Anterior|Siguiente

Multiscale aircraft detection in optical remote sensing imagery based on advanced Faster R-CNN

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    University of Chinese Academy of Sciences, School of Electronic, Electrical and Communication Engineering, Beijing 100049, China

  • Email：shamm2017@radi.ac.cn
  • Introduction：沙苗苗，研究方向为遥感图像处理、目标检测。E-mail： shamm2017@radi.ac.cn
SHA Miaomiao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：liyu@radi.ac.cn
  • Introduction：李宇，研究方向为遥感图像处理。E-mail：liyu@radi.ac.cn
LI Yu1*,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI An1

resumen

Aircraft detection from optical imagery is a significant application in remote sensing. Traditional methods based on corner points or shape of the aircraft can only generate shallow features with limited representative ability. These methods are insufficient for detecting aircraft in remote sensing imagery under complex and diverse circumstances. Current methods based on CNNs, especially Faster R-CNN, have improved the detection performance greatly with its magnificent feature extraction ability. However, detecting aircraft on a single-scale feature map is unsuitable for multiscale aircraft in remote sensing imagery. After several pooling operations on a single-scale feature map, the feature map loses its precise details and small target that corresponds to a smaller area in the feature map. Thus, aircraft detection may result in low target positioning accuracy and target missing.An advanced Faster R-CNN is presented by constructing a multiscale feature extraction network using multistage fusion structure to detect aircraft with multiple scales. The promoted network produces features of higher resolution by upsampling deep feature maps. These features are then enhanced with shallow features at the same scale. After this modification, we end up with four feature maps F2, F3, F4, and F5, which have different scales. The structure combines the high-level semantic information with the low-level detailed information. Thus, the generated multiscale feature maps have high positioning accuracy and good distinguishability. In addition, because the original RPN anchors are extremely large to cover the range of aircraft sizes in remote sensing imagery, we select suitable RPN anchor parameters for aircraft detection, i.e., anchor size of 322 for the larger-scale feature map F2, 642 for the large-scale F3, 1282 is set for the F4, and 2562 for the small-scale F5. With these settings, the RPN can generate proposals, which can cover the aircraft of multiple scales. Finally, these proposals are assigned to their corresponding feature map, and we use the classification and regression network to obtain our final detection results.The experiment was carried out on RSOD dataset, in which only the aircraft dataset was used for training, validation, and testing. Comparison of detection performance with different anchor scales showed that anchor scales greatly affect detection accuracy, and our selection of anchor scales is suitable for the dataset. Three feature extraction networks (ZF, VGG-16, and ResNet-50) were modified based on Faster R-CNN using multistage fusion structure. The experiment showed that the modification can effectively improve the model’s ability of detecting multiscale aircraft. Compared with models without the modification, AP increased by 11.34%, 9.87%, and 1.66% for the three networks. The qualitative and quantitative results also showed that this modification can generate adaptive detection box. The experiment results on Beijing Capital International Airport GF-2 imagery showed that this method performs well in different remote sensing imagery, in which most airplanes in the airport were detected successfully.We can draw the following conclusions: (1) the proposed method is suitable for multiscale aircraft detection, and it can generate detection box consistent with the scale of multiscale aircraft targets while reducing missing targets; (2) correction of the RPN candidate region scale improves the accuracy of aircraft detection in remote sensing imagery; (3) the method has good generalization ability.

palabra clave

remote sensing image;object detection;Faster R-CNN;multiple stages fusion structure;multi-scale

References

  1. 1.
    Cai D, Chen Y M and Wei W. 2014. Study on aircraft recognition in multi-spectral remote sensing image based on skeleton characteristics analysis. Bulletin of Surveying and Mapping, 2: 50-54, 71
  2. 2.
    Girshick R. 2015. Fast R-CNN//2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 1440-1448
  3. 3.
    Girshick R, Donahue J, Darrell T and Malik J. 2013. Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv:1311.2524
  4. 4.
    He K M, Zhang X Y, Ren S Q and Sun J. 2014. Spatial pyramid pooling in deep convolutional networks for visual recognition//Computer Vision - ECCV 2014. Switzerland: Springer, 8681: 346-361
  5. 5.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770-778
  6. 6.
    Krizhevsky A, Sutskever I and Hinton G E. 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6): 84-90
  7. 7.
    Li W, Xiang S M, Wang H B and Pan C H. 2011. Robust airplane detection in satellite images//2011 18th IEEE International Conference on Image Processing. Brussels, Belgium: IEEE: 2821-2824
  8. 8.
    Li Y B, Zhang S Y, Zhao J F and Tan W A. 2019. Aircraft detection in remote sensing images based on deep convolutional neural network. IOP Conference Series: Earth and Environmental Science, 252(5): 052122
  9. 9.
    Lin T Y, Dollár P, Girshick R, He K M, Hariharan B and Belongie S. 2017. Feature pyramid networks for object detection//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE: 936-944
  10. 10.
    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
  11. 11.
    Qiu J B, Li S J and Wang W. 2011. A new approach to detect aircrafts in remote sensing images based on corner and edge information fusion. Microelectronics and Computer, 28(9): 214-216
  12. 12.
    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
  13. 13.
    Ren Y, Zhu C R and Xiao S P. 2018. Deformable faster R-CNN with aggregating multi-layer features for partially occluded object detection in optical remote sensing images. Remote Sensing, 10(9): 1470
  14. 14.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
  15. 15.
    Wang H Z, Gong Y C, Wang Y, Wang L F and Pan C H. 2017. DeepPlane: a unified deep model for aircraft detection and recognition in remote sensing images. Journal of Applied Remote Sensing, 11(4): 042606
  16. 16.
    Wang Y, Yang Y, Wang B S, Wang T, Bo X H and Wang C Y. 2019. Building segmentation in high-resolution remote sensing image through deep neural network and conditional random fields. Journal of Remote Sensing, 23(6): 1194-1208
  17. 17.
    Zeiler M D and Fergus R. 2014. Visualizing and understanding convolutional networks//Fleet D, Pajdla T, Schiele B and Tuytelaars T, eds. Computer Vision - ECCV 2014. Switzerland: Springer: 818-833
  18. 18.
    Zhang H Q, Liu X Y, Yang S and Li Y. 2017. Retrieval of remote sensing images based on semisupervised deep learning. Journal of Remote Sensing, 21(3): 406-414
  19. 19.
    Zhang K, Hei B Q, Zhou Z and Li S Y. 2018. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification. Journal of Remote Sensing, 22(1): 87-96
  20. 20.
    Zhao A, Fu K, Sun H, Sun X, Li F, Zhang D B and Wang H Q. 2017. An effective method based on ACF for aircraft detection in remote sensing images. IEEE Geoscience and Remote Sensing Letters, 14(5): 744-748

Leer el texto completo

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website