Object detection in remote sensing is a crucial computer vision task that has seen significant advancements with deep learning techniques. However, most existing works in this area focus on the use of generic object detection and do not leverage the potential of multimodal data fusion. In this paper, we present a comparison of methods for multimodal object detection in remote sensing, survey available multimodal datasets suitable for evaluation, and discuss future directions.

翻译：遥感目标检测是一项关键的计算机视觉任务，随着深度学习技术的发展已取得显著进展。然而，现有研究大多集中于通用目标检测方法，未能充分利用多模态数据融合的潜力。本文对遥感中的多模态目标检测方法进行了比较，梳理了适用于评估的公开多模态数据集，并探讨了未来发展方向。