Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in astronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, provides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is reconstructed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the proposed approach on detection and localization.