EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 29 / No 5 (October 2024) Wuhan Univ. J. Nat. Sci., 29 5 (2024) 439-452 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 29, Number 5, October 2024
Page(s) 439 - 452
DOI https://doi.org/10.1051/wujns/2024295439
Published online 20 November 2024
  1. Dai L J, Liu C. Research on remote sensing image of land cover classification based on multiple classifier combination[J]. Wuhan University Journal of Natural Sciences, 2011, 16(4): 363-368. [CrossRef] [Google Scholar]
  2. 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. [CrossRef] [Google Scholar]
  3. Shivappriya S N, Priyadarsini M J P, Stateczny A, et al. Cascade object detection and remote sensing object detection method based on trainable activation function[J]. Remote Sensing, 2021, 13(2): 200. [NASA ADS] [CrossRef] [Google Scholar]
  4. Zhang Y, Jin R, Zhou Z H. Understanding bag-of-words model: A statistical framework[J]. International Journal of Machine Learning and Cybernetics, 2010, 1(1): 43-52. [CrossRef] [Google Scholar]
  5. Sandoval C, Pirogova E, Lech M. Two-stage deep learning approach to the classification of fine-art paintings[J]. IEEE Access, 2019, 7: 41770-41781. [NASA ADS] [CrossRef] [Google Scholar]
  6. He K M, Zhang X Y, Ren S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916. [CrossRef] [Google Scholar]
  7. Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV). New York: IEEE, 2015: 1440-1448. [Google Scholar]
  8. Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. New York: ACM, 2015: 91-99. [Google Scholar]
  9. de Vos B D, Berendsen F F, Viergever M A, et al. A deep learning framework for unsupervised affine and deformable image registration[J]. Medical Image Analysis, 2019, 52: 128-143. [CrossRef] [PubMed] [Google Scholar]
  10. Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//Computer Vision–ECCV 2016. Cham: Springer, 2016: 21-37. [Google Scholar]
  11. Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//2017 IEEE International Conference on Computer Vision (ICCV). New York: IEEE, 2017: 2999-3007. [Google Scholar]
  12. Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 779-788. [Google Scholar]
  13. 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. [NASA ADS] [CrossRef] [Google Scholar]
  14. Yang X, Yang J R, Yan J C, et al. SCRDet: Towards more robust detection for small, cluttered and rotated objects[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 8231-8240. [Google Scholar]
  15. 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. [NASA ADS] [CrossRef] [Google Scholar]
  16. Nayan A A, Saha J, Nokib Mozumder A, et al. Real time multi-class object detection and recognition using vision augmentation algorithm[J]. International Journal of Advanced Science and Technology, 2020, 29(5): 14070-14083. [Google Scholar]
  17. Xi Y, Jia W, Zheng J, et al. DRL-GAN: Dual-stream representation learning GAN for low-resolution image classification in UAV applications[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 1705-1716. [NASA ADS] [CrossRef] [Google Scholar]
  18. Nie X, Duan M Y, Ding H X, et al. Attention mask R-CNN for ship detection and segmentation from remote sensing images[J]. IEEE Access, 2020, 8: 9325-9334. [NASA ADS] [CrossRef] [Google Scholar]
  19. Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-Time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2016: 779-788. [Google Scholar]
  20. Redmon J, Farhadi A. YOLOv3: An incremental improvement[EB/OL]. [2018-04-08]. http://arxiv.org/abs/1804.02767. [Google Scholar]
  21. Cao C Q, Wu J, Zeng X D, et al. Research on airplane and ship detection of aerial remote sensing images based on convolutional neural network[J]. Sensors, 2020, 20(17): 4696. [Google Scholar]
  22. Bochkovskiy A, Wang C Y, Liao H M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. [2020-04-23]. https://arxiv.org/abs/2004.10934. [Google Scholar]
  23. Yu P D, Wang X, Liu J H, et al. Bridge target detection in remote sensing image based on improved YOLOv4 algorithm[C]//2020 4th International Conference on Computer Science and Artificial Intelligence. New York: ACM, 2020: 139-145. [Google Scholar]
  24. Zhao B Y, Wu Y F, Guan X R, et al. An improved aggregated-mosaic method for the sparse object detection of remote sensing imagery[J]. Remote Sensing, 2021, 13(13): 2602. [NASA ADS] [CrossRef] [Google Scholar]
  25. Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV). Cham: Springer, 2018: 3-19. [Google Scholar]
  26. Wang F, Jiang M Q, Qian C, et al. Residual attention network for image classification[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2017: 6450-6458. [Google Scholar]
  27. Ge Z, Liu S T, Wang F, et al. YOLOX: Exceeding YOLO series in 2021[EB/OL]. [2021-08-06]. http://arxiv.org/abs/2107.08430. [Google Scholar]
  28. Wang W H, Xie E Z, Song X G, et al. Efficient and accurate arbitrary-shaped text detection with pixel aggregation network[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). New York: IEEE, 2019: 8439-8448. [Google Scholar]
  29. Shen Y C, Jin H, Du B. An improved method to detect remote sensing image targets captured by sensor network[J]. Wuhan University Journal of Natural Sciences, 2011, 16(4): 301-307. [CrossRef] [Google Scholar]
  30. Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2018: 7132-7141. [Google Scholar]
  31. Wang Q L, Wu B G, Zhu P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2020: 11531-11539. [Google Scholar]
  32. He Y H, Zhu C C, Wang J R, et al. Bounding box regression with uncertainty for accurate object detection[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New York: IEEE, 2019: 2883-2892. [Google Scholar]
  33. Yu J H, Jiang Y N, Wang Z Y, et al. UnitBox: An advanced object detection network[C]//Proceedings of the 24th ACM international conference on Multimedia. New York: ACM, 2016: 516-520. [Google Scholar]
  34. He J B, Erfani S, Ma X J, et al. Alpha-IoU: A family of power intersection over union losses for bounding box regression[EB/OL]. [2022-01-22]. http://arxiv.org/abs/2110.13675. [Google Scholar]
  35. 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. [NASA ADS] [CrossRef] [Google Scholar]
  36. Xiao Z F, Liu Q, Tang G F, et al. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images[J]. International Journal of Remote Sensing, 2015, 36(2): 618-644. [NASA ADS] [CrossRef] [Google Scholar]
  37. Chen X, Liu J H, Xu F, et al. A novel method of aircraft detection under complex background based on circular intensity filter and rotation invariant feature[J]. Sensors, 2022, 22(1): 319. [Google Scholar]
  38. 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. [CrossRef] [Google Scholar]
  39. Yang C C, Ma J Y, Zhang M F, et al. Multiscale facet model for infrared small target detection[J]. Infrared Physics and Technology, 2014, 67: 202-209. [NASA ADS] [CrossRef] [Google Scholar]
  40. Xu S, Fang T, Li D R, et al. Object classification of aerial images with bag-of-visual words[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(2): 366-370. [NASA ADS] [CrossRef] [Google Scholar]
  41. 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. [NASA ADS] [CrossRef] [Google Scholar]
  42. Guo W, Yang W, Zhang H J, et al. Geospatial object detection in high resolution satellite images based on multi-scale convolutional neural network[J]. Remote Sensing, 2018, 10(1): 131. [NASA ADS] [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.