Aerial Visible-to-Infrared Image Translation: Dataset, Evaluation, and Baseline

Abstract

Aerial visible-to-infrared image translation aims to transfer aerial visible images to their corresponding infrared images, which can effectively generate the infrared images of specific targets. Although some image-to-image translation algorithms have been applied to color-to-thermal natural images and achieved impressive results, they cannot be directly applied to aerial visible-to-infrared image translation due to the substantial differences between natural images and aerial images, including shooting angles, multi-scale targets, and complicated backgrounds. In order to verify the performance of existing image-to-image translation algorithms on aerial scenes as well as advance the development of aerial visible-to-infrared image translation, an Aerial Visible-to-Infrared Image Dataset (AVIID) is created, which is the first specialized dataset for aerial visible-to-infrared image translation and consists of over 3,000 paired visible-infrared images. Over the constructed AVIID, a complete evaluation system is presented to evaluate the generated infrared images from 2 aspects: overall appearance and target quality. In addition, a comprehensive survey of existing image-to-image translation approaches that could be applied to aerial visible-to-infrared image translation is given. We then provide a performance analysis of a set of representative methods under our proposed evaluation system on AVIID, which can serve as baseline results for future work. Finally, we summarize some meaningful conclusions, problems of existing methods, and future research directions to advance state-of-the-art algorithms for aerial visible-to-infrared image translation.