Identification method of satellite local components based on combined feature metrics

In order to meet the requirements of identification of satellite local targets, a new method based on combined feature metrics is proposed. Firstly, the geometric features of satellite local targets including body, solar panel and antenna are analyzed respectively, and then the cluster of each component are constructed based on the combined feature metrics of mathematical morphology. Then the corresponding fractal clustering criterions are given. A cluster model is established, which determines the component classification according to weighted combination of the fractal geometric features. On this basis, the identified targets in the satellite image can be recognized by computing the matching probabilities between the identified targets and the clustered ones, which are weighted combinations of the component fractal feature metrics defined in the model. Moreover, the weights are iteratively selected through particle swarm optimization to promote recognition accuracy. Finally, the performance of the identification algorithm is analyzed and verified. Experimental results indicate that the algorithm is able to identify the satellite body, solar panel and antenna accurately with identification probability up to 95%, and has high computing efficiency. The proposed method can be applied to identify on-orbit satellite local targets and possesses potential application prospects on spatial target detection and identification.