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

Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery