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Relative pose measurement is the main content of spatial non-cooperative target situational awareness. In pose measurement, feature extraction of target image is required first, and the accuracy and robustness of feature extraction directly affect the performance of pose measurement. In order to improve the robustness of spatial target feature extraction, this paper presents a feature extraction algorithm which integrates multi-processing streams and utilizes the whole geometric frame of the target. Firstly, the gradient filter is used to eliminate the background interference of the spatial target image. Then, LSD line detection algorithm, Hough Lines line detection algorithm and Shi-Tomasi corner detection algorithm are used to extract three groups of feature points. Then, k-D spatial partition tree and K-nearest neighbor search algorithm are used to fuse the three groups of feature points. The minimum number of feature points containing the most significant features is extracted, and finally the filtered feature points are combined into a broken line structure, so as to introduce the correlation information between feature points and better characterize satellite features. The semi-physical simulation results show that the proposed feature extraction method has better robustness.