Space multi-target detection by second variance

Detecting small multi-target in aerospace is both a significant and difficult issue for the satellite tracking system. It is fundamentally challenging due to the existence of strong noise in captured images, the few characteristic of spot-like target, the simultaneous multiple targets and the real-time requirement. To address these challenges, we formulate space multi-target as a two-step detection problem, which integrates the variance vector detection and 2th variance detection. The pixels of images are projected to the variance subspace of two-dimension. In the first step, the candidate targets are extracted with the optimal threshold achieved by K-means and proposed Weighted Maximum Right Probability, namely WMRP. In the second step, the true targets are checked out by adopting the proposed 2th variance feature and multiscale threshold. Experiments demonstrate the proposed two-step framework can efficiently and rapidly detect the small multiple targets, which can satisfy the common application of space multi-target tracking.