Research progress of deep learning-based object detection of optical remote sensing image

Yurong LIAO; Haining WANG; Cunbao LIN; Yang LI; Yuqiang FANG; Shuyan NI

doi:10.11959/j.issn.1000-436x.2022071

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Research progress of deep learning-based object detection of optical remote sensing image

Comprehensive Reviews | 更新时间：2024-06-05

- Research progress of deep learning-based object detection of optical remote sensing image
- Journal on Communications Vol. 43, Issue 5, Pages: 190-203(2022)
- 作者机构：
  
  1. 航天工程大学电子与光学工程系，北京 101416
  2. 航天工程大学研究生院，北京 101416
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61805283);The National Natural Science Foundation of China(61805284);The National Natural Science Foundation of China(61906213)
- DOI：10.11959/j.issn.1000-436x.2022071
  CLC： TP391.41
- Online First：2022-05，
  
  Published：25 May 2022
- 稿件说明：
移动端阅览
Yurong LIAO, Haining WANG, Cunbao LIN, et al. Research progress of deep learning-based object detection of optical remote sensing image[J]. Journal on Communications, 2022, 43(5): 190-203.
DOI：

Yurong LIAO, Haining WANG, Cunbao LIN, et al. Research progress of deep learning-based object detection of optical remote sensing image[J]. Journal on Communications, 2022, 43(5): 190-203. DOI： 10.11959/j.issn.1000-436x.2022071.

摘要

目标检测是光学遥感图像解译中的核心问题，在情报侦察、目标监视、灾害救援等领域均具有广泛应用。首先，结合深度学习光学遥感图像目标检测算法研究进展，对基于候选区域和回归分析的两类遥感目标检测算法进行了综述。其次，针对旋转目标、小目标、多尺度、密集目标四类常见特定任务场景目标检测算法改进进行了总结。再次，结合常用遥感图像数据集对不同算法性能进行了对比分析。最后，对未来遥感图像目标检测研究值得关注的问题进行了展望，为后续相关研究提供思路。

Abstract

Object detection is the core issue in the interpretation of optical remote sensing images

and it is widely used in fields such as intelligence reconnaissance

target monitoring

and disaster rescue.Firstly

combined with the research progress of deep learning optical remote sensing image object detection algorithms

the two types of algorithms based on candidate regions and regression analysis were reviewed.Secondly

the improvement of object detection algorithms for four types of common task-specific scenes were summarized

including rotating objects

small objects

multi-scales

and dense objects.Then

combined with commonly used remote sensing image data sets

the performance of different algorithms was compared and analyzed.Finally

the issues worthy of attention in remote sensing image object detection in the future were prospected

and ideas for follow-up related research were provided.

关键词

Keywords

references

CHENG G , HAN J W . A survey on object detection in optical remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2016 , 117 : 11 - 28 .

WANG Z , BOVIK A C , SHEIKH H R , et al . Image quality assessment:from error visibility to structural similarity [J ] . IEEE Transactions on Image Processing , 2004 , 13 ( 4 ): 1 - 14 .

WU W . Quantized Gromov-Hausdorff distance [J ] . Journal of Functional Analysis , 2006 , 238 : 58 - 98 .

HAREL J , KOCH C , PERONA P . Graph-based visual saliency [J ] . Advances in Neural Information Processing Systems , 2006 , 19 : 545 - 552 .

SEDAGHAT A , EBADI H . Remote sensing image matching based on adaptive binning SIFT descriptor [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2015 , 53 ( 10 ): 5283 - 5293 .

DALAL N , TRIGGS B . Histograms of oriented gradients for human detection [C ] // Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2005 : 886 - 893 .

GÓMEZ C L , TUIA D , MOSER G , et al . Multimodal classification of remote sensing images:a review and future directions [J ] . Proceedings of the IEEE , 2015 , 103 ( 9 ): 1560 - 1584 .

ZHU X X , TUIA D , MOU L C , et al . Deep learning in remote sensing:a comprehensive review and list of resources [J ] . IEEE geoscience and remote sensing magazine , 2017 , 5 ( 4 ): 8 - 36 .

罗会兰 , 陈鸿坤 . 基于深度学习的目标检测研究综述 [J ] . 电子学报 , 2020 , 48 ( 6 ): 1230 - 1239 .

LUO H L , CHEN H K . Survey of object detection based on deep learning [J ] . ACTA ELECTRONICA SINICA , 2021 , 48 ( 6 ): 1230 - 1239 .

LI Y S , ZHANG Y J , HUANG X , et al . Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 146 : 182 - 196 .

XUE B , TONG N N . DIOD:fast and efficient weakly semi-supervised deep complex ISAR object detection [J ] . IEEE Transactions on Cybernetics , 2019 , 49 ( 11 ): 3991 - 4003 .

YU D W , JI S P . A new spatial-oriented object detection framework for remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 16 .

周勇 , 陈思霖 , 赵佳 , 等 . 基于弱语义注意力的遥感图像可解释目标检测 [J ] . 电子学报 , 2021 , 49 ( 4 ): 679 - 689 .

ZHOU Y , CHEN S L , ZHAO J , et al . Weakly semantic based attention network for interpretable object detection in remote sensing imagery [J ] . ACTA ELECTRONICA SINICA , 2021 , 49 ( 4 ): 679 - 689 .

ZHANG Y J , SHENG W G , JIA J F , et al . Priority branches for ship detection in optical remote sensing [J ] . Remote Sensing , 2020 , 12 ( 7 ): 1196 .

LI K , WAN G , CHENG G , et al . Object detection in optical remote sensing images:a survey and a new benchmark [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 159 : 296 - 307 .

聂光涛 , 黄华 . 光学遥感图像目标检测算法综述 [J ] . 自动化学报 , 2021 , 47 ( 8 ): 1749 - 1768 .

NIE G T , HUANG H . A survey of object detection in optical remote sensing images [J ] . ACTA AUTOMATICA SINICA , 2021 , 47 ( 8 ): 1749 - 1768 .

宋志娜 , 眭海刚 , 李永成 . 高分辨率可见光遥感图像舰船目标检测综述 [J ] . 武汉大学学报(信息科学版) , 2021 :doi.org/10.13203/j.whugis20200481.

SONG Z N , SUI H G , LI Y C . A survey on ship detection technology in high-resolution optical remote sensing images [J ] . Geomatics and Information Science of Wuhan University , 2021 :doi.org/10.13203/j.whugis20200481.

GIRSHICK R , DONAHUE J , DARRELL T , et al . Rich feature hierarchies for accurate object detection and semantic segmentation [C ] // Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2014 : 580 - 587 .

GIRSHICK R , . Fast R-CNN [C ] // Proceedings of IEEE Conference on Computer Vision . Piscataway:IEEE Press , 2015 : 1440 - 1448 .

REN S Q , HE K M , GIRSHICK R , et al . Faster R-CNN:towards real-time object detection with region proposal networks [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 ( 6 ): 1137 - 1149 .

XU C Y , LI C Z , CUI Z , et al . Hierarchical semantic propagation for object detection in remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 6 ): 4353 - 4364 .

FU K , CHANG Z H , ZHANG Y , et al . Rotation-aware and multi-scale convolutional neural network for object detection in remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 161 : 294 - 308 .

CHEN C Y , GONG W G , CHEN Y L , et al . Object detection in remote sensing images based on a scene-contextual feature pyramid network [J ] . Remote Sensing , 2019 , 11 ( 3 ): 339 .

YAN D C , LI G Q , LI X Q , et al . An improved faster R-CNN method to detect tailings ponds from high-resolution remote sensing images [J ] . Remote Sensing , 2021 , 13 ( 11 ): 2052 .

ZHANG G J , LU S J , ZHANG W . CAD-Net:a context-aware detection network for objects in remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 12 ): 10015 - 10024 .

LI Q P , MOU L C , LIU Q J , et al . HSF-Net:multiscale deep feature embedding for ship detection in optical remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 12 ): 7147 - 7161 .

赵彤洲 , 杨成万 , 刘威 . 非局部特征增强的遥感图像目标检测方法 [J ] . 华中科技大学学报(自然科学版) , 2021 :doi.org/10.13245/j.hust.210909.

ZHAO T Z , YANG C W , LIU W . Remote sensing image object detection method based on non-local feature enhancement [J ] . Journal of Huazhong University of Science ＆ Technology.(Natural Science Edition) , 2021 :doi.org/10.13245/j.hust.210909.

CHENG G , HAN J W , ZHOU P C , et al . Learning rotation-invariant and fisher discriminative convolutional neural networks for object detection [J ] . IEEE Transactions on Image Processing , 2019 , 28 ( 1 ): 265 - 278 .

HAN X B , ZHONG Y F , ZHANG L P . An efficient and robust integrated geospatial object detection framework for high spatial resolution remote sensing imagery [J ] . Remote Sensing , 2017 , 9 ( 7 ): 666 .

ZHONG Y F , HAN X B , ZHANG L P . Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 138 : 281 - 294 .

LONG Y , GONG Y P , XIAO Z F , et al . Accurate object localization in remote sensing images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2017 , 55 ( 5 ): 2486 - 2498 .

LI K , CHENG G , BU S H , et al . Rotation-insensitive and context-augmented object detection in remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 4 ): 2337 - 2348 .

DONG R C , XU D Z , ZHAO J , et al . Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 11 ): 8534 - 8545 .

仲伟峰 , 郭峰 , 向世明 , 等 . 旋转矩形区域的遥感图像舰船目标检测模型 [J ] . 计算机辅助设计与图形学学报 , 2019 , 31 ( 11 ): 1935 - 1945 .

ZHONG W F , GUO F , XIANG S M , et al . Ship detection in remote sensing based with rotated rectangular region [J ] . Journal of Computer-Aided Design ＆ Computer Graphics , 2019 , 31 ( 11 ): 1935 - 1945 .

REN Y , ZHU C R , XIAO S P . Deformable Faster R-CNN with aggregating multi-layer features for partially occluded object detection in optical remote sensing images [J ] . Remote Sensing , 2018 , 10 ( 9 ): 1470 .

DING P , ZHANG Y , DENG W J , et al . A light and faster regional convolutional neural network for object detection in optical remote sensing images [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 141 : 208 - 218 .

ZHANG S M , WU R Z , XU K Y , et al . R-CNN-based ship detection from high resolution remote sensing imagery [J ] . Remote Sensing , 2019 , 11 ( 6 ): 631 .

YAN J Q , WANG H Q , YAN M L , et al . IoU-adaptive deformable R-CNN:make full use of IoU for multi-class object detection in remote sensing imagery [J ] . Remote Sensing , 2019 , 11 ( 3 ): 286 .

HE K M , GKIOXARI G , DOLLÁR P , et al . Mask R-CNN [C ] // Proceedings of 2017 IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2017 : 2980 - 2988 .

WU Q F , FENG D Q , CAO C Q , et al . Improved Mask R-CNN for aircraft detection in remote sensing images [J ] . Sensors , 2021 , 21 ( 8 ): 2618 .

DING J , XUE N , LONG Y , et al . Learning RoI transformer for oriented object detection in aerial images [C ] // Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2019 : 2844 - 2853 .

ZHU Y X , DU J , WU X Q . Adaptive period embedding for representing oriented objects in aerial images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 10 ): 7247 - 7257 .

XU Z Z , XU X , WANG L , et al . Deformable ConvNet with aspect ratio constrained NMS for object detection in remote sensing imagery [J ] . Remote Sensing , 2017 , 9 ( 12 ): 1312 .

CHEN H B , JIANG S , HE G H , et al . TEANS:a target enhancement and attenuated nonmaximum suppression object detector for remote sensing images [J ] . IEEE Geoscience and Remote Sensing Letters , 2021 , 18 ( 4 ): 632 - 636 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once:unified,real-time object detection [C ] // Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 779 - 788 .

LIU W , ANGUELOV D , ERHAN D , et al . SSD:single shot multibox detector [C ] // Proceedings of European Conference on Computer Vision . Berlin:Springer , 2016 : 21 - 37 .

REDMON J , FARHADI A . YOLO9000:better,faster,stronger [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 6517 - 6525 .

REDMON J , FARHADI A . YOLOv3:an incremental improvement [J ] . arXiv Preprint,arXiv:1804.02767 , 2018 .

BOCHKOVSKIY A , WANG C Y , LIAO H Y . YOLOv4:optimal speed and accuracy of object detection [J ] . arXiv Preprint,arXiv:2004.10934 , 2020 .

JOCHER G , STOKEN A , BOROVEC J , et al . Ultralytics/yolov5:v4.0 [EB ] . 2021 .

GE Z , LIU S T , WANG F , et al . YOLOX:exceeding YOLO series in 2021 [J ] . arXiv Preprint,arXiv:2107.08430 , 2021 .

MA H J , LIU Y L , REN Y H , et al . Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3 [J ] . Remote Sensing , 2019 , 12 ( 1 ): 44 .

CHANG Y L , ANAGAW A , CHANG L N , et al . Ship detection based on YOLOv2 for SAR imagery [J ] . Remote Sensing , 2019 , 11 ( 7 ): 786 .

WANG C S , WANG Q , WU H R , et al . Low-altitude remote sensing opium poppy image detection based on modified YOLOv3 [J ] . Remote Sensing , 2021 , 13 ( 11 ): 2130 .

XU D Q , WU Y Q . FE-YOLO:a feature enhancement network for remote sensing target detection [J ] . Remote Sensing , 2021 , 13 ( 7 ): 1311 .

YANG X , YAN J C , FENG Z M , et al . R 3 Det:refined single-stage detector with feature refinement for rotating object [C ] // Proceedings of the 35th AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2021 : 3163 - 3171 .

LIU W C , MA L , WANG J , et al . Detection of multiclass objects in optical remote sensing images [J ] . IEEE Geoscience and Remote Sensing Letters , 2019 , 16 ( 5 ): 791 - 795 .

XU D Q , WU Y Q . MRFF-YOLO:a multi-receptive fields fusion network for remote sensing target detection [J ] . Remote Sensing , 2020 , 12 ( 19 ): 3118 .

HU J M , ZHI X Y , SHI T J , et al . PAG-YOLO:a portable attention-guided YOLO network for small ship detection [J ] . Remote Sensing , 2021 , 13 ( 16 ): 3059 .

CHEN L Q , SHI W X , DENG D X . Improved YOLOv3 based on attention mechanism for fast and accurate ship detection in optical remote sensing images [J ] . Remote Sensing , 2021 , 13 ( 4 ): 660 .

XU D Q , WU Y Q . Improved YOLO-V3 with DenseNet for multi-scale remote sensing target detection [J ] . Sensors , 2020 , 20 ( 15 ): 4276 .

WU K J , BAI C S , WANG D C , et al . Improved object detection algorithm of YOLOv3 remote sensing image [J ] . IEEE Access , 2021 , 9 : 113889 - 113900 .

PHAM M T , COURTRAI L , FRIGUET C , et al . YOLO-fine:one-stage detector of small objects under various backgrounds in remote sensing images [J ] . Remote Sensing , 2020 , 12 ( 15 ): 2501 .

VAN ETTEN A . You only look twice:rapid multi-scale object detection in satellite imagery [J ] . arXiv Preprint,arXiv:1805.09512 , 2018 .

HONG Z H , YANG T , TONG X H , et al . Multi-scale ship detection from SAR and optical imagery via a more accurate YOLOv3 [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 : 6083 - 6101 .

SHARMA M , DHANARAJ M , KARNAM S , et al . YOLOrs:object detection in multimodal remote sensing imagery [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2020 , 14 : 1497 - 1508 .

LU X C , JI J , XING Z Q , et al . Attention and feature fusion SSD for remote sensing object detection [J ] . IEEE Transactions on Instrumentation and Measurement , 2021 , 70 : 1 - 9 .

QU J S , SU C , ZHANG Z W , et al . Dilated convolution and feature fusion SSD network for small object detection in remote sensing images [J ] . IEEE Access , 2020 , 8 : 82832 - 82843 .

YIN R H , ZHAO W , FAN X D , et al . AF-SSD:an accurate and fast single shot detector for high spatial remote sensing imagery [J ] . Sensors , 2020 , 20 ( 22 ): 6530 .

BAO S Z , ZHONG X , ZHU R F , et al . Single shot anchor refinement network for oriented object detection in optical remote sensing imagery [J ] . IEEE Access , 2019 , 7 : 87150 - 87161 .

TANG T Y , ZHOU S L , DENG Z P , et al . Arbitrary-oriented vehicle detection in aerial imagery with single convolutional neural networks [J ] . Remote Sensing , 2017 , 9 ( 11 ): 1170 .

LIU L , PAN Z X , LEI B . Learning a rotation invariant detector with rotatable bounding box [J ] . arXiv Preprint,arXiv:1711.09405 , 2017 .

郭智 , 宋萍 , 张义 , 等 . 基于深度卷积神经网络的遥感图像飞机目标检测方法 [J ] . 电子与信息学报 , 2018 , 40 ( 11 ): 2684 - 2690 .

GUO Z , SONG P , ZHANG Y , et al . Aircraft detection method based on deep convolutional neural network for remote sensing images [J ] . Journal of Electronics ＆ Information Technology , 2018 , 40 ( 11 ): 2684 - 2690 .

李晖晖 , 周康鹏 , 韩太初 . 基于CReLU和FPN改进的 SSD舰船目标检测 [J ] . 仪器仪表学报 , 2020 , 41 ( 4 ): 183 - 190 .

LI H H , ZHOU K P , HAN T C . Ship object detection based on SSD improved with CReLU and FPN [J ] . Chinese Journal of Scientific Instrument , 2020 , 41 ( 4 ): 183 - 190 .

WANG P J , SUN X , DIAO W H , et al . FMSSD:feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 5 ): 3377 - 3390 .

SCHILLING H , BULATOV D , NIESSNER R , et al . Detection of vehicles in multisensor data via multibranch convolutional neural networks [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018 , 11 ( 11 ): 4299 - 4316 .

QIAN W , YANG X , PENG S L , et al . Learning modulated loss for rotated object detection [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2021 : 2458 - 2466 .

XU Y C , FU M T , WANG Q M , et al . Gliding vertex on the horizontal bounding box for multi-oriented object detection [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021 , 43 ( 4 ): 1452 - 1459 .

YANG X , YANG J R , YAN J C , et al . SCRDet:towards more robust detection for small,cluttered and rotated objects [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 2019 : 8231 - 8240 .

HOU L P , LU K , XUE J , et al . Cascade detector with feature fusion for arbitrary-oriented objects in remote sensing images [C ] // Proceedings of 2020 IEEE International Conference on Multimedia and Expo . Piscataway:IEEE Press , 2020 : 1 - 6 .

YANG X , YAN J C , HE T . Arbitrary-oriented object detection with circular smooth label [J ] . arXiv Preprint,arXiv:2003.05597 , 2020 .

YANG X , HOU L P , ZHOU Y , et al . Dense label encoding for boundary discontinuity free rotation detection [C ] // Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2021 : 15814 - 15824 .

YANG X , YAN J C , MING Q , et al . Rethinking rotated object detection with Gaussian Wasserstein distance loss [C ] // Proceedings of International Conference on Machine Learning . New York:ACM Press , 2021 : 11830 - 11841 .

PAN X J , REN Y Q , SHENG K K , et al . Dynamic refinement network for oriented and densely packed object detection [C ] // Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway:IEEE Press , 2020 : 11204 - 11213 .

WEI H R , ZHANG Y , CHANG Z H , et al . Oriented objects as pairs of middle lines [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 169 : 268 - 279 .

FENG P M , LIN Y T , GUAN J , et al . TOSO:student’s-T distribution aided one-stage orientation target detection in remote sensing images [C ] // Proceedings of 2020 IEEE International Conference on Acoustics,Speech and Signal Processing . Piscataway:IEEE Press , 2020 : 4057 - 4061 .

LI Y Y , HUANG Q , PEI X , et al . RADet:refine feature pyramid network and multi-layer attention network for arbitrary-oriented object detection of remote sensing images [J ] . Remote Sensing , 2020 , 12 ( 3 ): 389 .

CHENG G , ZHOU P C , HAN J W . Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 12 ): 7405 - 7415 .

LI C Y , CONG R M , GUO C L , et al . A parallel down-up fusion network for salient object detection in optical remote sensing images [J ] . Neurocomputing , 2020 , 415 : 411 - 420 .

ZHANG Q J , CONG R M , LI C Y , et al . Dense attention fluid network for salient object detection in optical remote sensing images [J ] . IEEE Transactions on Image Processing , 2021 , 30 : 1305 - 1317 .

DENG Z P , SUN H , ZHOU S L , et al . Multi-scale object detection in remote sensing imagery with convolutional neural networks [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2018 , 145 : 3 - 22 .

DONG Z P , WANG M , WANG Y L , et al . Object detection in high resolution remote sensing imagery based on convolutional neural networks with suitable object scale features [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 3 ): 2104 - 2114 .

WANG C , BAI X , WANG S , et al . Multiscale visual attention networks for object detection in VHR remote sensing images [J ] . IEEE Geoscience and Remote Sensing Letters , 2019 , 16 ( 2 ): 310 - 314 .

LI C Y , LUO B , HONG H L , et al . Object detection based on global-local saliency constraint in aerial images [J ] . Remote Sensing , 2020 , 12 ( 9 ): 1435 .

YU Y T , GUAN H Y , LI D L , et al . Orientation guided anchoring for geospatial object detection from remote sensing imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 160 : 67 - 82 .

HUA X , WANG X Q , RUI T , et al . A fast self-attention cascaded network for object detection in large scene remote sensing images [J ] . Applied Soft Computing , 2020 ,94:106495.

ZHENG Z , ZHONG Y F , MA A L , et al . HyNet:hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 166 : 1 - 14 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C ] // Annual Conference on Neural Information Processing Systems . Cambridge:MIT Press , 2017 : 5998 - 6008 .

ZHU X K , LYU S C , WANG X , et al . TPH-YOLOv5:improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios [C ] // Proceedings of 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) . Piscataway:IEEE Press , 2021 : 2778 - 2788 .

QING Y H , LIU W Y , FENG L Y , et al . Improved YOLO network for free-angle remote sensing target detection [J ] . Remote Sensing , 2021 , 13 ( 11 ): 2171 .

HEITZ G , KOLLER D . Learning spatial context:using stuff to find things [C ] // Lecture Notes in Computer Science . Berlin:Springer , 2008 : 30 - 43 .

FRANKLIN T , BRIAN C , CRAIG P , et al . Overhead imagery research data set - an annotated data library ＆ tools to aid in the development of computer vision algorithms [C ] // Proceedings of 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009) . Piscataway:IEEE Press , 2009 : 1 - 8 .

MAGGIORI E , TARABALKA Y , CHARPIAT G , et al . Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark [C ] // Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium . Piscataway:IEEE Press , 2017 : 3226 - 3229 .

ZHU H G , CHEN X G , DAI W Q , et al . Orientation robust object detection in aerial images using deep convolutional neural network [C ] // Proceedings of 2015 IEEE International Conference on Image Processing . Piscataway:IEEE Press , 2015 : 3735 - 3739 .

CHENG G , HAN J W , ZHOU P C , et al . Multi-class geospatial object detection and geographic image classification based on collection of part detectors [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2014 , 98 : 119 - 132 .

RAZAKARIVONY S , JURIE F . Vehicle detection in aerial imagery:a small target detection benchmark [J ] . Journal of Visual Communication and Image Representation , 2016 , 34 : 187 - 203 .

LIU Z K , YUAN L , WENG L B , et al . A high resolution optical satellite image dataset for ship recognition and some new baselines [C ] // Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods . Francisco:SciTePress , 2017 : 324 - 331 .

LIU K , MATTYUS G . Fast multiclass vehicle detection on aerial images [J ] . IEEE Geoscience and Remote Sensing Letters , 2015 , 12 ( 9 ): 1938 - 1942 .

ZHANG Y L , YUAN Y , FENG Y C , et al . Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2019 , 57 ( 8 ): 5535 - 5548 .

ZOU Z X , SHI Z W . Random access memories:a new paradigm for target detection in high resolution aerial remote sensing images [J ] . IEEE Transactions on Image Processing , 2018 , 27 ( 3 ): 1100 - 1111 .

YANG Y M . ITCVD dataset [EB ] . 2018 .

XIA G S , BAI X , DING J , et al . DOTA:a large-scale dataset for object detection in aerial images [C ] // Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2018 : 3974 - 3983 .

SUN X , WANG P J , YAN Z Y , et al . FAIR1M:a benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2022 , 184 : 116 - 130 .

CHENG G , YAN B W , SHI P Z , et al . Prototype-CNN for few-shot object detection in remote sensing images [C ] // Proceedings of IEEE Transactions on Geoscience and Remote Sensing . Piscataway:IEEE Press , 2022 : 1 - 10 .

LI X , DENG J Y , FANG Y . Few shot object detection in remote sensing images [C ] // Proceedings of IEEE Transactions on Geoscience and Remote Sensing . Piscataway:IEEE Press , 2021 : 76 - 81 .

CUI Y Y , HOU B , WU Q , et al . Remote sensing object tracking with deep reinforcement learning under occlusion [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 13 .

MERANER A , EBEL P , ZHU X X , et al . Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 166 : 333 - 346 .

DU Q , TANG B , XIE W Y , et al . Parallel and distributed computing for anomaly detection from hyperspectral remote sensing imagery [J ] . Proceedings of the IEEE , 2021 , 109 ( 8 ): 1306 - 1319 .

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