Imaging cameras are cost-effective sensors for spacecraft navigation. Image-driven techniques to extract the target spacecraft from its background are efficient and do not require pretraining. In this paper, we introduce several image-driven foreground extraction methods, including combining the difference of Gaussian-based scene detection and graph manifold ranking-based foreground saliency generation. We successfully apply our foreground extraction method on infrared images from the STS-135 flight mission captured by the space shuttle’s Triangulation and LIDAR Automated Rendezvous and Docking System (TriDAR) thermal camera. Our saliency approach demonstrates state-of-the-art performance and provides an order of magnitude reduction in processing speed from the traditional methods. Furthermore, we develop a new uncooperative spacecraft pose estimation method by combining our foreground extraction technique with the level-set region-based pose estimation with novel initialization and gradient descent enhancements. Our method is validated using synthetically generated Envisat, Radarsat model, and International Space Station motion sequences. The proposed process is also validated with real rendezvous flight images of the International Space Station.

成像相机是用于航天器导航的高性价比传感器。由图像驱动技术从目标背景中提取目标航天器是有效的，不需要预先训练。在本文中，我们介绍了几种图像驱动的前景提取方法，包括结合基于高斯的场景检测和基于图流形排序的前景显着性的差异。我们成功地将前景提取方法应用于航天飞机的Triangulation和LIDAR自动会合和对接系统（TriDAR）热像仪捕获的STS-135飞行任务的红外图像中。我们的显着性方法展示了最先进的性能，与传统方法相比，处理速度降低了一个数量级。此外，我们通过将前景提取技术与基于水平集区域的姿态估计与新颖的初始化和梯度下降增强功能相结合，开发了一种新的不合作的航天器姿态估计方法。我们的方法已使用合成生成的Envisat，Radarsat模型和国际空间站运动序列进行了验证。国际空间站的实际会合飞行图像也验证了提出的过程。