  This report deals with translation invariance of convolutional neural
networks (CNNs) for automatic target recognition (ATR) from synthetic aperture
radar (SAR) imagery. In particular, the translation invariance of CNNs for SAR
ATR represents the robustness against misalignment of target chips extracted
from SAR images. To understand the translation invariance of the CNNs, we
trained CNNs which classify the target chips from the MSTAR into the ten
classes under the condition of with and without data augmentation, and then
visualized the translation invariance of the CNNs. According to our results,
even if we use a deep residual network, the translation invariance of the CNN
without data augmentation using the aligned images such as the MSTAR target
chips is not so large. A more important factor of translation invariance is the
use of augmented training data. Furthermore, our CNN using augmented training
data achieved a state-of-the-art classification accuracy of 99.6%. These
results show an importance of domain-specific data augmentation.
