Accesso libero

One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition

INFORMAZIONI SU QUESTO ARTICOLO

Cita

1. Novak, L. M., G. J. Owirka, W. S. Brower, A. L. Weaver. The Automatic Target Recognition System in SAIP. – Lincoln Laboratory Journal, Vol. 10, 1983, No 2. Search in Google Scholar

2. Owirka, G. J., A. L. Weaver, L. M. Novak. Performance of a Multiresolution Classifier Using Enhanced-Resolution SAR Data. – Radar Sensor Technology II, Vol. 3066, 1997, pp. 90-100.10.1117/12.276091 Search in Google Scholar

3. Zhang, K., W. Zuo, L. Zhang. FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising. – IEEE Transactions on Image Processing, Vol. 27, 2018, No 9, pp. 4608-4622.10.1109/TIP.2018.283989129993717 Search in Google Scholar

4. Mu, N., X. Xu, X. Zhang, H. Zhang. Salient Object Detection Using a Covariance-Based CNN Model in Low-Contrast Images. – Neural Computing and Applications, Vol. 29, 2018, No 8, pp. 181-192.10.1007/s00521-017-2870-6 Search in Google Scholar

5. Xie, F., Q. Gao, C. Jin, F. Zhao. Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning. – Remote Sensing, Vol. 13, 2021, No 5, pp. 930.10.3390/rs13050930 Search in Google Scholar

6. Deng, J., W. Dong, R. Socher, L. J. Li, K. Li, L. Fei-Fei. Imagenet: A Large-Scale Hierarchical Image Database. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255.10.1109/CVPR.2009.5206848 Search in Google Scholar

7. Morgan, D. A. E. Deep Convolutional Neural Networks for ATR from SAR Imagery. – In: Algorithms for Synthetic Aperture Radar Imagery XXII. Vol. 9475. 2015, p. 94750F.10.1117/12.2176558 Search in Google Scholar

8. Gao, F., T. Huang, J. Sun, J. Wang, A. Hussain, E. Yang. A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network. – Cognitive Computation, Vol. 11, 2019, No 6, pp. 809-824.10.1007/s12559-018-9563-z Search in Google Scholar

9. Tian, Z., L. Wang, R. Zhan, J. Hu, J. Zhang. Classification via Weighted Kernel CNN: Application to SAR Target Recognition. – International Journal of Remote Sensing, Vol. 39, 2018, No 23, pp. 9249-9268.10.1080/01431161.2018.1531317 Search in Google Scholar

10. Zhang, J., M. Xing, Y. Xie. FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, 2020, No 3, pp. 2174-2187.10.1109/TGRS.2020.3003264 Search in Google Scholar

11. Guo, Y., Z. Pan, M. Wang, J. Wang, W. Yang. Learning Capsules for SAR Target Recognition. – IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 13, 2020, pp. 4663-4673.10.1109/JSTARS.2020.3015909 Search in Google Scholar

12. Krizhevsky, A., I. Sutskever, G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. – Advances in Neural Information Processing Systems, Vol. 25, 2017, pp. 1097-1105. Search in Google Scholar

13. Simonyan, K., A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition. – arXiv Preprint arXiv:1409.1556, 2014. Search in Google Scholar

14. Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich. Going Deeper with Convolutions. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1-9.10.1109/CVPR.2015.7298594 Search in Google Scholar

15. He, K., X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778.10.1109/CVPR.2016.90 Search in Google Scholar

16. Cheng, Y., D. Wang, P. Zhou, T. Zhang. A Survey of Model Compression and Acceleration for Deep Neural Networks. – arXiv Preprint arXiv:1710.09282, 2017. Search in Google Scholar

17. Ding, X., X. Zhang, J. Han, G. Ding. RepMLP: Re-Parameterizing Convolutions into Fully-Connected Layers for Image Recognition. – arXiv Preprint arXiv:2105.01883, 2021. Search in Google Scholar

18. Ding, X., X. Zhang, N. Ma, J. Han, G. Ding, J. Sun. RepVGG: Making VGG-style Convnets Great Again. – In: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 13733-13742.10.1109/CVPR46437.2021.01352 Search in Google Scholar

19. Chen, S., H. Wang, F. Xu, Y. Q. Jin. Target Classification Using the Deep Convolutional Networks for SAR Images. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, 2016, No 8, pp. 4806-4817.10.1109/TGRS.2016.2551720 Search in Google Scholar

20. Wagner, S. A. SAR ATR by a Combination of Convolutional Neural Network and Support Vector Machines. – IEEE Transactions on Aerospace and Electronic Systems, Vol. 52, 2016, No 6, pp. 2861-2872.10.1109/TAES.2016.160061 Search in Google Scholar

21. Zhong, C., X. Mu, X. He, J. Wang, M. Zhu. SAR Target Image Classification Based on Transfer Learning and Model Compression. – IEEE Geoscience and Remote Sensing Letters, Vol. 16, 2021, No 3, pp. 412-416.10.1109/LGRS.2018.2876378 Search in Google Scholar

22. Liu, Y., F. Zhang, F. Ma, Q. Yin, Y. Zhou. Incremental Multitask SAR Target Recognition with Dominant Neuron Preservation. – In: Proc. of IEEE International Geoscience And Remote Sensing Symposium (IGARSS’20), 2020, pp. 754-757.10.1109/IGARSS39084.2020.9323212 Search in Google Scholar

23. Chen, S., R. Zhan, W. Wang, J. Zhang. Learning Slimming SAR Ship Object Detector through Network Pruning and Knowledge Distillation. – IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, No 14, pp. 1267-1282.10.1109/JSTARS.2020.3041783 Search in Google Scholar

24. Chen, H., F. Zhang, B. Tang, Q. Yin, X. Sun. Slim and Efficient Neural Network Design for Resource-Constrained SAR Target Recognition. – Remote Sensing, Vol. 10, 2018, No 10, pp. 1618.10.3390/rs10101618 Search in Google Scholar

25. Yu, M., G. Dong, H. Fan, G. Kuang. SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation. – Remote Sensing, Vol. 10, 2018, No 2, pp. 211.10.3390/rs10020211 Search in Google Scholar

26. Min, R., H. Lan, Z. Cao, Z. Cui. A Gradually Distilled CNN for SAR Target Recognition. – IEEE Access, Vol. 7, 2019, pp. 42190-42200.10.1109/ACCESS.2019.2906564 Search in Google Scholar

27. Zhang, F., Y. Liu, Y. Zhou, Q. Yin, H. C. Li. A Lossless Lightweight CNN Design for SAR Target Recognition. – Remote Sensing Letters, Vol. 11, 2020, No 5, pp. 485-494.10.1080/2150704X.2020.1730472 Search in Google Scholar

28. Peng, L., M. Liu, X. Liu, L. Dong, M. Hui, Y. Zhao. SAR Image Classification Based on CNN in Real and Simulation Datasets. – In: Proc. of 9th International Conference on Graphic and Image Processing, 2018, p. 106152V. Search in Google Scholar

29. Ding, J., B. Chen, H. Liu, M. Huang. Convolutional Neural Network with Data Augmentation for SAR Target Recognition. – IEEE Geoscience and Remote Sensing Letters, Vol. 13, 2016, No 3, pp. 364-368.10.1109/LGRS.2015.2513754 Search in Google Scholar

30. Pei, J., Y. Huang, W. Huo, Y. Zhang, J. Yang, T. S. Yeo. SAR Automatic Target Recognition Based on Multiview Deep Learning Framework. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, 2017, No 4, pp. 2196-2210.10.1109/TGRS.2017.2776357 Search in Google Scholar

31. Yan, Y. Convolutional Neural Networks Based on Augmented Training Samples for Synthetic Aperture Radar Target Recognition. – Journal of Electronic Imaging, Vol. 27, 2018, No 2, pp. 023024.10.1117/1.JEI.27.2.023024 Search in Google Scholar

32. Wang, K., G. Zhang, H. Leung. SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning. – IEEE Access, Vol. 7, 2019, pp. 153391-153399.10.1109/ACCESS.2019.2948618 Search in Google Scholar

33. Malmgren-Hansen, D., A. Kusk, J. Dall, A. A. Nielsen, R. Engholm, H. Skriver. Improving SAR Automatic Target Recognition Models with Transfer Learning from Simulated Data. – IEEE Geoscience and Remote Sensing Letters, Vol. 14, 2017, No 9, pp. 1484-1488.10.1109/LGRS.2017.2717486 Search in Google Scholar

34. Wang, Z., L. Du, J. Mao, B. Liu, D. Yang. SAR Target Detection Based on SSD with Data Augmentation and Transfer Learning. – IEEE Geoscience and Remote Sensing Letters, Vol. 16, 2018, No 1, pp. 150-154.10.1109/LGRS.2018.2867242 Search in Google Scholar

35. Huang, Z., C. O. Dumitru, Z. Pan, B. Lei, M. Datcu. Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning. – IEEE Geoscience and Remote Sensing Letters, Vol. 18, 2020, No 1, pp. 107-111.10.1109/LGRS.2020.2965558 Search in Google Scholar

36. Huang, Z., Z. Pan, B. Lei. What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs. – IEEE Transactions on Geoscience and Remote Sensing, Vol. 58, 2019, No 4, pp. 2324-2336.10.1109/TGRS.2019.2947634 Search in Google Scholar

37. Lee, S., S. Purushwalkam, M. Cogswell, D. Crandall, D. Batra. Why M Heads Are Better Than One: Training a Diverse Ensemble of Deep Networks. – arXiv Preprint arXiv:1511.06314, 2015. Search in Google Scholar

38. Huang, X., Q. Yang, H. Qiao. Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition. – IEEE Geoscience and Remote Sensing Letters, Vol. 18, 2020, No 4, pp. 667-671.10.1109/LGRS.2020.2983718 Search in Google Scholar

39. Hafiz, A. M., M. Hassaballah. Digit Image Recognition Using an Ensemble of One-Versus-All Deep Network Classifiers. – arXiv Preprint arXiv:2007.01192, 2020. Search in Google Scholar

40. Polat, K., K. O. Koc. Detection of Skin Diseases from Dermoscopy Image Using the Combination of Convolutional Neural Network and One-Versus-All. – Journal of Artificial Intelligence and Systems, Vol. 2, 2020, No 1, pp. 80-97.10.33969/AIS.2020.21006 Search in Google Scholar

41. Le Cun, Y., L. Bottou, Y. Bengio, P. Haffner. Gradient-Based Learning Applied to Document Recognition. – Proceedings of the IEEE, Vol. 86, 1998, No 1, pp. 2278-2324.10.1109/5.726791 Search in Google Scholar

42. Nair, V., G. E. Hinton. Rectified Linear Units Improve Restricted Boltzmann Machines. – In: Proc. of 27th International Conference on Machine Learning (ICML’10), Haifa, Israel, 2010. Search in Google Scholar

43. Ioffe, S., C. Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. – In: Proc. of International Conference on Machine Learning, 2015, pp. 448-456. Search in Google Scholar

44. Kingma, D. P., J. Ba. Adam: A Method for Stochastic Optimization. – arXiv preprint arXiv:1412.6980, 2014. Search in Google Scholar

45. Vapnik, V. N., A. Y. Chervonenkis. On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities. – In: Measures of Complexity. Springer Cham, 2015, pp. 11-30.10.1007/978-3-319-21852-6_3 Search in Google Scholar

46. Zhang, K., W. Zuo, L. Zhang. FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising. – IEEE Transactions on Image Processing, Vol. 27, 2018, No 9, pp. 4608-4622.10.1109/TIP.2018.283989129993717 Search in Google Scholar

47. Ros s, T. D., S. W. W o r r e l l, V. J. V e l t e n, J. C. M o s s i n g, M. L. B r y a n t. Standard SAR ATR Evaluation Experiments Using the MSTAR Public Release Data Set. – In: Algorithms for Synthetic Aperture Radar Imagery V. Vol. 3370. 1998, pp. 566-573.10.1117/12.321859 Search in Google Scholar

48. Feng, Z., H. Ji, L. Stankovic, J. Fan, M. Zhu. SC-SM CAM: An Efficient Visual Interpretation of CNN for SAR Images Target Recognition. – Remote Sensing, Vol. 13, 2021, No 20, pp. 4139.10.3390/rs13204139 Search in Google Scholar

49. Zhang, A., X. Yang, S. Fang, J. Ai. Region Level SAR Image Classification Using Deep Features and Spatial Constraints. – ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 163, 2020, pp. 36-48.10.1016/j.isprsjprs.2020.03.001 Search in Google Scholar

eISSN:
1314-4081
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Information Technology