An approach for autonomous space object identification based on normalized AMI and illumination invariant MSA

The space environment is becoming more and more severe and crowded because of the rapid growth of space objects, which reveals an urgent demand to protect active satellites and other space assets. To accomplish such missions, e.g. the collision warning, the identification of space objects is important. In this paper, a three-stage approach for autonomous space object identification based on optical images is proposed. Firstly, on the basis of the approximate perspective imaging model, a scale and illumination invariant descriptor, composed of the normalized affine moment invariants (AMI) and the illumination invariant multiscale autoconvolution (MSA) transform, is developed to characterize the space object. Secondly, a multi-view modeling method is applied to construct multi-view databases of space objects for handling the viewpoint change. Finally, considering the extensibility of the databases, a K-nearest neighbor classifier is employed, and a K-means clustering is adopted to boost the search speed. Furthermore, to test the performance, a novel system based on the proposed approach is built and evaluated. The experimental evidence suggests that the system is stable and works well when the scale of a space object, the phase angle and the viewpoint change.