To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at $t_1$ together with low-resolution images at a future time $t_2$ from the same location as inputs and fuses them to generate high-resolution images for the same location at $t_2$. We propose an arithmetic operation applied to the low-resolution images at the two time points in feature space to take care of temporal changes. We evaluated the proposed model on three modality pairs for multimodal temporal image fusion, including downsampled WorldView-2/original WorldView-2, Landsat-8/Sentinel-2, and Sentinel-2/NAIP. Experimental results show that our model outperforms traditional algorithms and recent deep learning-based models by large margins in most scenarios, achieving sharp fused images while appropriately addressing temporal changes.