Target classification in infrared imagery by cross-spectral synthesis using GAN

摘要

Images can be captured using devices operating at different light spectrum's. As a result, cross domain image translation becomes a nontrivial task which requires the adaptation of Deep convolutional networks (DCNNs) to resolve the aforementioned imagery challenges. Automatic target recognition(ATR) from infrared imagery in a real time environment is one of such difficult tasks. Generative Adversarial Network (GAN) has already shown promising performance in translating image characteristic from one domain to another. In this paper, we have explored the potential of GAN architecture in cross-domain image translation. Our proposed GAN model maps images from the source domain to the target domain in a conditional GAN framework. We verify the performance of the generated images with the help of a CNX-based target classifier. Classification results of the synthetic images achieve a comparable performance to the ground truth ensuring realistic image generation of the designed network.