【作者】 陈飒；

【导师】 吴一全；

【作者基本信息】 南京航空航天大学 ， 通信与信息系统， 2010， 硕士

【摘要】 在遥感领域,卫星遥感系统提供着越来越多覆盖全球和重复测度的数据,为了充分、有效、综合地利用这些信息,就需要将同一目标或同一区域的多源图像数据进行匹配与融合。本文以多源遥感图像作为对象,研究并实现了多源遥感图像的匹配与融合算法。首先,讨论了一种基于Contourlet域、Krawtchouk矩和改进粒子群的图像匹配算法,主要针对单一传感器所获取的遥感图像进行快速匹配。与目前常用的匹配算法相比,该算法可在加快匹配速度的同时,大大提高匹配的精度。其次,针对多传感器获取的遥感图像,研究了两种多源遥感图像匹配算法,分别引入改进型Hausdorff距离和基于Tsallis熵的互信息量作为相似性度量准则。结果表明,这两种算法都具有较高的匹配效率和运算速度。然后,实现了一种基于对数极坐标变换和对齐度的多源遥感图像配准算法,对以Harris角点为中心的圆进行对数极坐标变换,采用对齐度度量准则得到匹配点。实验结果显示,该算法能有效地对多源遥感图像进行配准,对旋转、尺度变换具有一定的鲁棒性。接着,采用了无下采样Contourlet变换对多源遥感图像进行融合处理。该方法的性能较传统的基于小波变换或基于Contourlet变换的融合方法有了一定的提高,融合效果更好。最后,介绍了一种基于无下采样Contourlet变换和模糊推理的图像融合算法,利用模糊推理计算各源图像对融合图像的贡献程度。结果表明,该算法简单、可行,并能更加有效地融合源图像中的信息。更多还原

【Abstract】 In the field of remote sensing, satellite remote sensing systems are providing a growing number of global coverage and repeated measure data. To make full use of this information, multi-source images of the same target or the same region need to be matched and fused. In this paper, multi-source remote sensing images are taken as the research objects, the matching and fusion methods for multi-source remote sensing images are studied and implemented.Firstly, a fast image matching algorithm based on contourlet-domain, Krawtchouk moments and improved particle swarm optimization is discussed, which is mainly used to match the remote sensing images taken from a single sensor. Compared with those of other existing image matching methods, the algorithm is less time-consuming, while greatly improves the precision.Secondly, aiming at the remote sensing images taken from multi-sensors, two image matching algorithms are studied. Improved hausdorff distance and Tsallis entropy based mutual information are used as matching measure function, respectively. The results show that the algorithms both have the higher accuracy and require fewer operations.Then, an image registration method based on log-polar transformation and alignment metric is implemented. Log-polar transform is applied to the region whose centre is Harris corner, and the matching points are obtained by using alignment metric. The results show that the method can effectively match the multi-source remote sensing images, and has robustness with rotation and scale transform.Next, the nonsubsampled contourlet transform is used in image fusion for multi-source remote sensing images. The approach can achieve better results than the methods based on wavelet transform or contourlet transform.Finally, an image fusion method based on nonsubsampled contourlet transform and fuzzy reasoning is introduced, estimating the importance of the source images with fuzzy reasoning. The results show that the method is simple, feasible and can be more effective for image fusion.更多还原