Multi-frame infrared small target (MIRST) detection in satellite videos is a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as: First, extremely small target size, highly complex clutters & noises, various satellite motions result in limited feature representation, high false alarms, and difficult motion analyses. Second, the lack of large-scale public available MIRST dataset in satellite videos greatly hinders the algorithm development. To address the aforementioned challenges, in this paper, we first build a large-scale dataset for MIRST detection in satellite videos (namely IRSatVideo-LEO), and then develop a recurrent feature refinement (RFR) framework as the baseline method. Specifically, IRSatVideo-LEO is a semi-simulated dataset with synthesized satellite motion, target appearance, trajectory and intensity, which can provide a standard toolbox for satellite video generation and a reliable evaluation platform to facilitate the algorithm development. For baseline method, RFR is proposed to be equipped with existing powerful CNN-based methods for long-term temporal dependency exploitation and integrated motion compensation & MIRST detection. Specifically, a pyramid deformable alignment (PDA) module and a temporal-spatial-frequency modulation (TSFM) module are proposed to achieve effective and efficient feature alignment, propagation, aggregation and refinement. Extensive experiments have been conducted to demonstrate the effectiveness and superiority of our scheme. The comparative results show that ResUNet equipped with RFR outperforms the state-of-the-art MIRST detection methods. Dataset and code are released at https://github.com/XinyiYing/RFR.

翻译：卫星视频中的多帧红外小目标检测是一项长期存在、基础且具有数十年挑战性的任务，其挑战可归纳为：首先，极小的目标尺寸、高度复杂的杂波与噪声、多样的卫星运动导致特征表示受限、虚警率高且运动分析困难。其次，缺乏大规模公开可用的卫星视频多帧红外小目标数据集严重阻碍了算法发展。为应对上述挑战，本文首先构建了用于卫星视频多帧红外小目标检测的大规模数据集，并开发了一种循环特征精炼框架作为基准方法。具体而言，该数据集是一个包含合成卫星运动、目标外观、轨迹与强度的半仿真数据集，可为卫星视频生成提供标准工具包，并为算法发展提供可靠的评估平台。在基准方法方面，所提出的循环特征精炼框架可与现有基于CNN的强大方法结合，实现长期时序依赖挖掘以及集成的运动补偿与多帧红外小目标检测。具体来说，该框架通过金字塔可变形对齐模块和时空频调制模块，实现了高效的特征对齐、传播、聚合与精炼。大量实验验证了本方案的有效性与优越性。对比结果表明，配备循环特征精炼框架的ResUNet模型在性能上超越了当前最先进的多帧红外小目标检测方法。数据集与代码已发布于https://github.com/XinyiYing/RFR。