Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 13, Issue 3, Pages 364-368Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2015.2513754
Keywords
Convolutional neural network (CNN); data augmentation; feature extraction; synthetic aperture radar (SAR); image; target recognition
Categories
Funding
- National Natural Science Foundation of China [61372132, 61201292]
- One Thousand Young Talents Program
- New Century Excellent Talents in University [NCET-13-0945]
- National Natural Science Fund for Distinguished Young Scholars [61525105]
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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 posemissing 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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