  We propose a new convolutional neural network (CNN) which performs coarse and
fine segmentation for end-to-end synthetic aperture radar (SAR) automatic
target recognition (ATR) system. In recent years, many CNNs for SAR ATR using
deep learning have been proposed, but most of them classify target classes from
fixed size target chips extracted from SAR imagery. On the other hand, we
proposed the CNN which outputs the score of the multiple target classes and a
background class for each pixel from the SAR imagery of arbitrary size and
multiple targets as fine segmentation. However, it was necessary for humans to
judge the CNN segmentation result. In this report, we propose a CNN called SAR
ATR with verification support (SAVERS), which performs region-wise (i.e.
coarse) segmentation and pixel-wise segmentation. SAVERS discriminates between
target and non-target, and classifies multiple target classes and non-target
class by coarse segmentation. This report describes the evaluation results of
SAVERS using the Moving and Stationary Target Acquisition and Recognition
(MSTAR) dataset.
