidence for further information interpretation reasoning. This technology is crucial for aerial remote sensing image interpretation and has important applications in intelligence reconnaissance， target surveillance， and disaster rescue. The early remote sensing image object detection task mainly relies on manual interpretation. The interpretation results are greatly affected by subjective factors， such as the experience and energy of the interpreters. Moreover， the timeliness is low. Various remote sensing image object detection methods based on machine learning technology have been proposed with the progress and development of machine learning technology. Traditional machine learning-based object detection techniques generally use manually designed models to extract feature information， such as feature spectrum， gray value， texture， and shape of remote sensing images， after generating sliding windows. Then， they feed the extracted feature information into classifiers， such as support vector machine （SVM） and adaptive boosting （AdaBoost）， to achieve object detection in remote sensing images. These methods design the corresponding feature extraction models for specific targets with strong interpretability but weak feature expression capability， poor generalization， time-consuming computation， and low accuracy. These features make meeting the needs of accurate and efficient object detection tasks challenging in complex and variable application scenarios. In recent years， the research on the application of deep learning in remote sensing image processing has received considerable attention and become a hotspot because of the wide application of deep learning techniques， such as deep convolutional neural networks and generative adversarial neural networks， in the fields of natural image object detection， classification， and recognition， and the excellent performance in the task of large-scale natural scene image object detection. Thus， many excellent works have emerged. Object detection in aerial remote sensing images mainly faces challenges， such as large-size and high-resolution images， interference from complex backgrounds， target direction diversity， dense targets， dramatic scale changes， and small targets. At present， these challenges have corresponding model improvement methods. For large-scale natural scene image object detection， high-resolution aerial remote sensing images are used because the target scale in the image is widely distributed. This approach ensures the integrity of small target detail information. Thus， the most commonly used detection and recognition method involves segmenting the image during data preprocessing； that is， the large image is segmented into regular image sizes and sent to the object detection algorithm for detection and recognition in turn. In the subsequent processing， all the detection results are finally stitched together and reset to complete the detection of the whole image. Moreover， the aerial remote sensing image with the ultrahigh resolution has a complex background. The target to be detected is easily interfered with by various similar objects， and the similar targets to be detected present different characteristics. Thus， false detection quickly occurs during detection. Therefore， the usual methods for solving complex background interference can be divided into two types： extracting the contextual information in the image and improving the attention mechanism. The targets to be detected in the images for the complex multidirectional and multitarget situations are multidirectional because the aerial remote sensing images are all top-down images. Moreover， the aspect ratio range of the targets to be detected is more diverse than that of the targets in the natural images. Thus， the interference between the targets is serious， thereby affecting the accuracy of the final target localization and classification. At present， three practical improvement ideas are available for the problems of directional diversity and dense arrangement distribution of targets to be detected： image rotation enhancement， design of rotation invariant module， and design of an accurate position regression method. The designed model needs to have good scale invariance， i.e.， the model has high recognition ability even under the drastic changes of multiple scales of multiple targets， to meet the challenge of drastic changes in the target scales in aerial remote sensing images. Thus， the common improvement scheme is the multiscale feature fusion. For the small target detection in aerial remote sensing images， the current algorithms are mainly improved from feature enhancement， multilevel feature map detection， and the design of precise positioning strategies. In summary， the challenges and difficulties of object detection in aerial remote sensing imagery do not exist independently. For example， the large size and high resolution of aerial remote sensing images inevitably lead to a complex background in the images and a sharp increase in the category and number of small targets to be detected. Moreover， most of the small targets are susceptible to strong interference from the complex background. This phenomenon results in localization and classification recognition accuracy. In addition， the improvements for one challenge also apply to other difficulties， e.g.， the improvements for multiscale target feature enhancement benefit almost all challenges. Therefore， the problems in the field must be analyzed and improved from a global perspective. Based on the full study of the latest reviews and related research works， this study systematically compares and summarizes deep learning object detection algorithms for aerial remote sensing images， particularly the research methods at home and abroad in the past three years， to provide appropriate object detection research for aerial remote sensing images and help scholars comprehensively understand and grasp the latest progress in aerial remote sensing image object detection research based on deep learning. First， the present study introduces the deep-learning-based image object detection model. Then， it systematically composes the deep-learning-based aerial remote sensing image detection methods， introduces the publicly available datasets for aerial remote sensing image object detection， and compares the performances of typical methods through experiments. Finally， the problems in the current research of aerial remote sensing image object detection are presented， and future research and development trends are prospected.
Amit R A and Mohan C K . 2021 . A robust airport runway detection network based on R-CNN using remote sensing images . IEEE Aerospace and Electronic Systems Magazine ， 36 （ 11 ）： 4 - 20 ［ DOI： 10.1109/MAES.2021.3088477 http://dx.doi.org/10.1109/MAES.2021.3088477 ］
Bochkovskiy A ， Wang C Y and Liao H Y M . 2020 . YOLOv4： optimal speed and accuracy of object detection ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2004.10934.pdf http://arxiv.org/pdf/2004.10934.pdf ［ DOI： 10.48550/arXiv.2004.10934 http://dx.doi.org/10.48550/arXiv.2004.10934 ］
Boroughani M ， Pourhashemi S ， Hashemi H ， Salehi M ， Amirahmadi A ， Asadi M A Z and Berndtsson R . 2020 . Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping . Ecological Informatics ， 56 ： # 101059 ［ DOI： 10.1016/j.ecoinf.2020.101059 http://dx.doi.org/10.1016/j.ecoinf.2020.101059 ］
Cai Z W and Vasconcelos N . 2018 . Cascade R-CNN： delving into high quality object detection // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 6154 - 6162 ［ DOI： 10.1109/cvpr.2018.00644 http://dx.doi.org/10.1109/cvpr.2018.00644 ］
Carion N ， Massa F ， Synnaeve G ， Usunier N ， Kirillov A and Zagoruyko S . 2020 . End-to-end object detection with Transformers // Proceedings of the 16th European Conference on Computer Vision . Glasgow， UK ： 213 - 229 ［ DOI： 10.1007/978-3-030-58452-8_13 http://dx.doi.org/10.1007/978-3-030-58452-8_13 ］
Chalavadi V ， Jeripothula P ， Datla R ， Ch S B C K M . 2022 . mSODANet： a network for multi-scale object detection in aerial images using hierarchical dilated convolutions . Pattern Recognition ， 126 ： # 108548 ［ DOI： 10.1016/j.patcog.2022.108548 http://dx.doi.org/10.1016/j.patcog.2022.108548 ］
Chen Q ， Wang Y M ， Yang T ， Zhang X Y ， Cheng J and Sun J . 2021 . You only look one-level feature // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 13034 - 13043 ［ DOI： 10.1109/cvpr46437.2021.01284 http://dx.doi.org/10.1109/cvpr46437.2021.01284 ］
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 ， Zhou P C and Han J W . 2016 . 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 ］
Cooner A J ， Shao Y and Campbell J B . 2016 . Detection of urban damage using remote sensing and machine learning algorithms： revisiting the 2010 Haiti earthquake . Remote Sensing ， 8 （ 10 ）： # 868 ［ DOI： 10.3390/rs8100868 http://dx.doi.org/10.3390/rs8100868 ］
Cortes C and Vapnik V . 1995 . Support-vector networks . Machine Learning ， 20 （ 3 ）： 273 - 297 ［ DOI： 10.1007/BF00994018 http://dx.doi.org/10.1007/BF00994018 ］
Dai J F ， Qi H Z ， Xiong Y W ， Li Y ， Zhang G D ， Hu H and Wei Y C . 2017 . Deformable convolutional networks // Proceedings of 2017 IEEE International Conference on Computer Vision . Venice， Italy ： IEEE： 764 - 773 ［ DOI： 10.1109/ICCV.2017.89 http://dx.doi.org/10.1109/ICCV.2017.89 ］
Dai K ， Xu L B ， Huang S Y and Li Y L . 2022 . Single stage object detection algorithm based on fusing strategy optimization selection and dual attention mechanism . Journal of Image and Graphics ， 27 （ 8 ）： 2430 - 2443
戴坤 ， 许立波 ， 黄世旸 ， 李鋆铃 . 2022 . 融合策略优选和双注意力的单阶段目标检测 . 中国图象图形学报 ， 27 （ 8 ）： 2430 - 2443 ［ DOI： 10.11834/jig.210204 http://dx.doi.org/10.11834/jig.210204 ］
Dai L H ， Liu H ， Tang H ， Wu Z W and Song P H . 2022a . AO2-DETR： arbitrary-oriented object detection Transformer ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2205.12785.pdf http://arxiv.org/pdf/2205.12785.pdf
Dai P W ， Yao S Y ， Li Z K ， Zhang S Y and Cao X C . 2022b . ACE： anchor-free corner evolution for real-time arbitrarily-oriented object detection . IEEE Transactions on Image Processing ， 31 ： 4076 - 4089 ［ DOI： 10.1109/TIP.2022.3167919 http://dx.doi.org/10.1109/TIP.2022.3167919 ］
Dai Y N ， Yu J Y ， Zhang D A ， Hu T H and Zheng X T . 2022c . RODFormer： high-precision design for rotating object detection with Transformers . Sensors ， 22 （ 7 ）： # 2633 ［ DOI： 10.3390/s22072633 http://dx.doi.org/10.3390/s22072633 ］
Dai Z G ， Cai B L ， Lin Y G and Chen J Y . 2021 . UP-DETR： unsupervised pre-training for object detection with Transformers // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 1601 - 1610 ［ DOI： 10.1109/cvpr46437.2021.00165 http://dx.doi.org/10.1109/cvpr46437.2021.00165 ］
Dalal N and Triggs B . 2005 . Histograms of oriented gradients for human detection // Proceedings of 2005 IEEE Conference on Computer Vision and Pattern Recognition . San Diego， USA ： IEEE： 886 - 893 ［ DOI： 10.1109/CVPR.2005.177 http://dx.doi.org/10.1109/CVPR.2005.177 ］
Deng S T ， Li S ， Xie K ， Song W F ， Liao X ， Hao A M and Qin H . 2021 . A global-local self-adaptive network for drone-view object detection . IEEE Transactions on Image Processing ， 30 ： 1556 - 1569 ［ DOI： 10.1109/TIP.2020.3045636 http://dx.doi.org/10.1109/TIP.2020.3045636 ］
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 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 2844 - 2853 ［ DOI： 10.1109/cvpr.2019.00296 http://dx.doi.org/10.1109/cvpr.2019.00296 ］
Ding X H ， Zhang X Y ， Han JG and Ding G G . 2022 . Scaling up your kernels to 31 × 31 ： revisiting large kernel design in CNNs //Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA ： IEEE： 11953 - 11965 ［ DOI： 10.1109/cvpr52688.2022.01166 http://dx.doi.org/10.1109/cvpr52688.2022.01166 ］
Ding X H ， Zhang X Y ， Ma N N ， Han J G ， Ding G G and Sun J . 2021 . RepVGG： making VGG-style ConvNets great again // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 13728 - 13737 ［ DOI： 10.1109/CVPR46437.2021.01352 http://dx.doi.org/10.1109/CVPR46437.2021.01352 ］
Dong X H ， Qin Y ， Fu R G ， Gao Y H ， Liu S L ， Ye Y X and Li B . 2022 . Multiscale deformable attention and multilevel features aggregation for remote sensing object detection . IEEE Geoscience and Remote Sensing Letters ， 19 ： # 6510405 ［ DOI： 10.1109/LGRS.2022.3178479 http://dx.doi.org/10.1109/LGRS.2022.3178479 ］
Dong Z P ， Wang M ， Wang Y L ， Zhu Y and Zhang Z Q . 2020 . Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features . IEEE Transactions on Geoscience and Remote Sensing ， 58 （ 3 ）： 2104 - 2114 ［ DOI： 10.1109/TGRS.2019.2953119 http://dx.doi.org/10.1109/TGRS.2019.2953119 ］
Du D W ， Qi Y K ， Yu H Y ， Yang Y F ， Duan K W ， Li G R ， Zhang W G ， Huang Q M and Tian Q . 2018 . The unmanned aerial vehicle benchmark： object detection and tracking // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 375 - 391 ［ DOI： 10.1007/978-3-030-01249-6_23 http://dx.doi.org/10.1007/978-3-030-01249-6_23 ］
Du D W， Zhu P F， Wen L Y， Bian X， Lin H B， Hu Q H， Peng T， Zheng J Y， Wang X Y， Zhang Y， Bo L F， Shi H L， Zhu R， Kumar A， Li A J， Zinollayev A， Askergaliyev A， Schumann A， Mao B J， Lee B， Liu C， Chen C R， Pan C H， Huo C L， Yu D， Cong D C， Zeng D N， Pailla D R， Li D， Wang D， Cho D， Zhang D Y， Bai F R， Jose G， Gao G Y， Liu G Z， Xiong H T， Qi H， Wang H R， Qiu H Q， Li H L， Lu H C， Kim I， Kim J， Shen J， Lee J， Ge J， Xu J J， Zhou J K， Meier J， Choi J W， Hu J H， Zhang J Y， Huang J Y， Huang K Q， Wang K Y， Sommer L， Jin L， Zhang L， Huang L H， Sun L， Steinmann L， Jia M X， Xu N， Zhang P Y， Chen Q， Lyu Q X， Liu Q， Cheng Q S， Chennamsetty S S， Chen S H， Wei S， Kruthiventi S S S， Hong S， Kang S， Wu T， Feng T， Kollerathu V A， Li W Q， Dai W， Qin W D， Wang W Y， Wang X R， Chen X Y， Chen X， Sun X， Zhang X， Zhao X， Zhang X D， Zhang X Y， Chen X K， Wei X D， Zhang X Z， Li Y C， Chen Y F， Toh Y H， Zhang Y， Zhu Y， Zhong Y X， Wang Z X， Wang Z K， Song Z C and Liu Z M . 2019 . VisDrone-DET2019： the vision meets drone object detection in image challenge results //Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South） ： IEEE： 213 - 223 ［ DOI： 10.1109/iccvw.2019.00030 http://dx.doi.org/10.1109/iccvw.2019.00030 ］
Duan C Z ， Wei Z W ， Zhang C ， Qu S Y and Wang H P . 2021 . Coarse-grained density map guided object detection in aerial images // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops . Montreal， Canada ： IEEE： 2789 - 2798 ［ DOI： 10.1109/iccvw54120.2021.00313 http://dx.doi.org/10.1109/iccvw54120.2021.00313 ］
Fang Y ， Liao B ， Wang X ， Fang J ， Qi J ， Wu R ， Niu J and Liu W . 2021 . You only look at one sequence： rethinking Transformer in vision through object detection // Advances in Neural Information Processing Systems ， 34 ， 26183 - 26197 ［ DOI： 10.48550/arXiv.2106.00666 http://dx.doi.org/10.48550/arXiv.2106.00666 ］
Felzenszwalb P F ， Girshick R B ， McAllester D and Ramanan D . 2010 . Object detection with discriminatively trained part-based models . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 32 （ 9 ）： 1627 - 1645 ［ DOI： 10.1109/TPAMI.2009.167 http://dx.doi.org/10.1109/TPAMI.2009.167 ］
Fu H ， Fan X T ， Yan Z Z and Du X P . 2022 . Progress of object detection in remote sensing images based on deep learning . Remote Sensing Technology and Application ， 37 （ 2 ）： 290 - 305
付涵 ， 范湘涛 ， 严珍珍 ， 杜小平 . 2022 . 基于深度学习的遥感图像目标检测技术研究进展 . 遥感技术与应用 ， 37 （ 2 ）： 290 - 305 ［ DOI： 10.11873/j.issn.1004-0323.2022.2.0290 http://dx.doi.org/10.11873/j.issn.1004-0323.2022.2.0290 ］
Fu J ， Liu J ， Tian H J ， Li Y ， Bao Y J ， Fang Z W and Lu H Q . 2019 . Dual attention network for scene segmentation // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 3141 - 3149 ［ DOI： 10.1109/cvpr.2019.00326 http://dx.doi.org/10.1109/cvpr.2019.00326 ］
Fu J M ， Sun X ， Wang Z R and Fu K . 2021 . An anchor-free method based on feature balancing and refinement network for multiscale ship detection in SAR images . IEEE Transactions on Geoscience and Remote Sensing ， 59 （ 2 ）： 1331 - 1344 ［ DOI： 10.1109/TGRS.2020.3005151 http://dx.doi.org/10.1109/TGRS.2020.3005151 ］
Ge Z ， Liu S T ， Wang F ， Li Z M and Sun J . 2021 . YOLOX： exceeding YOLO series in 2021 ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2107.08430.pdf http://arxiv.org/pdf/2107.08430.pdf
Gevorgyan Z . 2022 . SIoU loss： more powerful learning for bounding box regression ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2205.12740.pdf http://arxiv.org/pdf/2205.12740.pdf
Ghaffarian S ， Valente J ， Van Der Voort M and Tekinerdogan B . 2021 . Effect of attention mechanism in deep learning-based remote sensing image processing： a systematic literature review . Remote Sensing ， 13 （ 15 ）： # 2965 ［ DOI： 10.3390/rs13152965 http://dx.doi.org/10.3390/rs13152965 ］
Ghasemian N and Akhoondzadeh M . 2018 . Introducing two Random Forest based methods for cloud detection in remote sensing images . Advances in Space Research ， 62 （ 2 ）： 288 - 303 ［ DOI： 10.1016/j.asr.2018.04.030 http://dx.doi.org/10.1016/j.asr.2018.04.030 ］
Girshick R . 2015 . Fast R-CNN // Proceedings of 2015 IEEE International Conference on Computer Vision . Santiago， Chile ： IEEE： 1440 - 1448 ［ DOI： 10.1109/iccv.2015.169 http://dx.doi.org/10.1109/iccv.2015.169 ］
Girshick R ， Donahue J ， Darrell T and Malik J . 2014 . Rich feature hierarchies for accurate object detection and semantic segmentation // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Columbus， USA ： 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 . 2022 . 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 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 2785 - 2794 ［ DOI： 10.1109/cvpr46437.2021.00281 http://dx.doi.org/10.1109/cvpr46437.2021.00281 ］
He K M ， Gkioxari G ， Dollr P and Girshick R . 2017 . Mask R-CNN // Proceedings of 2017 IEEE International Conference on Computer Vision . Venice， Italy ： IEEE： 2980 - 2988 ［ DOI： 10.1109/iccv.2017.322 http://dx.doi.org/10.1109/iccv.2017.322 ］
He Y Q ， Sun X ， Gao L R and Zhang B . 2018 . Ship detection without sea-land segmentation for large-scale high-resolution optical satellite images // IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia， Spain ： IEEE： 717 - 720 ［ DOI： 10.1109/IGARSS.2018.8519391 http://dx.doi.org/10.1109/IGARSS.2018.8519391 ］
Hou B ， Ren Z L ， Zhao W ， Wu Q and Jiao L C . 2020 . Object detection in high-resolution panchromatic images using deep models and spatial template matching . IEEE Transactions on Geoscience and Remote Sensing ， 58 （ 2 ）： 956 - 970 ［ DOI： 10.1109/TGRS.2019.2942103 http://dx.doi.org/10.1109/TGRS.2019.2942103 ］
Hu J ， Shen L and Sun G . 2018 . Squeeze-and-excitation networks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 7132 - 7141 ［ DOI： 10.1109/cvpr.2018.00745 http://dx.doi.org/10.1109/cvpr.2018.00745 ］
Hua X ， Wang X Q ， Rui T ， Zhang H T and Wang D . 2020 . A fast self-attention cascaded network for object detection in large scene remote sensing images . Applied Soft Computing ， 94 ： # 106495 ［ DOI： 10.1016/j.asoc.2020.106495 http://dx.doi.org/10.1016/j.asoc.2020.106495 ］
Hussain M ， Chen D M ， Cheng A ， Wei H and Stanley D . 2013 . Change detection from remotely sensed images： from pixel-based to object-based approaches . ISPRS Journal of Photogrammetry and Remote Sensing ， 80 ： 91 - 106 ［ DOI： 10.1016/j.isprsjprs.2013.03.006 http://dx.doi.org/10.1016/j.isprsjprs.2013.03.006 ］
Inglada J . 2007 . Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features . ISPRS Journal of Photogrammetry and Remote Sensing ， 62 （ 3 ）： 236 - 248 ［ DOI： 10.1016/j.isprsjprs.2007.05.011 http://dx.doi.org/10.1016/j.isprsjprs.2007.05.011 ］
Jaderberg M ， Simonyan K ， Zisserman A and Kavukcuoglu K . 2015 . Spatial Transformer networks // Proceedings of the 28th International Conference on Neural Information Processing Systems . Montreal， Canada ： MIT Press： 2017 - 2025
Jia K X ， Ma Z H ， Zhu R and Li Y G . 2022 . Attention-mechanism-based light single shot multiBox detector modelling improvement for small object detection on the sea surface . Journal of Image and Graphics ， 27 （ 4 ）： 1161 - 1175
贾可心 ， 马正华 ， 朱蓉 ， 李永刚 . 2022 . 注意力机制改进轻量SSD模型的海面小目标检测 . 中国图象图形学报 ， 27 （ 4 ）： 1161 - 1175 ［ DOI： 10.11834/jig.200517 http://dx.doi.org/10.11834/jig.200517 ］
Jiang H ， Zhang Y T ， Guo J Y ， Zhao X ， Li F F ， Huang L J ， Hu Y X ， Lei B and Ding C B . 2021 . Accurate localization and parameter extraction of oil tank in remote sensing images . Journal of Image and Graphics ， 26 （ 12 ）： 2953 - 2963
江晗 ， 张月婷 ， 郭嘉逸 ， 赵鑫 ， 李芳芳 ， 黄丽佳 ， 胡玉新 ， 雷斌 ， 丁赤飚 . 2021 . 遥感图像中油罐目标精确定位与参数提取 . 中国图象图形学报 ， 26 （ 12 ）： 2953 - 2963 ［ DOI： 10.11834/jig.200604 http://dx.doi.org/10.11834/jig.200604 ］
Jocher Glenn . 2020 . YOLOv 5 release v 6 . 2 ［EB/OL］. ［ 2023-01-19 ］. https://github.com/ultralytics/yolov5/releases/tag/v6.1 https://github.com/ultralytics/yolov5/releases/tag/v6.1
Kattenborn T ， Leitloff J ， Schiefer F and Hinz S . 2021 . Review on convolutional neural networks （CNN） in vegetation remote sensing . ISPRS Journal of Photogrammetry and Remote Sensing ， 173 ： 24 - 49 ［ DOI： 10.1016/j.isprsjprs.2020.12.010 http://dx.doi.org/10.1016/j.isprsjprs.2020.12.010 ］
Li C Y ， Li L L ， Jiang H L ， Weng K H ， Geng Y F ， Li L ， Ke Z D ， Li Q Y ， Cheng M ， Nie W Q ， Li Y D ， Zhang B ， Liang Y F ， Zhou L Y ， Xu X M ， Chu X X ， Wei X M and Wei X L . 2022a . YOLOv6： a single-stage object detection framework for industrial applications ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2209.02976.pdf http://arxiv.org/pdf/2209.02976.pdf
Li C L ， Yang T J N ， Zhu S J ， Chen C and Guan S Y . 2020b . Density map guided object detection in aerial images // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Seattle， USA ： IEEE： 737 - 746 ［ DOI： 10.1109/cvprw50498.2020.00103 http://dx.doi.org/10.1109/cvprw50498.2020.00103 ］
Li F ， Zhang H ， Liu S L ， Guo J ， Ni L M and Zhang L . 2022b . DN-DETR： accelerate DETR training by introducing query denoising // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 13609 - 13617 ［ DOI： 10.1109/cvpr52688.2022.01325 http://dx.doi.org/10.1109/cvpr52688.2022.01325 ］
Li J X ， Tian Y ， Xu Y P and Zhang Z L . 2022c . Oriented object detection in remote sensing images with anchor-free oriented region proposal network . Remote Sensing ， 14 （ 5 ）： # 1246 ［ DOI： 10.3390/rs14051246 http://dx.doi.org/10.3390/rs14051246 ］
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 M J ， Guo W W ， Zhang Z H ， Yu W X and Zhang T . 2018a . Rotated region based fully convolutional network for ship detection // IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium . Valencia， Spain ： IEEE： 673 - 676 ［ DOI： 10.1109/IGARSS.2018.8519094 http://dx.doi.org/10.1109/IGARSS.2018.8519094 ］
Li Q Y ， Chen Y S and Zeng Y . 2022d . Transformer with transfer CNN for remote-sensing-image object detection . Remote Sensing ， 14 （ 4 ）： # 984 ［ DOI： 10.3390/rs14040984 http://dx.doi.org/10.3390/rs14040984 ］
Li Q P ， Mou L C ， Liu Q J ， Wang Y H and Zhu X X . 2018b . HSF-Net： multiscale deep feature embedding for ship detection in optical remote sensing imagery . IEEE Transactions on Geoscience and Remote Sensing ， 56 （ 12 ）： 7147 - 7161 ［ DOI： 10.1109/TGRS.2018.2848901 http://dx.doi.org/10.1109/TGRS.2018.2848901 ］
Li W T ， Chen Y J ， Hu K X and Zhu J K . 2022e . Oriented RepPoints for aerial object detection // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans， USA ： IEEE： 1819 - 1828 ［ DOI： 10.1109/cvpr52688.2022.00187 http://dx.doi.org/10.1109/cvpr52688.2022.00187 ］
Li W J ， Dong R M ， Fu H H and Yu L . 2019 . Large-scale oil palm tree detection from high-resolution satellite images using two-stage convolutional neural networks . Remote Sensing ， 11 （ 1 ）： # 11 ［ DOI： 10.3390/rs11010011 http://dx.doi.org/10.3390/rs11010011 ］
Li Y S ， Zhang Y J ， Huang X and Yuille A L . 2018c . Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images . ISPRS Journal of Photogrammetry and Remote Sensing ， 146 ： 182 - 196 ［ DOI： 10.1016/j.isprsjprs.2018.09.014 http://dx.doi.org/10.1016/j.isprsjprs.2018.09.014 ］
Li Y Y ， Huang Q ， Pei X ， Chen Y Q ， Jiao L C and Shang R H . 2021 . Cross-layer attention network for small object detection in remote sensing imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 14 ： 2148 - 2161 ［ DOI： 10.1109/JSTARS.2020.3046482 http://dx.doi.org/10.1109/JSTARS.2020.3046482 ］
Li Y Y ， Huang Q ， Pei X ， Jiao L C and Shang R H . 2020c . RADet： refine feature pyramid network and multi-layer attention network for arbitrary-oriented object detection of remote sensing images . Remote Sensing ， 12 （ 3 ）： # 389 ［ DOI： 10.3390/rs12030389 http://dx.doi.org/10.3390/rs12030389 ］
Liao J J ， Piao Y ， Su J H ， Cai G R ， Huang X W ， Chen L ， Huang Z H and Wu Y D . 2021 . Unsupervised cluster guided object detection in aerial images . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 14 ： 11204 - 11216 ［ DOI： 10.1109/JSTARS.2021.3122152 http://dx.doi.org/10.1109/JSTARS.2021.3122152 ］
Liao Y R ， Wang H N ， Lin C B ， Li Y ， Fang Y Q and Ni S Y . 2022 . Research progress of deep learning-based object detection of optical remote sensing image . Journal on Communications ， 43 （ 5 ）： 190 - 203
廖育荣 ， 王海宁 ， 林存宝 ， 李阳 ， 方宇强 ， 倪淑燕 . 2022 . 基于深度学习的光学遥感图像目标检测研究进展 . 通信学报 ， 43 （ 5 ）： 190 - 203 ［ DOI： 10.11959/j.issn.1000-436x.2022071 http://dx.doi.org/10.11959/j.issn.1000-436x.2022071 ］
Lin T Y ， Goyal P ， Girshick R ， He K M and Dollr P . 2017 . Focal loss for dense object detection // Proceedings of 2017 IEEE/CVF International Conference on Computer Vision . Venice， Italy ： IEEE： 2999 - 3007 ［ DOI： 10.1109/iccv.2017.324 http://dx.doi.org/10.1109/iccv.2017.324 ］
Liu G ， Zhang Y S ， Zheng X W ， Sun X ， Fu K and Wang H Q . 2014 . A new method on inshore ship detection in high-resolution satellite images using shape and context information . IEEE Geoscience and Remote Sensing Letters ， 11 （ 3 ）： 617 - 621 ［ DOI： 10.1109/LGRS.2013.2272492 http://dx.doi.org/10.1109/LGRS.2013.2272492 ］
Liu J H ， Yang D H and Hu F . 2022a . Multiscale object detection in remote sensing images combined with multi-receptive-field features and relation-connected attention . Remote Sensing ， 14 （ 2 ）： # 427 ［ DOI： 10.3390/rs14020427 http://dx.doi.org/10.3390/rs14020427 ］
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 S ， Zhang L ， Lu H C and He Y . 2022b . Center-boundary dual attention for oriented object detection in remote sensing images . IEEE Transactions on Geoscience and Remote Sensing ， 60 ： # 5603914 ［ DOI： 10.1109/TGRS.2021.3069056 http://dx.doi.org/10.1109/TGRS.2021.3069056 ］
Liu T L ， Luo R H ， Xu L Q ， Feng D C ， Cao L ， Liu S Y and Guo J J . 2022c . Spatial channel attention for deep convolutional neural networks . Mathematics ， 10 （ 10 ）： # 1750 ［ DOI： 10.3390/math10101750 http://dx.doi.org/10.3390/math10101750 ］
Liu W ， Anguelov D ， Erhan D ， Szegedy C ， Reed S ， Fu C Y and Berg A C . 2016 . SSD： single shot MultiBox detector // Proceedings of the 14th European Conference on Computer Vision . Amsterdam， the Netherlands ： Springer： 21 - 37 ［ DOI： 10.1007/978-3-319-46448-0_2 http://dx.doi.org/10.1007/978-3-319-46448-0_2 ］
Liu X L ， Ma S P ， He L Y ， Wang C and Chen Z . 2022d . Hybrid network model： TransConvNet for oriented object detection in remote sensing images . Remote Sensing ， 14 （ 9 ）： # 2090 ［ DOI： 10.3390/rs14092090 http://dx.doi.org/10.3390/rs14092090 ］
Liu Y ， Li H F ， Hu C ， Luo S ， Luo Y and Chen C W . 2022e . Learning to aggregate multi-scale context for instance segmentation in remote sensing images ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2111.11057.pdf http://arxiv.org/pdf/2111.11057.pdf
Liu Y ， Zhang Y ， Wang Y X ， Hou F ， Yuan J ， Tian J ， Zhang Y ， Shi Z C ， Fan J P and He Z Q . 2022f . A survey of visual Transformers ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2111.06091.pdf http://arxiv.org/pdf/2111.06091.pdf
Liu Z K ， Hu J G ， Weng L B and Yang Y P . 2017a . Rotated region based CNN for ship detection // Proceedings of 2021 IEEE International Conference on Image Processing . Beijing， China ： IEEE： 900 - 904 ［ DOI： 10.1109/ICIP.2017.8296411 http://dx.doi.org/10.1109/ICIP.2017.8296411 ］
Liu Z K ， Yuan L ， Weng L B and Yang Y P . 2017b . 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， Portugal ： SciTePress： 324 - 331 ［ DOI： 10.5220/0006120603240331 http://dx.doi.org/10.5220/0006120603240331 ］
Luo C ， Feng S S ， Yang X F ， Ye Y M ， Li X T ， Zhang B Q ， Chen Z H and Quan Y L . 2022 . LWCDnet： a lightweight network for efficient cloud detection in remote sensing images . IEEE Transactions on Geoscience and Remote Sensing ， 60 ： # 5409816 ［ DOI： 10.1109/TGRS.2022.3173661 http://dx.doi.org/10.1109/TGRS.2022.3173661 ］
Ma T ， Mao M Y ， Zheng H H ， Gao P ， Wang X D ， Han S M ， Ding E R ， Zhang B C and Doermann D . 2021 . Oriented object detection with Transformer ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2106.03146.pdf http://arxiv.org/pdf/2106.03146.pdf
Mirhajianmoghadam H and Haghighi B B . 2022 . EYNet： extended YOLO for airport detection in remote sensing images ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2203.14007.pdf http://arxiv.org/pdf/2203.14007.pdf
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 ］
Ojala T ， Pietikainen M and Maenpaa T . 2002 . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 24 （ 7 ）： 971 - 987 ［ DOI： 10.1109/TPAMI.2002.1017623 http://dx.doi.org/10.1109/TPAMI.2002.1017623 ］
Olson D and Anderson J . 2021 . Review on unmanned aerial vehicles， remote sensors， imagery processing， and their applications in agriculture . Agronomy Journal ， 113 （ 2 ）： 971 - 992 ［ DOI： 10.1002/agj2.20595 http://dx.doi.org/10.1002/agj2.20595 ］
Qin Z Q ， Zhang P Y ， Wu F and Li X . 2021 . FcaNet： frequency channel attention networks // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 783 - 792 ［ DOI： 10.1109/iccv48922.2021.00082 http://dx.doi.org/10.1109/iccv48922.2021.00082 ］
Ran Q ， Wang Q ， Zhao B Y ， Wu Y F ， Pu S L and Li Z J . 2021 . Lightweight oriented object detection using multiscale context and enhanced channel attention in remote sensing images . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 14 ： 5786 - 5795 ［ DOI： 10.1109/JSTARS.2021.3079968 http://dx.doi.org/10.1109/JSTARS.2021.3079968 ］
Redmon J ， Divvala S ， Girshick R and Farhadi A . 2016 . You only look once： unified， real-time object detection // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas， USA ： IEEE： 779 - 788 ［ DOI： 10.1109/cvpr.2016.91 http://dx.doi.org/10.1109/cvpr.2016.91 ］
Redmon J and Farhadi A . 2017 . YOLO9000： better， faster， stronger // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Honolulu， USA ： IEEE： 6517 - 6525 ［ DOI： 10.1109/cvpr.2017.690 http://dx.doi.org/10.1109/cvpr.2017.690 ］
Redmon J and Farhadi A . 2018 . YOLOv3： an incremental improvement ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/1804.02767.pdf http://arxiv.org/pdf/1804.02767.pdf
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， Canada ： MIT Press： 91 - 99
Rodríguez J J and Maudes J . 2008 . Boosting recombined weak classifiers . Pattern Recognition Letters ， 29 （ 8 ）： 1049 - 1059 ［ DOI： 10.1016/j.patrec.2007.06.019 http://dx.doi.org/10.1016/j.patrec.2007.06.019 ］
Roh B ， Shin J ， Shin W and Kim S . 2022 . Sparse DETR： efficient end-to-end object detection with learnable sparsity ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2111.14330.pdf http://arxiv.org/pdf/2111.14330.pdf
Shafique A ， Cao G ， Khan Z ， Asad M and Aslam M . 2022 . Deep learning-based change detection in remote sensing images： a review . Remote Sensing ， 14 （ 4 ）： # 871 ［ DOI： 10.3390/rs14040871 http://dx.doi.org/10.3390/rs14040871 ］
Singh I and Munjal G . 2022 . Improved Yolov5 for small target detection in aerial images . （SSRN Scholarly Paper No # 4049533 ） ［DOI： 10.2139/ssrn.4049533］
Song Z N ， Sui H and Hua L . 2021 . A hierarchical object detection method in large-scale optical remote sensing satellite imagery using saliency detection and CNN . International Journal of Remote Sensing ， 42 （ 8 ）： 2827 - 2847 ［ DOI： 10.1080/01431161.2020.1826059 http://dx.doi.org/10.1080/01431161.2020.1826059 ］
Song Z N ， Sui H G and Li Y C . 2021 . A survey on ship detection technology in high-resolution optical remote sensing images . Geomatics and Information Science of Wuhan University ， 46 （ 11 ）： 1703 - 1715
宋志娜 ， 眭海刚 ， 李永成 . 2021 . 高分辨率可见光遥感图像舰船目标检测综述 . 武汉大学学报（信息科学版） ， 46 （ 11 ）： 1703 - 1715 ［ DOI： 10.13203/j.whugis20200481 http://dx.doi.org/10.13203/j.whugis20200481 ］
Sun X ， Wang P J ， Wang C ， Liu Y F and Fu K . 2021 . PBNet： part-based convolutional neural network for complex composite object detection in remote sensing imagery . ISPRS Journal of Photogrammetry and Remote Sensing ， 173 ： 50 - 65 ［ DOI： 10.1016/j.isprsjprs.2020.12.015 http://dx.doi.org/10.1016/j.isprsjprs.2020.12.015 ］
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 . FAIR1M： 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 ］
Van Etten A . 2018 . You only look twice： rapid multi-scale object detection in satellite imagery ［EB/OL］. ［ 2023-01-19 ］. https://arxiv.org/pdf/1805.09512.pdf https://arxiv.org/pdf/1805.09512.pdf
Vaswani A ， Shazeer N ， Parmar N ， Uszkoreit J ， Jones L ， Gomez A N ， Kaiser Ł and Polosukhin I . 2017 . Attention is all you need // Proceedings of the 31st International Conference on Neural Information Processing Systems . Long Beach， USA ： Curran Associates Inc.： 6000 - 6010
Viola P and Jones M . 2001 . Rapid object detection using a boosted cascade of simple features // Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . Kauai， USA ： IEEE： I-511-I-518 ［ DOI： 10.1109/CVPR.2001.990517 http://dx.doi.org/10.1109/CVPR.2001.990517 ］
Wang C ， Bai X ， Wang S ， Zhou J and Ren P . 2019 . Multiscale visual attention networks for object detection in VHR remote sensing images . IEEE Geoscience and Remote Sensing Letters ， 16 （ 2 ）： 310 - 314 ［ DOI： 10.1109/LGRS.2018.2872355 http://dx.doi.org/10.1109/LGRS.2018.2872355 ］
Wang C Y ， Bochkovskiy A and Liao H Y M . 2022a . YOLOv7： trainable bag-of-freebies sets new state-of-the-art for real-time object detectors ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2207.02696.pdf http://arxiv.org/pdf/2207.02696.pdf
Wang C Y ， Yeh I H and Liao H Y M . 2021a . You only learn one representation： unified network for multiple tasks ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2105.04206.pdf http://arxiv.org/pdf/2105.04206.pdf
Wang J W ， Xu C ， Yang W and Yu L . 2022b . A normalized gaussian Wasserstein distance for tiny object detection ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2110.13389.pdf http://arxiv.org/pdf/2110.13389.pdf
Wang J W ， Yang W ， Guo H W ， Zhang R X and Xia G S . 2021b . Tiny object detection in aerial images // Proceedings of the 25th International Conference on Pattern Recognition . Milan， Italy ： 3791 - 3798 ［ DOI： 10.1109/ICPR48806.2021.9413340 http://dx.doi.org/10.1109/ICPR48806.2021.9413340 ］
Wang P J ， Sun X ， Diao W H and Fu K . 2020a . FMSSD： feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery . IEEE Transactions on Geoscience and Remote Sensing ， 58 （ 5 ）： 3377 - 3390 ［ DOI： 10.1109/TGRS.2019.2954328 http://dx.doi.org/10.1109/TGRS.2019.2954328 ］
Wang T ， Yuan L ， Chen Y P ， Feng J S and Yan S C . 2021c . PnP-DETR： towards efficient visual analysis with Transformers // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 4641 - 4650 ［ DOI： 10.1109/iccv48922.2021.00462 http://dx.doi.org/10.1109/iccv48922.2021.00462 ］
Wang X L ， Girshick R ， Gupta A and He K M . 2018 . Non-local neural networks // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 7794 - 7803 ［ DOI： 10.1109/cvpr.2018.00813 http://dx.doi.org/10.1109/cvpr.2018.00813 ］
Wang Y ， Bashir S M A ， Khan M ， Ullah Q ， Wang R ， Song Y L ， Guo Z and Niu Y L . 2022c . Remote sensing image super-resolution and object detection： benchmark and state of the art . Expert Systems with Applications ， 197 ： # 116793 ［ DOI： 10.1016/j.eswa.2022.116793 http://dx.doi.org/10.1016/j.eswa.2022.116793 ］
Wang Y ， Xu C F ， Liu C W and Li Z K . 2022d . Context information refinement for few-shot object detection in remote sensing images . Remote Sensing ， 14 （ 14 ）： # 3255 ［ DOI： 10.3390/rs14143255 http://dx.doi.org/10.3390/rs14143255 ］
Wang Y ， Yang Y L and Zhao X . 2020b . Object detection using clustering algorithm adaptive searching regions in aerial images // Proceedings of 2020 European Conference on Computer Vision . Glasgow， UK ： Springer： 651 - 664 ［ DOI： 10.1007/978-3-030-66823-5_39 http://dx.doi.org/10.1007/978-3-030-66823-5_39 ］
Woo S ， Park J ， Lee J Y and Kweon I S . 2018 . CBAM： convolutional block attention module // Proceedings of the 15th European Conference on Computer Vision . Munich， Germany ： Springer： 3 - 19 ［ DOI： 10.1007/978-3-030-01234-2_1 http://dx.doi.org/10.1007/978-3-030-01234-2_1 ］
Wu Z Z ， Xu J ， Wang Y ， Sun F ， Tan M and Weise T . 2022 . Hierarchical fusion and divergent activation based weakly supervised learning for object detection from remote sensing images . Information Fusion ， 80 ： 23 - 43 ［ DOI： 10.1016/j.inffus.2021.10.010 http://dx.doi.org/10.1016/j.inffus.2021.10.010 ］
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 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City， USA ： IEEE： 3974 - 3983 ［ DOI： 10.1109/cvpr.2018.00418 http://dx.doi.org/10.1109/cvpr.2018.00418 ］
Xie X X ， Cheng G ， Wang J B ， Yao X W and Han J W . 2021 . Oriented R-CNN for object detection // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision . Montreal， Canada ： IEEE： 3500 - 3509 ［ DOI： 10.1109/iccv48922.2021.00350 http://dx.doi.org/10.1109/iccv48922.2021.00350 ］
Xu C ， Wang J W ， Yang W and Yu L . 2021a . Dot distance for tiny object detection in aerial images // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops . Nashville， USA ： IEEE： 1192 - 1201 ［ DOI： 10.1109/cvprw53098.2021.00130 http://dx.doi.org/10.1109/cvprw53098.2021.00130 ］
Xu J T ， Li Y L and Wang S J . 2022a . AdaZoom： towards scale-aware large scene object detection . IEEE Transactions on Multimedia ， 1 - 1 ［ DOI： 10.1109/TMM.2022.3178871 http://dx.doi.org/10.1109/TMM.2022.3178871 ］
Xu S L ， Wang X X ， Lyu W Y ， Chang Q Y ， Cui C ， Deng K P ， Wang G Z ， Dang Q Q ， Wei S Y ， Du Y N and Lai B H . 2022b . PP-YOLOE： an evolved version of YOLO ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2203.16250.pdf http://arxiv.org/pdf/2203.16250.pdf
Xu X K ， Feng Z J ， Cao C Q ， Li M Y ， Wu J ， Wu Z Y ， Shang ， Y J and Ye S B . 2021b . An improved swin Transformer-based model for remote sensing object detection and instance segmentation . Remote Sensing ， 13 （ 23 ）： # 4779 ［ DOI： 10.3390/rs13234779 http://dx.doi.org/10.3390/rs13234779 ］
Yan J Q ， Zhao L J ， Diao W H ， Wang H Q and Sun X . 2021 . AF-EMS detector： improve the multi-scale detection performance of the anchor-free detector . Remote Sensing ， 13 （ 2 ）： # 160 ［ DOI： 10.3390/rs13020160 http://dx.doi.org/10.3390/rs13020160 ］
Yan Z G ， Song X ， Zhong H Y and Zhu X Z . 2018 . Object detection in optical remote sensing images based on transfer learning convolutional neural networks // Proceedings of the 5th IEEE International Conference on Cloud Computing and Intelligence Systems . Nanjing， China ： IEEE： 935 - 942 ［ DOI： 10.1109/CCIS.2018.8691238 http://dx.doi.org/10.1109/CCIS.2018.8691238 ］
Yang F ， Fan H ， Chu P ， Blasch E and Ling H B . 2019a . Clustered object detection in aerial images // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 8310 - 8319 ［ DOI： 10.1109/iccv.2019.00840 http://dx.doi.org/10.1109/iccv.2019.00840 ］
Yang X ， Hou L P ， Zhou Y ， Wang W T and Yan J C . 2021a . Dense label encoding for boundary discontinuity free rotation detection // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Nashville， USA ： IEEE： 15814 - 15824 ［ DOI： 10.1109/cvpr46437.2021.01556 http://dx.doi.org/10.1109/cvpr46437.2021.01556 ］
Yang X ， Sun H ， Sun X ， Yan M L ， Guo Z and Fu K . 2018 . Position detection and direction prediction for arbitrary-oriented ships via multitask rotation region convolutional neural network . IEEE Access ， 6 ： 50839 - 50849 ［ DOI： 10.1109/ACCESS.2018.2869884 http://dx.doi.org/10.1109/ACCESS.2018.2869884 ］
Yang X ， Yan J C ， Feng Z M and He T . 2021b . R3Det： refined single-stage detector with feature refinement for rotating object . Proceedings of the AAAI Conference on Artificial Intelligence ， 35 （ 4 ）： 3163 - 3171 ［ DOI： 10.1609/aaai.v35i4.16426 http://dx.doi.org/10.1609/aaai.v35i4.16426 ］
Yang X ， Yan J C ， Liao W L ， Yang X K ， Tang J and He T . 2023 . SCRDet++： detecting small， cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing . IEEE Transactions on Pattern Analysis and Machine Intelligence ， 45 （ 2 ）： 2384 - 2399 ［ DOI： 10.1109/tpami.2022.3166956 http://dx.doi.org/10.1109/tpami.2022.3166956 ］
Yang X ， Yan J C ， Ming Q ， Wang W T ， Zhang X P and Tian Q . 2021c . Rethinking rotated object detection with Gaussian Wasserstein distance loss // Proceedings of the 38th International Conference on Machine Learning . Virtual ： ICML： 11830 - 11841 ［ DOI： 10.48550/arXiv.2101.11952 http://dx.doi.org/10.48550/arXiv.2101.11952 ］
Yang X ， Yang J R ， Yan J C ， Zhang Y ， Zhang T F ， Guo Z ， Sun X and Fu K . 2019b . SCRDet： towards more robust detection for small， cluttered and rotated objects // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 8231 - 8240 ［ DOI： 10.1109/iccv.2019.00832 http://dx.doi.org/10.1109/iccv.2019.00832 ］
Yang X ， Yang X J ， Yang J R ， Ming Q ， Wang W T ， Tian Q and Yan J C . 2021d . Learning high-precision bounding box for rotated object detection via Kullback-Leibler divergence // Advances in Neural Information Processing Systems ， 34 ， 18381 - 18394 ［ DOI： 10.48550/arXiv.2106.01883 http://dx.doi.org/10.48550/arXiv.2106.01883 ］
Yang Z ， Liu S H ， Hu H ， Wang L W and Lin S . 2019c . RepPoints： point set representation for object detection // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Seoul， Korea （South） ： IEEE： 9656 - 9665 ［ DOI： 10.1109/ICCV.2019.00975 http://dx.doi.org/10.1109/ICCV.2019.00975 ］
Yao Z Y ， Ai J B ， Li B X and Zhang C . 2021 . Efficient DETR： improving end-to-end object detector with dense prior ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2104.01318.pdf http://arxiv.org/pdf/2104.01318.pdf
Yi J R ， Wu P X ， Liu B ， Huang Q Y ， Qu H and Metaxas D . 2021 . Oriented object detection in aerial images with box boundary-aware vectors // Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision . Waikoloa， USA ： IEEE： 2149 - 2158 ［ DOI： 10.1109/wacv48630.2021.00220 http://dx.doi.org/10.1109/wacv48630.2021.00220 ］
Yu D W and Ji S P . 2022 . A new spatial-oriented object detection framework for remote sensing images . IEEE Transactions on Geoscience and Remote Sensing ， 60 ： # 4407416 ［ DOI： 10.1109/TGRS.2021.3127232 http://dx.doi.org/10.1109/TGRS.2021.3127232 ］
Zhang G J ， Lu S J and Zhang W . 2019 . CAD-Net： a context-aware detection network for objects in remote sensing imagery . IEEE Transactions on Geoscience and Remote Sensing ， 57 （ 12 ）： 10015 - 10024 ［ DOI： 10.1109/TGRS.2019.2930982 http://dx.doi.org/10.1109/TGRS.2019.2930982 ］
Zhang H ， Li F ， Liu S L ， Zhang L ， Su H ， Zhu J ， Ni L M and Shum H Y . 2022a . DINO： DETR with improved DeNoising anchor boxes for end-to-end object detection ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2203.03605.pdf http://arxiv.org/pdf/2203.03605.pdf
Zhang J ， Xie C M ， Xu X ， Shi Z W and Pan B . 2020a . A contextual bidirectional enhancement method for remote sensing image object detection . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ， 13 ： 4518 - 4531 ［ DOI： 10.1109/JSTARS.2020.3015049 http://dx.doi.org/10.1109/JSTARS.2020.3015049 ］
Zhang K ， Wu Y L ， Wang J Y and Wang Q . 2022b . A hierarchical context embedding network for object detection in remote sensing images . IEEE Geoscience and Remote Sensing Letters ， 19 ： # 6508105 ［ DOI： 10.1109/LGRS.2022.3161938 http://dx.doi.org/10.1109/LGRS.2022.3161938 ］
Zhang Y ， Liu X ， Wa S ， Chen S Y and Ma Q . 2022c . GANsformer： a detection network for aerial images with high performance combining convolutional network and Transformer . Remote Sensing ， 14 （ 4 ）： # 923 ［ DOI： 10.3390/rs14040923 http://dx.doi.org/10.3390/rs14040923 ］
Zhang Y J ， Sheng W G ， Jiang J F ， Jing N F ， Wang Q and Mao Z G . 2020c . Priority branches for ship detection in optical remote sensing images . Remote Sensing ， 12 （ 7 ）： # 1196 ［ DOI： 10.3390/rs12071196 http://dx.doi.org/10.3390/rs12071196 ］
Zhang Y L ， Guo L H ， Wang Z F ， Yu Y ， Liu X W and Xu F . 2020b . Intelligent ship detection in remote sensing images based on multi-layer convolutional feature fusion . Remote Sensing ， 12 （ 20 ）： # 3316 ［ DOI： 10.3390/rs12203316 http://dx.doi.org/10.3390/rs12203316 ］
Zhang Z C ， Boubin J ， Stewart C and Khanal S . 2020d . Whole-field reinforcement learning： a fully autonomous aerial scouting method for precision agriculture . Sensors ， 20 （ 22 ）： # 6585 ［ DOI： 10.3390/s20226585 http://dx.doi.org/10.3390/s20226585 ］
Zhao W Q ， Kong Z X ， Zhou Z D and Zhao Z B . 2021 . Target detection algorithm of aerial remote sensing based on feature enhancement technology . Journal of Image and Graphics ， 26 （ 3 ）： 644 - 653
赵文清 ， 孔子旭 ， 周震东 ， 赵振兵 . 2021 . 增强小目标特征的航空遥感目标检测 . 中国图象图形学报 ， 26 （ 3 ）： 644 - 653 ［ DOI： 10.11834/jig.190612 http://dx.doi.org/10.11834/jig.190612 ］
Zheng M H ， Gao P ， Zhang R R ， Li K C ， Wang X G ， Li H S and Dong H . 2021a . End-to-end object detection with adaptive clustering Transformer ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2011.09315.pdf http://arxiv.org/pdf/2011.09315.pdf
Zheng Y B ， Sun P ， Zhou Z T ， Xu W Y and Ren Q . 2021b . ADT-Det： adaptive dynamic refined single-stage Transformer detector for arbitrary-oriented object detection in satellite optical imagery . Remote Sensing ， 13 （ 13 ）： # 2623 ［ DOI： 10.3390/rs13132623 http://dx.doi.org/10.3390/rs13132623 ］
Zheng Z ， Zhong Y F ， Ma A L ， Han X B ， Zhao J ， Liu Y F and Zhang L P . 2020 . HyNet： hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery . ISPRS Journal of Photogrammetry and Remote Sensing ， 166 ： 1 - 14 ［ DOI： 10.1016/j.isprsjprs.2020.04.019 http://dx.doi.org/10.1016/j.isprsjprs.2020.04.019 ］
Zhou X Y ， Wang D Q and Krähenbühl P . 2019 . Objects as points ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/1904.07850.pdf http://arxiv.org/pdf/1904.07850.pdf
Zhou Z ， Huang J F ， Wang J ， Zhang K Y ， Kuang Z M ， Zhong S Q and Song X D . 2015 . Object-oriented classification of sugarcane using time-series middle-resolution remote sensing data based on AdaBoost . PLoS ONE ， 10 （ 11 ）： #e 0142069 ［ DOI： 10.1371/journal.pone.0142069 http://dx.doi.org/10.1371/journal.pone.0142069 ］
Zhu X K ， Lyu S ， Wang X and Zhao Q . 2021a . TPH-YOLOv5： improved YOLOv5 based on Transformer prediction head for object detection on drone-captured scenarios // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops . Montreal， Canada ： IEEE： 2778 - 2788 ［ DOI： 10.1109/iccvw54120.2021.00312 http://dx.doi.org/10.1109/iccvw54120.2021.00312 ］
Zhu X Z ， Hu H ， Lin S and Dai J F . 2019 . Deformable ConvNets V2： more deformable， better results // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Long Beach， USA ： IEEE： 9300 - 9308 ［ DOI： 10.1109/cvpr.2019.00953 http://dx.doi.org/10.1109/cvpr.2019.00953 ］
Zhu X Z ， Su W J ， Lu L W ， Li B ， Wang X G and Dai J F . 2021b . Deformable DETR： deformable Transformers for end-to-end object detection ［EB/OL］. ［ 2023-01-19 ］. http://arxiv.org/pdf/2010.04159.pdf http://arxiv.org/pdf/2010.04159.pdf
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