logo

SCIENTIA SINICA Informationis, Volume 49 , Issue 12 : 1626-1639(2019) https://doi.org/10.1360/SSI-2019-0093

Progress of deep learning-based target recognition in radar images

More info
  • ReceivedMay 8, 2019
  • AcceptedJun 12, 2019
  • PublishedDec 10, 2019

Abstract


Funded by

国家自然科学基金(61701478)


References

[1] Dudgeon D E, Lacoss R T. An overview of automatic target recognition. Lincoln Lab J, 1993, 6: 3--10. Google Scholar

[2] Pan Z, Liu L, Qiu X. Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode.. Sensors, 2017, 17: 1578 CrossRef PubMed Google Scholar

[3] An Q, Pan Z, You H. Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network.. Sensors, 2018, 18: 334 CrossRef PubMed Google Scholar

[4] Kang M, Ji K F, Leng X G. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection. Remote Sens, 2017, 9: 860 CrossRef ADS Google Scholar

[5] Liu L, Chen G W, Pan Z X, et al. Inshore ship detection in SAR images based on deep neural networks. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2018. 25--28. Google Scholar

[6] An Q Z, Pan Z X, Liu L, et al. DRBox-v2: an improved detector with rotatable boxes for target detection in SAR images. IEEE Trans Geosci Remote Sens, 2019, 57: 8333--8349. Google Scholar

[7] Kuang G Y, Ji K F, Su Y, et al. A survey of researches on SAR ATR. J Image Graph, 2003, 8: 1115--1120. Google Scholar

[8] Hu F M, Zhang P, Yang R L, et al. SAR target recognition based on Gabor filter and sub-block statistical feature. In: Proceedings of IET International Radar Conference, 2009. Google Scholar

[9] Papson S, Narayanan R M. Classification via the Shadow Region in SAR Imagery. IEEE Trans Aerosp Electron Syst, 2012, 48: 969-980 CrossRef ADS Google Scholar

[10] Zhang H, Tian X, Wang C. Merchant Vessel Classification Based on Scattering Component Analysis for COSMO-SkyMed SAR Images. IEEE Geosci Remote Sens Lett, 2013, 10: 1275-1279 CrossRef ADS Google Scholar

[11] Chen J H, Zhang B, Wang C. Backscattering Feature Analysis and Recognition of Civilian Aircraft in TerraSAR-X Images. IEEE Geosci Remote Sens Lett, 2015, 12: 796-800 CrossRef ADS Google Scholar

[12] Pan Z, Qiu X, Huang Z. Airplane Recognition in TerraSAR-X Images via Scatter Cluster Extraction and Reweighted Sparse Representation. IEEE Geosci Remote Sens Lett, 2017, 14: 112-116 CrossRef ADS Google Scholar

[13] Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition. IEEE Trans Aerosp Electron Syst, 2001, 37: 643-654 CrossRef ADS Google Scholar

[14] Margarit G, Tabasco A. Ship Classification in Single-Pol SAR Images Based on Fuzzy Logic. IEEE Trans Geosci Remote Sens, 2011, 49: 3129-3138 CrossRef ADS Google Scholar

[15] Srinivas U, Monga V, Raj R G. SAR Automatic Target Recognition Using Discriminative Graphical Models. IEEE Trans Aerosp Electron Syst, 2014, 50: 591-606 CrossRef ADS Google Scholar

[16] Cui Z Y, Cao Z J, Yang J Y, et al. SAR target recognition using nonnegative matrix factorization with L$_{1/2}~$ constraint. In: Proceedings of IEEE Radar Conference, 2014. 382--386. Google Scholar

[17] Dong G, Kuang G, Wang N. SAR Target Recognition via Joint Sparse Representation of Monogenic Signal. IEEE J Sel Top Appl Earth Observations Remote Sens, 2015, 8: 3316-3328 CrossRef ADS Google Scholar

[18] Pan Z X, Liu L, Lei B. Identity regularized sparse representation for automatic target recognition in SAR images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2018. 37--40. Google Scholar

[19] Huang Y, Peia J, Yanga J. Neighborhood Geometric Center Scaling Embedding for SAR ATR. IEEE Trans Aerosp Electron Syst, 2014, 50: 180-192 CrossRef ADS Google Scholar

[20] Liu X, Huang Y L, Pei J F. Sample discriminant analysis for SAR ATR. IEEE Geosci Remote Sens Lett, 2014, 11: 2120-2124 CrossRef ADS Google Scholar

[21] Dong G G, Kuang G Y. Target Recognition in SAR Images via Classification on Riemannian Manifolds. IEEE Geosci Remote Sens Lett, 2015, 12: 199-203 CrossRef ADS Google Scholar

[22] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012. 1097--1105. Google Scholar

[23] Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. Google Scholar

[24] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 770--778. Google Scholar

[25] Xu F, Wang H P, Jin Y Q. Deep learning as applied in SAR target recognition and terrain classification. Journal of Radars, 2017, 6: 136--148. Google Scholar

[26] Keydel E R, Lee S W, Moore J T. MSTAR extended operating conditions: a tutorial. In: Proceedings of SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, 1996. 228--242. Google Scholar

[27] Huang L, Liu B, Li B. OpenSARShip: A Dataset Dedicated to Sentinel-1 Ship Interpretation. IEEE J Sel Top Appl Earth Observations Remote Sens, 2018, 11: 195-208 CrossRef ADS Google Scholar

[28] Ding J, Chen B, Liu H. Convolutional Neural Network With Data Augmentation for SAR Target Recognition. IEEE Geosci Remote Sens Lett, 2016, : 1-5 CrossRef Google Scholar

[29] Wagner S A. SAR ATR by a combination of convolutional neural network and support vector machines. IEEE Trans Aerosp Electron Syst, 2016, 52: 2861-2872 CrossRef ADS Google Scholar

[30] Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014. 2672--2680. Google Scholar

[31] Guo J, Lei B, Ding C. Synthetic Aperture Radar Image Synthesis by Using Generative Adversarial Nets. IEEE Geosci Remote Sens Lett, 2017, 14: 1111-1115 CrossRef ADS Google Scholar

[32] Bao X J, Pan Z X, Liu L, et al. SAR image simulation by generative adversarial networks. In: Proceedings of IGARSS, 2019. 9995--9998. Google Scholar

[33] Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. 2015,. arXiv Google Scholar

[34] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN. 2017,. arXiv Google Scholar

[35] Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of Wasserstein GANs. 2017,. arXiv Google Scholar

[36] Cui Z, Zhang M, Cao Z. Image Data Augmentation for SAR Sensor via Generative Adversarial Nets. IEEE Access, 2019, 7: 42255-42268 CrossRef Google Scholar

[37] Auer S J. 3D synthetic aperture radar simulation for interpreting complex urban reflection scenarios. Dissertation for Ph.D. Degree. Munich: Technical University of Munich, 2011. Google Scholar

[38] Niu S R, Qiu X L, Peng L X, et al. Parameter prediction method of SAR target simulation based on convolutional neural networks. In: Proceedings of the 12th European Conference on Synthetic Aperture Radar, 2018. 106--110. Google Scholar

[39] Liu L, Pan Z X, Qiu X L, et al. SAR target classification with CycleGAN transferred simulated samples. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2018. 4411--4414. Google Scholar

[40] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 2242--2251. Google Scholar

[41] Sun Z J, Xue L, Xu Y M. Recognition of SAR target based on multilayer auto-encoder and SNN. Int J Innov Comput Inf Control, 2013, 9: 4331--4341. Google Scholar

[42] Li X, Li C S, Wang P B, et al. SAR ATR based on dividing CNN into CAE and SNN. In: Proceedings of the 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), 2015. 676--679. Google Scholar

[43] Geng J, Fan J, Wang H. High-Resolution SAR Image Classification via Deep Convolutional Autoencoders. IEEE Geosci Remote Sens Lett, 2015, 12: 2351-2355 CrossRef ADS Google Scholar

[44] Bentes C, Velotto D, Lehner S. Target classification in oceanographic SAR images with deep neural networks: architecture and initial results. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015. 3703--3706. Google Scholar

[45] Chen S, Wang H, Xu F. Target Classification Using the Deep Convolutional Networks for SAR Images. IEEE Trans Geosci Remote Sens, 2016, 54: 4806-4817 CrossRef ADS Google Scholar

[46] Lin Z, Ji K, Kang M. Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data. IEEE Geosci Remote Sens Lett, 2017, 14: 1091-1095 CrossRef ADS Google Scholar

[47] Shang R, Wang J, Jiao L. SAR Targets Classification Based on Deep Memory Convolution Neural Networks and Transfer Parameters. IEEE J Sel Top Appl Earth Observations Remote Sens, 2018, 11: 2834-2846 CrossRef ADS Google Scholar

[48] Song Q, Xu F. Zero-Shot Learning of SAR Target Feature Space With Deep Generative Neural Networks. IEEE Geosci Remote Sens Lett, 2017, 14: 2245-2249 CrossRef ADS Google Scholar

[49] Xu B, Chen B, Liu H W, et al. Attention-based recurrent neural network model for radar high-resolution range profile target recognition. J Electron Inform Technol, 2016, 38: 2988--2995. Google Scholar

[50] Pan M, Jiang J, Kong Q. Radar HRRP Target Recognition Based on t-SNE Segmentation and Discriminant Deep Belief Network. IEEE Geosci Remote Sens Lett, 2017, 14: 1609-1613 CrossRef ADS Google Scholar

[51] Pei J, Huang Y, Sun Z. Multiview Synthetic Aperture Radar Automatic Target Recognition Optimization: Modeling and Implementation. IEEE Trans Geosci Remote Sens, 2018, 56: 6425-6439 CrossRef ADS Google Scholar

[52] He Z, Xiao H, Tian Z. Multi-View Tensor Sparse Representation Model for SAR Target Recognition. IEEE Access, 2019, 7: 48256-48265 CrossRef Google Scholar

[53] Zhang F, Hu C, Yin Q. Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition. IEEE Access, 2017, 5: 26880-26891 CrossRef Google Scholar

[54] Zhou Y, Wang H, Xu F. Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks. IEEE Geosci Remote Sens Lett, 2016, 13: 1935-1939 CrossRef ADS Google Scholar

[55] Zhang Z, Wang H, Xu F. Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification. IEEE Trans Geosci Remote Sens, 2017, 55: 7177-7188 CrossRef ADS Google Scholar

[56] Shang R, Wang G, A. Okoth M. Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification. Remote Sens, 2019, 11: 522 CrossRef ADS Google Scholar

[57] Chen H, Zhang F, Tang B. Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition. Remote Sens, 2018, 10: 1618 CrossRef ADS Google Scholar

[58] Shao J, Qu C, Li J. A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification.. Sensors, 2018, 18: 3039 CrossRef PubMed Google Scholar

[59] Min R, Lan H, Cao Z. A Gradually Distilled CNN for SAR Target Recognition. IEEE Access, 2019, 7: 42190-42200 CrossRef Google Scholar

[60] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. 2015,. arXiv Google Scholar

[61] Kang C Y, He C. SAR image classification based on the multi-layer network and transfer learning of mid-level representations. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016. 1146--1149. Google Scholar

[62] Marmanis D, Yao W, Adam F, et al. Artificial generation of big data for improving image classification: a generative adversarial network approach on SAR data. 2017,. arXiv Google Scholar

[63] Huang Z, Pan Z, Lei B. Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sens, 2017, 9: 907 CrossRef ADS Google Scholar

[64] Malmgren-Hansen D, Kusk A, Dall J. Improving SAR Automatic Target Recognition Models With Transfer Learning From Simulated Data. IEEE Geosci Remote Sens Lett, 2017, 14: 1484-1488 CrossRef ADS Google Scholar

[65] Huang Z L, Pan Z X, Lei B. What, where and how to transfer in SAR target recognition based on deep CNNs. IEEE Trans Geosci Remote Sens, 2019. doi: 10.1109/TGRS.2019.2947634. Google Scholar

[66] Borgwardt K M, Gretton A, Rasch M J. Integrating structured biological data by Kernel Maximum Mean Discrepancy.. Bioinformatics, 2006, 22: e49-e57 CrossRef PubMed Google Scholar

[67] Pan Z X, Bao X J, Wang B W, et al. Siamese network based metric learning for SAR target classification. In: Proceedings of IGARSS, 2019. 1342--1345. Google Scholar

[68] Bucher M, Herbin S, Jurie F. Hard negative mining for metric learning based zero-shot classification. In: Proceedings of European Conference on Computer Vision, 2016. 524--531. Google Scholar

[69] Chen G W, Liu L, Hu W L, et al. Semi-supervised object detection in remote sensing images using generative adversarial networks. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2018. 2503--2506. Google Scholar

[70] Salimans T, Goodfellow I, Zaremba W, et al. Improved techniques for training GANs. In: Proceedings of NIPS, 2016. 2234--2242. Google Scholar

[71] Zhou D Y, Bousquet O, Lal T N, et al. Learning with local and global consistency. In: Proceedings of NIPS, 2004. 284--291. Google Scholar

[72] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In: Proceedings of NIPS, 2017. Google Scholar

[73] Thys S, Van Ranst W, Goedemé T. Fooling automated surveillance cameras: adversarial patches to attack person detection. 2019,. arXiv Google Scholar

  • Figure 1

    (Color online) Target recognition model architecture based on semi-supervised generative adversarial networks

  • Figure 2

    Samples generated from the generator. Generated samples after (a) 0 iterations, (b) 10000 iterations, andprotect łinebreak (c) 20000 iterations

  • Table 1   Comparison of recognition rates (%) of various methods
    SOC-10 SOC-20 SOC-30 EOC
    SGraph 56.45 69.86 72.49 62.90
    CNN 70.76 81.36 87.34 70.89
    SSGANs 85.65 89.86 93.07 75.50