【作者】 柴艳妹；

【导师】 任金昌；

【作者基本信息】 西北工业大学 ， 计算机软件及理论， 2004， 硕士

【摘要】 作为图像融合领域的一个重要分支，多源遥感图像融合研究的是如何综合利用不同航空遥感传感器所获取的图像信息，来产生新的数据，以获取对同一事物或目标的更为全面、客观及本质上的认识。在高度信息化的今天，遥感图像融合已经成为图像处理和图像信息理解领域中不可或缺的技术，并在很多军事和民用方面有着重要应用。 本文应用图像处理和现代信号处理技术中的多种手段，研究了不同层次上多源遥感图像的融合方法。通过对不同航空遥感传感器所获取的图像数据进行融合，从而提高图像的分辨率、图像分析结果的准确性和置信度，并最终提高对特定航空目标进行自动检测、识别的有效性。 本文的主要研究内容和工作可总结如下： 1．介绍了图像融合的基本概念和原理，进而系统分析了多种传统的图像融合方法，并通过实验对其特点和性能做了细致的对比，总结出了一系列有用的结论； 2．在特征级融合方面，提出了一种基于方向性信息测度和IHS变换的图像融合算法。实验表明该算法产生的光谱畸变很小，并且有良好的抗噪性，非常适合处理多光谱图像的融合； 3．在多分辨率图像融合算法中，提出了基于小波变换的自适应图像融合算法(DWT_EI)。与传统算法比较，该算法在提高图像信息含量方面表现得最好。同时它的运算也相对简单，并且和原始图像的相关性也很好(即光谱畸变小)，是一种非常好的融合算法； 4．归纳并给出了基于信息量的评价、基于统计特性的评价、基于相关性的评价和基于梯度值的评价四类十项融合结果评价指标。这些指标被用于对融合实验结果的实际评价中，使得对算法的评价从定性到定量两方面都有了一定的评价标准； 5．不仅从实际应用角度验证了图像融合技术能够增加图像信息含量、提高图像分割、分类和识别的有效性这一结论，而且从理论角度出发，进一步探讨了图像融合中的识别与决策问题。同时对图像融合中的可靠性与容错性问题也进行了简要分析； 6．除多源遥感影像作为实验数据以外，本文还选用了一组多聚焦可见光图片来验证和评价各种算法的融合效果，从而使得各种算法的有效性及优劣性更加直观。更多还原

【Abstract】 As one of the important aspects of image fusion, multi-sensor remote-sensing image fusion (MS-RSIF) investigates how to integrate image information from different aerial remote sensors, and generate new data, for more complete, objective, and essential cognition of the specified target. With the development of information technologies, currently, RSIF becomes indispensable in image processing and understanding, along with many important military and civil applications.In this thesis, MS-RSIF methods are investigated in different levels by means of image processing and modern signal processing techniques. Through fusion of image data from different aerial remote sensors, we can improve the image resolution and analysis results in precision and believability, and further improve the effectiveness in detection and recognition of specific aerial targets.In the following is the main contents and contributions of this thesis:1. Basic concepts and principles of RSIF are introduced, and many traditional algorithms are then systemically analyzed; With detail comparison of their characteristics and performances, a series of useful conclusions are given;2. As for feature-level fusion, a new algorithm based on orientation information measurement and HIS transform is proposed. The experimental results have demonstrated that the proposed method very suitable for fusion of spectrum images in mostly preserving spectrum information and resisting noises;3. As for the multi-resolution image fusion, a wavelet transform based self-adaptive algorithm (DWT_EI) is proposed. Compared with traditional ones, our method has best performance in obtaining information entropy with lower calculation cost, and more correlation with the source image;4. Ten criterion items in four groups based on information quantity, statistic speciality, correlation and gradient respectively, are summarized for fusion evaluation, which are utilized in the experiments to acquire both qualitative and quantitative analysis of the fusion results;5. Not only verified the conclusion that image fusion can improve image information and efficiency of segment, classification and recognition, but also further discussed the recognition and decision problems involved in image fusion. In Addition, reliability and fault-tolerance are also analyzed;6. Except for multi-source remote sensing images, a group of multi-focus images are also applied in our experiments to validate and evaluate fusion algorithms with more intuitional and explicit results.更多还原