Deep Transductive Transfer Learning for Automatic Target Recognition
Shoaib M. Sami Nasser M. Nasrabadi Raghuveer Rao
One of the major obstacles in designing an automatic target recognition (ATR)
algorithm, is that there are often labeled images in one domain (i.e., infrared
source domain) but no annotated images in the other target domains (i.e.,
visible, SAR, LIDAR). Therefore, automatically annotating these images is
essential to build a robust classifier in the target domain based on the
labeled images of the source domain. Transductive transfer learning is an
effective way to adapt a network to a new target domain by utilizing a
pretrained ATR network in the source domain. We propose an unpaired
transductive transfer learning framework where a CycleGAN model and a
well-trained ATR classifier in the source domain are used to construct an ATR
classifier in the target domain without having any labeled data in the target
domain. We employ a CycleGAN model to transfer the mid-wave infrared (MWIR)
images to visible (VIS) domain images (or visible to MWIR domain). To train the
transductive CycleGAN, we optimize a cost function consisting of the
adversarial, identity, cycle-consistency, and categorical cross-entropy loss
for both the source and target classifiers. In this paper, we perform a
detailed experimental analysis on the challenging DSIAC ATR dataset. The
dataset consists of ten classes of vehicles at different poses and distances
ranging from 1-5 kilometers on both the MWIR and VIS domains. In our
experiment, we assume that the images in the VIS domain are the unlabeled
target dataset. We first detect and crop the vehicles from the raw images and
then project them into a common distance of 2 kilometers. Our proposed
transductive CycleGAN achieves 71.56% accuracy in classifying the visible
domain vehicles in the DSIAC ATR dataset.