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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 s...Show More

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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 13, Issue: 3, March 2016)
Page(s): 364 - 368
Date of Publication: 26 January 2016

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