| 基于量子免疫克隆聚类的SAR图像变化检测 |
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| 引用本文： | 李阳阳,吴娜娜,焦李成,尚荣华,刘若辰.基于量子免疫克隆聚类的SAR图像变化检测[J].红外与毫米波学报,2011,30(4):372-376. |
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| 作者姓名： | 李阳阳 吴娜娜 焦李成 尚荣华 刘若辰 |
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| 作者单位： | 西安电子科技大学智能感知与图像理解教育部重点实验室,陕西西安,710071 |
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| 基金项目： | 国家自然科学基金(61001202、60803098、61075041); 陕西省“13115”科技创新工程重大科技专项(2008ZDKG-37); 高等学校学科创新引智计划(111计划)(B07048); 国家部委科技项目(9140A07011810DZ0107); 中国博士后科学基金(200801426);中国博士后科学基金(20080431228、20090451369、20090461283); 中央高校基本科研业务费专项资金(JY10000902001、JY10000902039、JY10000902040、K50510020011、JY10000902042) |
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| 摘 要： | 传统的基于进化聚类方法在处理变化检测时耗时过长,在搜索最优聚类中心过程中容易陷入局部最优,对于SAR图像的变化检测存在边缘定位不够准确的缺点,提出了基于量子免疫克隆聚类的SAR图像变化检测方法.把图像的灰度值作为输入信息,通过量子比特定义聚类中心,通过量子免疫克隆算法来搜索最优聚类中心,从而得到更佳的全局阈值,最后根据...
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| 关 键 词： | 变化检测 SAR图像 聚类 量子免疫克隆算法 |
| 收稿时间： | 8/30/2010 2:08:43 PM |
| 修稿时间： | 4/8/2011 4:31:57 PM |
Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm |
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| LI Yang-Yang,WU Na-N,JIAO Li-Cheng,SHANG Rong-Hua and LIU Ruo-Chen.Change detection for SAR images based on quantum-inspired immune clonal clustering algorithm[J].Journal of Infrared and Millimeter Waves,2011,30(4):372-376. |
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| Authors: | LI Yang-Yang WU Na-N JIAO Li-Cheng SHANG Rong-Hua and LIU Ruo-Chen |
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| Affiliation: | Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University,Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China |
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| Abstract: | As the conventional evolutionary clustering optimization methods are often time-consuming and easy to trap in local optimal value in dealing with the problem of change detection. Furthermore, it can not detect the edge accurately for SAR images. We propose the change detection for SAR images based on the clustering analysis. The proposed method takes gray-levels as an input, uses the quantum bit to define the clustering center, and searches the optimal cluster center using the quantum-inspired immune clonal algorithm and gets the global threshold. Finally, the change-detection map is produced. Compared with K&I threshold, it can achieve the better value, and compared with Genetic Algorithm Based Clustering (GAC), the proposed method can search the more better clustering center quickly and effectively, besides, it can detect the accurate edge, improve the change detection accuracy. |
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| Keywords: | Change Detection SAR Images Clustering Quantum-Inspired Immune Clona Algorithm |
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