| Contourlet变换和Tsallis熵的多源遥感图像匹配 |
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| 引用本文： | 吴一全,陈飒.Contourlet变换和Tsallis熵的多源遥感图像匹配[J].遥感学报,2010,14(5):899-910. |
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| 作者姓名： | 吴一全 陈飒 |
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| 作者单位： | 南京航空航天大学信息科学与技术学院,江苏南京,210016 |
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| 基金项目： | 国家自然科学基金资助项目“The National Natural Science Foundation of China”资助(No.60872065)。 |
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| 摘 要： | 提出了一种利用Contourlet变换、Tsallis熵和改进粒子群优化的多源遥感图像匹配算法。在分别对参考图像和目标图像进行Contourlet分解的基础上,以基于Tsallis熵的互信息量作为相似性度量准则,利用改进的带极值扰动的简化粒子群优化算法对低分辨率的遥感图像进行匹配操作,逐级上推,最终实现全分辨率情况下多源遥感图像的匹配。实验结果表明,与常用的遥感图像匹配算法相比,该算法匹配精度高,稳健性好,且运算量大幅减少。
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| 关 键 词： | 多源遥感图像匹配 Contourlet变换 Tsallis熵 粒子群优化 |
| 收稿时间： | 2009/7/23 0:00:00 |
| 修稿时间： | 2009/11/9 0:00:00 |
Multi-source remote sensing image matching based on contourlet transform and Tsallis entropy |
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| WU Yiquan and CHEN Sa.Multi-source remote sensing image matching based on contourlet transform and Tsallis entropy[J].Journal of Remote Sensing,2010,14(5):899-910. |
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| Authors: | WU Yiquan and CHEN Sa |
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| Institution: | School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu Nanjing 210016, China;School of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Jiangsu Nanjing 210016, China |
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| Abstract: | There are a lot of differences in multi-source remote sensing images from various sensors about the same scene. Maximization of mutual information can be used for the multi-source image matching, but the accuracy and efficiency of image matching need to be further improved. Therefore, an algorithm for multi-source remote sensing image matching was proposed in this paper, based on contourlet transform, Tsallis entropy based mutual information and improved particle swarm optimization. Firstly, the target image and reference image were decomposed to the low resolution image using contourlet transform, respectively. Then, a new image similarity measure criterion, the Tsallis entropy based mutual information, was used to achieve the global optimization. Meanwhile, a modified extremum disturbed and simple particle swarm optimization algorithm was applied to match the lowest resolution remote sensing images. Based on the preliminary result, the matching between the higher resolution images could be implemented stepwise up to the full resolution images. The experimental results show that, compared with those of other existing remote sensing image matching methods, the proposed algorithm has the high accuracy, strong robustness and requires much fewer operations. |
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| Keywords: | multi-source remote sensing image matching contourlet transform Tsallis entropy particle swarm optimization |
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