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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 48, 2022 - Issue 6
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Research Articles

Ship Detection in SAR Images via Cross-Attention Mechanism

Détection de navires dans des images RSO via le mécanisme de l’attention croisée

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Pages 764-778 | Received 27 Feb 2022, Accepted 01 Aug 2022, Published online: 14 Sep 2022

References

  • Ao, W., Xu, F., Li, Y., and Wang, H. 2018. “Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(No. 2): pp. 536–550. doi:10.1109/JSTARS.2017.2787573.
  • Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. 2020. “Yolov4: Optimal speed and accuracy of object detection.” arXiv Preprint arXiv:2004.10934.
  • Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. 2020. “End-to-end object detection with transformers.” In European Conference on Computer Vision, pp. 213–229. Cham: Springer.
  • Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., and Lin, D. 2019. “MMDetection: Open mmlab detection toolbox and benchmark.” arXiv Preprint arXiv:1906.07155.
  • Chen, S., Zhan, R., Wang, W., and Zhang, J. 2021. “Learning slimming SAR ship object detector through network pruning and knowledge distillation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14: pp. 1267–1282. doi:10.1109/JSTARS.2020.3041783.
  • Chen, Y., Yu, J., and Xu, Y. 2020. “SAR ship target detection for SSDv2 under complex backgrounds.” Proc. Int. Conf. Comput. Vis., Image Deep Learn, pp. 560–565.
  • Chollet, F. 2017. “Xception: Deep learning with depthwise separable convolutions.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258.
  • Crisp, D.J. 2004. The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery. Organic letters.
  • Cui, Z., Li, Q., Cao, Z., and Liu, N. 2019. “Dense attention pyramid networks for multi-scale ship detection in SAR images.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 11): pp. 8983–8997. doi:10.1109/TGRS.2019.2923988.
  • Davari, N., Akbarizadeh, G., and Mashhour, E. 2021. “Intelligent diagnosis of incipient fault in power distribution lines based on corona detection in UV-visible videos.” IEEE Transactions on Power Delivery, Vol. 36(No. 6): pp. 3640–3648. doi:10.1109/TPWRD.2020.3046161.
  • Deng, Z., Sun, H., Zhou, S., and Zhao, J. 2019. “Learning deep ship detector in SAR images from scratch.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 6): pp. 4021–4039. doi:10.1109/TGRS.2018.2889353.
  • Everingham, M., Zisserman, A., Williams, C.K., Van Gool, L., Allan, M., Bishop, C.M., and Zhang, J. 2008. The PASCAL visual object classes challenge 2007 (VOC2007) results.
  • Ferrentino, E., Nunziata, F., Marino, A., Migliaccio, M., and Li, X. 2019. “Detection of wind turbines in intertidal areas using SAR polarimetry.” IEEE Geoscience and Remote Sensing Letters, Vol. 16(No. 10): pp. 1516–1520. doi:10.1109/LGRS.2019.2905714.
  • Fu, J., Sun, X., Wang, Z., 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, Vol. 59(No. 2): pp. 1331–1344. doi:10.1109/TGRS.2020.3005151.
  • Ghiasi, G., Lin, T.Y., and Le, Q.V. 2019. “NAS-FPN: Learning scalable feature pyramid architecture for object detection.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7036–7045.
  • Gierull, C.H. 2018. “Demystifying the capability of sublook correlation techniques for vessel detection in SAR imagery.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 4): pp. 2031–2042.
  • Gierull, C.H., and Sikaneta, I. 2018. “A compound-plus-noise model for improved vessel detection in non-Gaussian SAR imagery.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 56(No. 3): pp. 1444–1453. doi:10.1109/TGRS.2017.2763089.
  • He, K., Zhang, X., Ren, S., and Sun, J. 2016. “Deep residual learning for image recognition.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778.
  • Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. 2017. “Densely connected convolutional networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708.
  • Iervolino, P., and Guida, R. 2017. “A novel ship detector based on the generalized-likelihood ratio test for SAR imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10(No. 8): pp. 3616–3630. doi:10.1109/JSTARS.2017.2692820.
  • Kaplan, L.M. 2001. “Improved SAR target detection via extended fractal features.” IEEE Transactions on Aerospace and Electronic Systems, Vol. 37(No. 2): pp. 436–451. doi:10.1109/7.937460.
  • Lang, H., Xi, Y., and Zhang, X. 2019. “Ship detection in high-resolution SAR images by clustering spatially enhanced pixel descriptor.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 57(No. 8): pp. 5407–5423. doi:10.1109/TGRS.2019.2899337.
  • Law, H., and Deng, J. 2020. “CornerNet: Detecting objects as paired keypoints.” International Journal of Computer Vision, Vol. 128(No. 3): pp. 642–656. doi:10.1007/s11263-019-01204-1.
  • Leng, X., Ji, K., Xing, X., Zhou, S., and Zou, H. 2018. “Area ratio invariant feature group for ship detection in SAR imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(No. 7): pp. 2376–2388. doi:10.1109/JSTARS.2018.2820078.
  • Li, T., Liu, Z., Xie, R., and Ran, L. 2018. “An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 11(No. 1): pp. 184–194. doi:10.1109/JSTARS.2017.2764506.
  • Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. 2017. “Feature pyramid networks for object detection.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125.
  • Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., and Zitnick, C.L. 2014. “Microsoft coco: Common objects in context.” In European Conference on Computer Vision, pp. 740–755. Cham: Springer.
  • Lin, T., Goyal, P., Girshick, R., He, K., and Dollar, P. 2020. “Focal loss for dense object detection.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42(No. 2): pp. 318–327.
  • Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. 2018. “Path aggregation network for instance segmentation.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768.
  • Liu, T., Yang, Z., Marino, A., Gao, G., and Yang, J. 2020. “Robust CFAR detector based on truncated statistics for polarimetric synthetic aperture radar.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 58(No. 9): pp. 6731–6747. doi:10.1109/TGRS.2020.2979252.
  • Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., and Guo, B. 2021. “Swin transformer: Hierarchical vision transformer using shifted windows.” Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10012–10022.
  • Long, J., Shelhamer, E., and Darrell, T. 2015. “Fully convolutional networks for semantic segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39(No. 4): pp. 640–651.
  • Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Dollar, P. 2020. “Designing network design spaces.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10428–10436.
  • Schwegmann, C.P., Kleynhans, W., and Salmon, B.P. 2015. “Manifold adaptation for constant false alarm rate ship detection in South African oceans.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8(No. 7): pp. 3329–3337. doi:10.1109/JSTARS.2015.2417756.
  • Sharifzadeh, F., Akbarizadeh, G., and Kavian, Y.S. 2019. “Ship classification in SAR images using a new hybrid CNN-MLP classifier.” Journal of the Indian Society of Remote Sensing, Vol. 47(No. 4): pp. 551–562. doi:10.1007/s12524-018-0891-y.
  • Song, S., Xu, B., Li, Z., and Yang, J. 2016. “Ship detection in SAR imagery via variational Bayesian inference.” IEEE Geoscience and Remote Sensing Letters, Vol. 13(No. 3): pp. 319–323.
  • Sun, K., Xiao, B., Liu, D., and Wang, J. 2019. “Deep high-resolution representation learning for human pose estimation.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703.
  • Tello, M., Lopez-Martinez, C., and Mallorqui, J.J. 2005. “A novel algorithm for ship detection in SAR imagery based on the wavelet transform.” IEEE Geoscience and Remote Sensing Letters, Vol. 2(No. 2): pp. 201–205. doi:10.1109/LGRS.2005.845033.
  • Tian, Z., Shen, C., Chen, H., and He, T. 2020. “FCOS: A simple and strong anchor-free object detector.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44(No. 4): pp. 1922–1933. doi:10.1109/TPAMI.2020.3032166.
  • Wang, J., Chen, K., Xu, R., Liu, Z., Loy, C. C., and Lin, D. 2019. “Carafe: Content-aware reassembly of features.” Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3007–3016.
  • Wang, S., Wang, M., Yang, S., and Jiao, L. 2016. “New hierarchical saliency filtering for fast ship detection in high-resolution SAR images.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55(No. 1): pp. 351–362.
  • Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. 2017. “Aggregated residual transformations for deep neural networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500.
  • Zhang, H., Chang, H., Ma, B., Wang, N., and Chen, X. 2020. “Dynamic R-CNN: Towards high quality object detection via dynamic training.” In European Conference on Computer Vision, pp. 260–275. Cham: Springer.
  • Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., et al. 2021. “Sar ship detection dataset (SSDD): Official release and comprehensive data analysis.” Remote Sensing, Vol. 13(No. 18): pp. 3690. doi:10.3390/rs13183690.
  • Zhang, T., Zhang, X., Shi, J., Wei, S., Wang, J., and Li, J. 2020. “Balanced feature pyramid network for ship detection in synthetic aperture radar images.” 2020 IEEE Radar Conference (RadarConf20), pp. 1–5. IEEE. doi:10.1109/RadarConf2043947.2020.9266519.
  • Zheng, M., Gao, P., Zhang, R., Li, K., Wang, X., Li, H., and Dong, H. 2020. “End-to-end object detection with adaptive clustering transformer.” arXiv Preprint arXiv:2011.09315. https://arxiv.org/abs/2011.09315.
  • Zhou, X., Wang, D., and Krahenbuhl, P. 2019. “Objects as points.” arXiv:1904.07850v2. https://arxiv.org/abs/1904.07850.
  • Zhu, M., Hu, G., Zhou, H., Lu, C., Zhang, Y., Yue, S., and Li, Y. 2020. “Rapid ship detection in SAR images based on YOLOv3.” 2020 5th International Conference on Communication, Image and Signal Processing (CCISP), pp. 214–218. IEEE. doi:10.1109/CCISP51026.2020.9273476.

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