Robust foreground segmentation and image registration for optical detection of GEO objects

Abstract

With the rapid growth in space utilisation, the probability of collisions between space assets and orbital debris also increases substantially. To support the safe utilisation of space and prevent disruptions to satellite-based services, maintaining space situational awareness (SSA) is crucial. A vital first step in achieving SSA is detecting the man-made objects in orbit, such as space-crafts and debris. We focus on the surveillance of Geo-stationary (GEO) orbital band, due to the prevalence of major assets in GEO. Detecting objects in GEO is challenging, due to the objects being significantly distant (hence fainter) and slow moving relative to the observer (e.g., a ground station or an observing satellite). In this paper, we introduce a new detection technique called GP-ICP to detect GEO objects using optical sensors that is applicable for both ground and space-based observations. Our technique is based on mathematically principled methods from computer vision (robust point set registration and line fitting) and machine learning (Gaussian process regression). We demonstrate the superior performance of our technique in detecting objects in GEO.