The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Convolutional Neural Network With Data Augmentation for SAR Target Recognition
Abstract
Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under target translation, the invariance under speckle variation in different observations, and the tolerance of pose missing in training data. In this letter, we investigate the capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition. Experimental results demonstrate the effectiveness and efficiency of the proposed method. The best performance is obtained by using the CNN trained by all types of augmentation operations, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
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Deep convolutional neural network based medical image classification for disease diagnosis
Yadav, S. S., & Jadhav, S. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0276-2 · Cited by 1,069
Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks
Long, Y., Gong, Y., Xiao, Z., & Liu, Q. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2645610 · Cited by 895
Towards global flood mapping onboard low cost satellites with machine learning
Mateo-Garcia, G., Veitch-Michaelis, J., Smith, L., Oprea, S. V., Schumann, G., Gal, Y., Baydin, A. G., & Backes, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-86650-z · Cited by 202
He, D., Xu, K., & Zhou, P. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.043 · Cited by 140
Metadata
- Authors
- Ding, J., Chen, B., Liu, H., & Huang, M.
- Venue
- IEEE Geoscience and Remote Sensing Letters
- Published
- 2016
- Pages
- 1-5
- ISSN
- 1545-598X (ISSN-L)
- 1558-0571
- OpenAlex
- W2292481059
- Cited by
- 708
- References
- 2
- FWCI
- 85.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ding, J., Chen, B., Liu, H., & Huang, M. (2016). Convolutional Neural Network With Data Augmentation for SAR Target Recognition. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/lgrs.2015.2513754
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