DGCGAN: An Unpaired Method for Cross-Spectral Image Translation from Visible to Thermal Infrared

Thermal infrared imaging is widely used in applications such as disaster monitoring and target recognition because it remains stable under illumination changes and supports nighttime observation. However, thermal infrared data are expensive to acquire, and the related application scenarios are often sensitive, which leads to limited publicly available thermal infrared datasets and restricts the development of relevant research. Cross-spectral image translation from visible to thermal infrared provides a solution for expanding infrared datasets, but accurate mapping remains difficult because visible light reflection and thermal infrared emission follow different physical mechanisms. This paper proposes a Dual Geometric Cycle Generative Adversarial Network (DGCGAN), for unpaired visible-to-thermal infrared translation. The proposed method improves cross-spectral mapping accuracy by combining geometric-consistency constraints with cycle-consistency constraints. In addition, disentangled representation learning is introduced to decompose cross-spectral images into a domain-invariant semantic structure space and a domain-specific imaging style space, enabling one-to-many synthesis through the cross-combination of structure and style. Experiments on public aerial datasets, including AVIID and Drone Vehicle, demonstrate that DGCGAN significantly improves the realism and diversity of generated images compared with other popular unpaired translation methods. Specifically, DGCGAN achieves FID and KID values of 63. 727 and 0. 008711 on the Day Road dataset (part of Drone Vehicle), 69. 419 and 0. 019352 on the Night Road dataset (part of Drone Vehicle). Moreover, it outperforms other methods on all four evaluation metrics on the AVIID dataset. Furthermore, real drone data collected using a dual-spectrum platform are used to validate the practical usefulness of the proposed method. We also collected real data using a dual-spectrum drone platform to verify the practical usefulness of the proposed method.