Joint Embedding and Classification for SAR Target Recognition

Deep learning can be an effective and efficient means to automatically detect and classify targets in synthetic aperture radar (SAR) images, but it is critical for trained neural networks to be robust to variations that exist between training and test environments. The layers in a neural network can be understood as successive transformations of an input image into embedded feature representations and ultimately into a semantic class label. To address the overfitting problem in SAR target classification, we train neural networks to optimize the spatial clustering of points in the embedded space in addition to optimizing the final classification score. We demonstrate that networks trained with this dual embedding and classification loss outperform networks with classification loss only. We study placing the embedding loss after different network layers and find that applying the embedding loss on the classification space results in the best SAR classification performance. Finally, our visualization of the network's ten-dimensional classification space supports our claim that the embedding loss encourages greater separation between target class clusters for both training and testing partitions of the MSTAR dataset.