  Machine learning driven object detection and classification within
non-visible imagery has an important role in many fields such as night vision,
all-weather surveillance and aviation security. However, such applications
often suffer due to the limited quantity and variety of non-visible spectral
domain imagery, in contrast to the high data availability of visible-band
imagery that readily enables contemporary deep learning driven detection and
classification approaches. To address this problem, this paper proposes and
evaluates a novel data augmentation approach that leverages the more readily
available visible-band imagery via a generative domain transfer model. The
model can synthesise large volumes of non-visible domain imagery by
image-to-image (I2I) translation from the visible image domain. Furthermore, we
show that the generation of interpolated mixed class (non-visible domain) image
examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA)
methodology can lead to a significant improvement in the quality of non-visible
domain classification tasks that otherwise suffer due to limited data
availability. Focusing on classification within the Synthetic Aperture Radar
(SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE
Iceberg Classifier Challenge dataset and achieves 75.4% accuracy, demonstrating
a significant improvement when compared against traditional data augmentation
strategies (Rotation, Mixup, and MixCycleGAN).
