Multi-view space object recognition and pose estimation based on kernel regression  

作　　者：Zhang Haopeng Jiang Zhiguo 

机构地区：[1]Image Processing Center, School of Astronautics, Beihang University [2]Beijing Key Laboratory of Digital Media

出　　处：《Chinese Journal of Aeronautics》2014年第5期1233-1241,共9页中国航空学报（英文版）

基　　金：co-supported by the National Natural Science Foundation of China (Grant Nos. 61371134, 61071137);the National Basic Research Program of China (No. 2010CB327900)

摘　　要：The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.

关 键 词：Kernel regression Object recognition Pose estimation Space objects Vision-based 

分 类 号：V448.22[航空宇航科学与技术—飞行器设计]