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MAR20： A benchmark for military aircraft recognition in remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email：1884716321@163.com
  • Introduction：禹文奇， 研究方向为高分辨率遥感图像理解。E-mail： 1884716321@163.com
YU Wenqi,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

  • Email：gcheng@nwpu.edu.cn
  • Introduction：程塨， 研究方向为高分辨率遥感图像理解。E-mail： gcheng@nwpu.edu.cn
CHENG Gong*,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

WANG Meijun,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

YAO Yanqing,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

XIE Xingxing,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

YAO Xiwen,  
  • Affiliation：

    School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

HAN Junwei

résumé

Military aircraft recognition in remote sensing images locates military aircraft in remote sensing images and classify them at a fine-grained level. It plays a vital role in reconnaissance and early warning, intelligence analysis, and other fields. However, the development of military aircraft recognition in remote sensing images is relatively slow due to the lack of publicly available datasets. Therefore, constructing a high-quality and large-scale military aircraft recognition dataset is important.This study constructs a public remote sensing image military aircraft recognition dataset called MAR20 to promote the research progress in this field. The dataset has the following characteristics: (1) MAR20 is currently the largest remote sensing image military aircraft recognition dataset, which includes 3842 images, 20 types, and 22341 instances. Each instance has a horizontal bounding box and also an oriented bounding box. (2) Given that all fine-grained types belong to the aircraft category, different types of aircraft often have similar characteristics, which result in high similarity of different types of targets. (3) Large intra-class differences exist between targets of the same type due to the influence of climate, season, illumination, occlusion, and even the atmospheric scattering in the process of remote sensing imaging.To establish a benchmark for military aircraft recognition in remote sensing images, this paper study evaluates seven commonly used horizontal object recognition methods, namely, Faster R-CNN, RetinaNet, ATSS, FCOS, Cascade R-CNN, TSD, and Double-Head, as well as eight oriented object recognition methods, namely, Faster R-CNN-O, RetinaNet-O, RoI Transformer, Gliding Vertex, Double-Head-O, Oriented R-CNN, FCOS-O, and S2A-Net, on the MAR20 dataset. Through experimental comparisons in the tasks of horizontal object recognition and oriented object recognition, two-stage methods are proven to be more effective in target recognition than one-stage methods.In this study, 3842 high-resolution remote sensing images were collected from 60 military airports around the world through Google Earth, and a large-scale publicly available remote sensing image military aircraft recognition dataset, named MAR20, was established. In terms of data annotation, MAR20 provides two annotation methods, namely, horizontal bounding boxes and oriented bounding boxes, which correspond to the tasks of horizontal target recognition and oriented target recognition. We hope that the MAR20 dataset established in this study could promote the research progress in this field. MAR20 can be downloaded athttps://gcheng-nwpu.github.io/.

mots-clés

Military aircraft;object recognition;dataset;remote sensing images;Fine-grained recognition

References

  1. 1.
    Cai Z W and Vasconcelos N. 2018. Cascade R-CNN: delving into high quality object detection//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE: 6154-6162
  2. 2.
    Chen K, Wang J Q, Pang J M, Cao Y H, Xiong Y, Li X X, Sun S Y, Feng W S, Liu Z W, Xu J R, Zhang Z, Cheng D Z, Zhu C C, Cheng T H, Zhao Q J, Li B Y, Lu X, Zhu R, Wu Y, Dai J F, Wang J D, Shi J P, Ouyang W L, Loy C C and Lin D H. 2019. MMDetection: open MMLab detection toolbox and benchmark. arXiv preprint arXiv: 1906.07155
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Everingham M, Van Gool L, Williams C K I, Winn J and Zisserman A. 2010. The Pascal visual object classes (VOC) challenge. International Journal of Computer Vision, 88(2): 303-338
  6. 6.
    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
  7. 7.
    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
  8. 8.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE: 770-778
  9. 9.
    Li K, Wan G, Cheng G, Meng L Q and Han J W. 2020. Object detection in optical remote sensing images: a survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 159: 296-307
  10. 10.
    Lin T Y, Dollár P, Girshick R, He K M, Hariharan B and Belongie S. 2017a. Feature pyramid networks for object detection//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE: 936-944
  11. 11.
    Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2017b. Focal loss for dense object detection//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice: IEEE: 2999-3007
  12. 12.
    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
  13. 13.
    Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149
  14. 14.
    Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S A, Huang Z H, Karpathy A, Khosla A, Bernstein M, Berg A C and Fei-Fei L. 2015. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3): 211-252
  15. 15.
    Russell B C, Torralba A, Murphy K P and Freeman W T. 2008. LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision, 77(1/3): 157-173
  16. 16.
    Shermeyer J, Hossler T, Van Etten A, Hogan D, Lewis R and Kim D. 2020. RarePlanes: synthetic data takes flight. arXiv preprint arXiv: 2006.02963
  17. 17.
    Song G L, Liu Y and Wang X G. 2020. Revisiting the sibling head in object detector//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 11560-11569
  18. 18.
    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
  19. 19.
    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
  20. 20.
    Wu Y, Chen Y P, Yuan L, Liu Z C, Wang L J, Li H Z and Fu Y. 2020a. Rethinking classification and localization for object detection//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 10183-10192
  21. 21.
    Wu Z Z, Wan S H, Wang X F, Tan M, Zou L, Li X L and Chen Y. 2020b. A benchmark data set for aircraft type recognition from remote sensing images. Applied Soft Computing, 89: 106132
  22. 22.
    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
  23. 23.
    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
  24. 24.
    Xu Y C, Fu M T, Wang Q M, Wang Y K, Chen K, Xia G S and Bai X. 2021. Gliding vertex on the horizontal bounding box for multi-oriented object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4): 1452-1459
  25. 25.
    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. Journal of Remote Sensing, 25(5): 1124-1137
  26. 26.
    Zhang S F, Chi C, Yao Y Q, Lei Z and Li S Z. 2020. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE: 9756-9765
  27. 27.
    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
  28. 28.
    Zou Z X and Shi Z W. 2018. Random access memories: a new paradigm for target detection in high resolution aerial remote sensing images. IEEE Transactions on Image Processing, 27(3): 1100-1111

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