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.

翻译：设计自动目标识别（ATR）算法的主要障碍之一在于，通常某个领域（如红外源领域）存在标注图像，而其他目标领域（如可见光、合成孔径雷达、激光雷达）缺乏标注图像。因此，基于源领域的标注图像自动标注这些目标领域图像，对于构建鲁棒的目标领域分类器至关重要。跨导迁移学习是一种有效方法，可利用源领域预训练的ATR网络将网络适应到新目标领域。我们提出一种非配对跨导迁移学习框架，其中利用CycleGAN模型和源领域训练好的ATR分类器，在无任何目标领域标签数据的情况下构建目标领域ATR分类器。我们采用CycleGAN模型将中波红外（MWIR）图像迁移至可见光（VIS）领域（或可见光迁移至中波红外）。为训练跨导CycleGAN，我们优化包含对抗损失、恒等损失、循环一致性损失以及源分类器和目标分类器分类交叉熵损失的代价函数。本文在具有挑战性的DSIAC ATR数据集上进行详细实验分析。该数据集包含中波红外和可见光两个领域中十类车辆目标，涵盖不同姿态及1-5公里距离。实验中假设可见光领域图像为无标签目标数据集。我们首先从原始图像中检测并裁剪车辆，随后将其投影至2公里统一距离。所提出的跨导CycleGAN在DSIAC ATR数据集的可见光领域车辆分类任务中达到71.56%的准确率。