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多传感器遥感图像配准算法研究
其他题名multi-sensor remote sensing image registration
舒丽霞
学位类型工学博士
导师谭铁牛
2008-01-10
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词图像配准 互信息 半峰宽 曲率变化率 Image Registration Mutual Information Half Band-width Curvature Variation
摘要融合多传感器遥感图像,可有效降低单源信息的模糊性,实现多传感器信息的互补。具体来说,融合遥感领域备受关注并广泛应用的SAR与SPOT图像,可以综合地物目标的微波与可见光辐射特性,为地物分析提供更为全面有价值的信息。作为融合的关键一步,SAR与SPOT图像的配准因而有着非常重要的研究意义。 然而,由于SAR与SPOT成像方式的巨大差异,使得SAR与SPOT图像配准成为配准领域最富挑战性的工作之一,目前真正行之有效的SAR与SPOT图像配准方法非常有限。另一方面,定性和主观的传统配准评价无法满足实际应用中定量和客观评价的需求。围绕这些问题,我们逐一开展研究,主要贡献包括: 1) 提出了一种客观定量的配准评价方法。所有的配准研究都在努力追求设计这样的算法:其配准函数曲线是平滑的,且对应正确配准位置的峰突出且尖锐。然而目前对配准函数曲线的评价依赖于目视比较。本文以半峰高与半峰宽之比描述峰的尖锐程度,以曲率变化率描述曲线的平滑程度,分别实现了对配准精度和配准鲁棒性的客观定量的评价,而且只需要两幅图像参与评估。通过与传统评价方法的比较,实验验证了该方法的有效性。 2) 我们发现在未对SAR图像做任何预处理的情况下,互信息可以有效实现SAR与SPOT图像的配准,然而互信息算法计算量大且不够鲁棒,结合基于特征的方法,我们对传统的互信息配准进行了相应的改进: ① 发现SAR图像中的各个部分对互信息配准的贡献大小不一,其中我们称为互信息突出区域的区域对互信息配准函数贡献尤为突出;基于此,我们提出了一种快速的基于互信息的SAR与SPOT图像配准算法。实验验证了互信息突出区域对SAR图像互信息配准的突出贡献,同时也证明了快速互信息配准算法在保证相当的配准准确度的同时,有效加快了传统互信息算法的配准速度。 ② 在分析互信息算法不够鲁棒的原因之后,先后将包含空间信息的方向信息和对比度信息融入到互信息中,分别提出了基于方向信息(MIOI)和基于对比度(MIC)的两种鲁棒的互信息配准算法。结合传统的评价方法以及本文所提出的基于配准函数曲线的评价方法,我们对两种方法进行了客观、定量、全面的比较。实验结果表明两种算法均有效增强了传统互信息配准的鲁棒性,MIOI较MIC更为鲁棒,但是付出了更多的计算代价。
其他摘要Multi-sensor remote sensing image fusion can reduce the uncertainty of single sensor images, and make each other complementary. Specifically, fusion of SAR and SPOT images, the most often used images in remote sensing, can integrate the optical and microwave spectrum of a ground object, and thus provide more comprehensive information for object analysis. Accordingly, as the key step for fusion, SAR and SPOT image registration is important. However, due to the very different imaging condition, it is very challenging for SAR and SPOT image registration. Up to now, few methods can register SAR and SPOT images very well. Meanwhile, traditional evaluation on registration methods is qualitative and subjective. Focusing on these problems, this dissertation mainly addresses the following issues: 1) Based on the quantitative descriptions of registration curves, we propose an objective assessment method on remote sensing image registration. All researches are focusing on developing a registration algorithm, whose registration curves are sharp and smooth. Instead of visual comparison, we use the ratio of half band-width to half amplitude to measure the sharpness of the peak, and curvature variation to measure the smoothness of the curves. Our evaluation is objective, quantitative, and furthermore, only two images are needed. Experiments results demonstrate its validity. 2) If the SAR image is not preprocessed, we notice that mutual information (MI) based method can register SAR and SPOT image well. Nevertheless, it is time consuming and not robust enough. Combined with feature based approach, the traditional MI based registration is improved in the following ways: ① We note that for MI based SAR image registration, different data have different extent of influence on the registration function, and the areas we call MI salient regions, contribute more significantly than the other data. Hence, to decrease the huge computational cost, with the MI salient regions, we propose an accelerated MI based SAR and SPOT image registration method. Experimental results prove the significance of MI salient regions, and they also show that the accelerated approach is satisfactory. ② To improve the robustness of MI based registration, we try to combine MI with orientation information and contrast measure respectively, and then present two robust methods: MI based registration with orientation information (MIOI) and MI with contrast measure (MIC). Based on the evaluation with registration curves, we compare MI, MIOI and MIC quantitatively. Experimental results indicate that both MIOI and MIC are more robust than traditional MI, and moreover, MIOI is more robust than MIC, with more computational cost.
馆藏号XWLW1285
其他标识符200318014603022
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/6042
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
舒丽霞. 多传感器遥感图像配准算法研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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