Abstract
With the rapidly growing population of resident space objects (RSOs) in the near-Earth space environment, detailed information about their condition and capabilities is needed to provide Space Domain Awareness (SDA). Space-based sensing will enable inspection of RSOs at shorter ranges, independent of atmospheric effects, and from all aspects. The use of a sub-THz inverse synthetic aperture radar (ISAR) imaging and sensing system for SDA has been proposed in previous work, demonstrating the achievement of sub-cm image resolution at ranges of up to 100 km. This work focuses on recognition of external structures by use of sequential feature detection and tracking throughout the aligned ISAR images of the satellites. The Hough transform is employed to detect linear features, which are tracked throughout the sequence. ISAR imagery is generated via a metaheuristic simulator capable of modelling encounters for a variety of deployment scenarios. Initial frame-to-frame alignment is achieved through a series of affine transformations to facilitate later association between image features. A gradient-by-ratio method is used for edge detection within individual ISAR images, and edge magnitude and direction are subsequently used to inform a double-weighted Hough transform to detect features with high accuracy. Feature evolution during sequences of frames is analysed. It is shown that the use of feature tracking within sequences with the proposed approach will increase confidence in feature detection and classification, and an example use-case of robust detection of shadowing as a feature is presented.
Abstract (translated)
随着近地空间环境中在轨航天器（RSOs）数量的迅速增长，为了提供空间领域意识（SDA），需要详细了解这些物体的状态和能力。基于太空的感应技术将使我们能够在不受大气影响的情况下，在更短的距离内从各个角度对RSOs进行检查。 之前的工作提出使用低于THz频段的逆合成孔径雷达（ISAR）成像和传感系统来实现SDA，并且该研究展示了在多达100公里范围内能够达到亚厘米级图像分辨率。这项工作重点在于通过顺序特征检测和跟踪，在对齐后的ISAR卫星图像中识别外部结构。 霍夫变换被用来检测线性特征，并在整个序列中进行追踪。ISAR影像由一个元启发式模拟器生成，该模拟器能够为各种部署场景建模相遇情况。初步的帧间对准是通过一系列仿射变换来实现的，以促进后续图像特征间的关联。 在每个单独的ISAR图像内使用梯度比率方法进行边缘检测，并利用边界的强度和方向信息来进行双加权霍夫变换，从而精确地识别特征。在整个序列中分析特性随时间的变化情况。 研究表明，在序列中使用提出的特征跟踪技术将增加对特征检测与分类的信心，并展示了通过该方法稳健地检测阴影这一特性的示例用例。
URL
https://arxiv.org/abs/2512.15618
