This paper proposes the SPARK dataset as a new unique space object
multi-modal image dataset. Image-based object recognition is an important
component of Space Situational Awareness, especially for applications such as
on-orbit servicing, active debris removal, and satellite formation. However,
the lack of sufficient annotated space data has limited research efforts in
developing data-driven spacecraft recognition approaches. The SPARK dataset has
been generated under a realistic space simulation environment, with a large
diversity in sensing conditions for different orbital scenarios. It provides
about 150k images per modality, RGB and depth, and 11 classes for spacecrafts
and debris. This dataset offers an opportunity to benchmark and further develop
object recognition, classification and detection algorithms, as well as
multi-modal RGB-Depth approaches under space sensing conditions. Preliminary
experimental evaluation validates the relevance of the data, and highlights
interesting challenging scenarios specific to the space environment.

本论文提出 SPARK 数据集作为一种新颖的空间物体多模态图像数据集，为太空态势感知中的物体识别做出了重要贡献，通过提供多种不同的轨道场景的感应条件，为空间环境下的目标识别、分类及检测算法的开发提供了具有挑战性的基准测试数据集。