| 引用本文: | 踪华,高晓颖,汪渤,王海罗,李磊.强干扰条件下的星体提取方法[J].哈尔滨工业大学学报,2014,46(8):113.DOI:10.11918/j.issn.0367-6234.2014.08.019 |
| ZONG Hua,GAO Xiaoying,WANG Bo,WANG Hailuo,LI Lei.Method of star extraction on strong interference[J].Journal of Harbin Institute of Technology,2014,46(8):113.DOI:10.11918/j.issn.0367-6234.2014.08.019 |
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| 强干扰条件下的星体提取方法 |
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踪华1,2,3, 高晓颖3, 汪渤1, 王海罗1, 李磊3
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(1.北京理工大学 自动化学院, 100081 北京; 2. 宇航智能控制技术国家级重点实验室,100854 北京; 3. 北京航天自动控制研究所,100854 北京)
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| 摘要: |
| 为了解决在强干扰条件下星体提取问题,提出了一种基于边缘检测+星体像素筛选的星体提取方法,首先用一种边缘检测算法分割星图,然后对像素进行标记,最后利用自适应阈值对星体的目标像素进一步筛选．并对像素标记算法进行改进,改进的算法在标记目标像素时,同时分析与它相邻的像素的连通性,使得属于同一星体的所有像素只分配一个标记值,提高了星图处理的效率．试验结果表明,该处理方法对强干扰噪声具有更好的鲁棒性、且运算速度更快,精度也有所提高．提出的边缘检测+星体像素筛选的星体提取方法有助于在强干扰条件下分离星体目标和背景,改进的像素标记方法,可以更快地区分不同的星体,便于工程应用． |
| 关键词: 星体提取 边缘检测 自适应阈值 像素标记 关联性 |
| DOI：10.11918/j.issn.0367-6234.2014.08.019 |
| 分类号:V488 |
| 基金项目:总装仿真专业组资助项目(51304030106). |
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| Method of star extraction on strong interference |
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ZONG Hua1,2,3, GAO Xiaoying3, WANG Bo1, WANG Hailuo1, LI Lei3
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(1. School of Automation, Beijing Institute of Technology, 100081 Beijing, China; 2. National Key Laboratory of Science and Technology on Aerospace Intelligent Control, 100854 Beijing,China; 3. Beijing Aerospace Automatic Control Institute, 100854 Beijing,China)
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| Abstract: |
| To solve the problem of star extraction on the condition of strong interference, a star extraction method based on edge detection+selection for star pixels was proposed in this paper. An edge detection algorithm to segment the star image is first utilized, and then each pixel is marked. Finally, self-adaptive threshold to select the object pixels of each star is used. Also, the pixel labeling algorithm is improved, and before the pixel of target is labeled, the connectivity of its neighbors is examined so that all of the pixels of the same star only have one mark and the processing efficiency of star image is improved. Experimental results show that the method has better robustness to strange interference, higher speed and accuracy. The improved method can distinguish sensed stars more quickly, and may be more convenient for engineering applications. |
| Key words: star extraction edge detection self-adaptive threshold pixel labeling connectivity |
