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Detection, tracking, characterization and identification of space targets orbiting the Earth is one of the main tasks of the space surveillance system. Spatial target image feature extraction can find out some descriptions that reflect the essence of the target from the original image. The moment feature describes the statistical characteristics of grayscale by the moments of each order of image distribution, which can effectively reflect the shape information of the target. A Hu extension moment is proposed, and a feature extraction method for spatial target images is proposed based on this. It solves the problem of information loss when the confidence variance difference is large and the eigenvalue is negative. A linear combinatorial feature calculation method is proposed for spatial target classification, and a linear classifiable quantitative discrimination method is given. The image needs to be preprocessed. Then, the seven eigenvalues based on the extension moment are calculated, and the similarity analysis of the Hu extension moment is carried out. At last, the similarity of the sample target region matching is analyzed, and the target similarity can reach 90%. For image sets with multiple types of targets, according to the numerical distribution calculates the linear combination feature, linear classifiable quantitative discriminant results can be calculated. 48 images of four satellites in the database were selected as test data. Its recognition accuracy rate reaches 95.8%. Calculating the recognition result of the same platform for multi-sample joint recognition, and the probability of recognition attribution after multi-sample union is 100%.
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