多源图像融合方法研究
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摘要
多源图像信息融合技术广泛应用于军事、计算机视觉、医疗诊断及遥感应用等领域。论文从像元、特征和决策融合三个方面研究了多源图像融合技术,提出了一些新的分析处理方法。
像元级图像融合技术的主要研究目的是获得一幅视觉增强的图像。本论文首先研究了一般意义下单纯面向视觉增强的多源图像融合方法,提出了一种基于统计融合模型的多分辨融合方法,给出了一般意义下的统计融合模型,通过引入传感器噪声项,有效地抑制了传感器噪声对图像融合的影响。然后在多光谱图像融合中,提出了一种新的融合方法,将相关约束进一步引入到统计融合模型中。这个方法充分增强了相关的空间信息,同时又有效抑制了光谱失真。
特征组合、分类等是现有文献中特征级图像融合的主要研究内容。论文提出了特征级融合的新思想,即在图像特征提取过程中利用多源图像的信息融合。基于这个思想,提出了一种融合多源图像边缘信息的直线提取算法,把边缘的相位信息作为融合要素,通过融合规则组合多源图像中的不同特性。算法可以提取出仅利用单一或部分图像不能获得的直线特征。论文进一步把道路的直线特征与光谱特征相结合,发展了一种从遥感多光谱图像提取道路的算法。
决策级图像融合技术具有广泛的应用范围。Dempster-Shafer(D-S)证据理论是决策融合的主要方法之一,但典型的D-S理论不大适应高冲突证据组合。论文提出了一种基于预处理模式的新方法,在利用Dempster组合规则进行证据组合之前,将冲突焦元的基本概率赋值部分转移到焦元并集,采用证据之间的冲突额度来确定证据组合顺序。由于预方法将冲突化解为不确定的知识表示,D-S理论可以处理冲突证据的组合问题。论文进一步将改进方法应用于高光谱图像分类,发展了一种基于D-S理论的高光谱图像分类方法,提供了比基于典型D-S理论方法更好的结果。
论文提出的所有算法均应用于真实多源图像数据,实验结果显示了算法的有效性。
Multisource image fusion technology is widely applied in a variety of fields such as military surveillance, computer vision, medical diagnosis and remote sensing. In this dissertation, three levels of image fusion technology: pixel-level, feature-level and decision-level are studied, and some novel methods of analysis and processing are presented.The main research objective of pixel-level image fusion is to obtain a visual enhanced image. A new image multi-resolution fusion method based on statistical model is presented in typical multisource image fusion this thesis. The method results in restraining sensitivity to sensor noise since sensor noise item is introduced into the model. In multi-spectral image fusion, a new method is developed, which introduces correlative restriction into the statistical model. Using the method, the interrelated spatial information is well enhanced, and the spectral information of multi-spectral images is effectively kept.Feature integration and classification from multisource images are main issues in existing references. A new idea of feature-level fusion is presented in this thesis, whose concept is feature extraction is supported by multisource image fusion. Based on this idea, a new line extraction algorithm is developed by fusing the edge information of multisource image, where phase of edge is main fusion element and fusion rules are used to integrate different properties of multisource images. It can extract the line segments, which are not obtained by using single image or part images. Followed it, a road extraction algorithm from multi-spectral images is presented by using the line and spectral features.Decision-level image fusion is widely used. Dempster-Shafer theory of evidence is one of the main methods of decision fusion, but typical D-S theory is sensitive to highly conflict evidences. In this thesis, a new method based on the pretreatment mode is presented, where the basic probability assignments of conflict focal elements are partly transferred to the union of focal elements before using the Dempster combination rule, and the combination order are determined based on the conflicted measurement. Therefore D-S theory can deal with the cases with highly conflict evidences since conflict information is translated into unknown knowledge. Followed it, a classification algorithm for hyperspectral image data is presented, which provides
better results than the method based on typical D-S theory.All algorithms presented are applied to real multisource image data. The experimental results show their validity and adaptability.
像元级图像融合技术的主要研究目的是获得一幅视觉增强的图像。本论文首先研究了一般意义下单纯面向视觉增强的多源图像融合方法,提出了一种基于统计融合模型的多分辨融合方法,给出了一般意义下的统计融合模型,通过引入传感器噪声项,有效地抑制了传感器噪声对图像融合的影响。然后在多光谱图像融合中,提出了一种新的融合方法,将相关约束进一步引入到统计融合模型中。这个方法充分增强了相关的空间信息,同时又有效抑制了光谱失真。
特征组合、分类等是现有文献中特征级图像融合的主要研究内容。论文提出了特征级融合的新思想,即在图像特征提取过程中利用多源图像的信息融合。基于这个思想,提出了一种融合多源图像边缘信息的直线提取算法,把边缘的相位信息作为融合要素,通过融合规则组合多源图像中的不同特性。算法可以提取出仅利用单一或部分图像不能获得的直线特征。论文进一步把道路的直线特征与光谱特征相结合,发展了一种从遥感多光谱图像提取道路的算法。
决策级图像融合技术具有广泛的应用范围。Dempster-Shafer(D-S)证据理论是决策融合的主要方法之一,但典型的D-S理论不大适应高冲突证据组合。论文提出了一种基于预处理模式的新方法,在利用Dempster组合规则进行证据组合之前,将冲突焦元的基本概率赋值部分转移到焦元并集,采用证据之间的冲突额度来确定证据组合顺序。由于预方法将冲突化解为不确定的知识表示,D-S理论可以处理冲突证据的组合问题。论文进一步将改进方法应用于高光谱图像分类,发展了一种基于D-S理论的高光谱图像分类方法,提供了比基于典型D-S理论方法更好的结果。
论文提出的所有算法均应用于真实多源图像数据,实验结果显示了算法的有效性。
Multisource image fusion technology is widely applied in a variety of fields such as military surveillance, computer vision, medical diagnosis and remote sensing. In this dissertation, three levels of image fusion technology: pixel-level, feature-level and decision-level are studied, and some novel methods of analysis and processing are presented.The main research objective of pixel-level image fusion is to obtain a visual enhanced image. A new image multi-resolution fusion method based on statistical model is presented in typical multisource image fusion this thesis. The method results in restraining sensitivity to sensor noise since sensor noise item is introduced into the model. In multi-spectral image fusion, a new method is developed, which introduces correlative restriction into the statistical model. Using the method, the interrelated spatial information is well enhanced, and the spectral information of multi-spectral images is effectively kept.Feature integration and classification from multisource images are main issues in existing references. A new idea of feature-level fusion is presented in this thesis, whose concept is feature extraction is supported by multisource image fusion. Based on this idea, a new line extraction algorithm is developed by fusing the edge information of multisource image, where phase of edge is main fusion element and fusion rules are used to integrate different properties of multisource images. It can extract the line segments, which are not obtained by using single image or part images. Followed it, a road extraction algorithm from multi-spectral images is presented by using the line and spectral features.Decision-level image fusion is widely used. Dempster-Shafer theory of evidence is one of the main methods of decision fusion, but typical D-S theory is sensitive to highly conflict evidences. In this thesis, a new method based on the pretreatment mode is presented, where the basic probability assignments of conflict focal elements are partly transferred to the union of focal elements before using the Dempster combination rule, and the combination order are determined based on the conflicted measurement. Therefore D-S theory can deal with the cases with highly conflict evidences since conflict information is translated into unknown knowledge. Followed it, a classification algorithm for hyperspectral image data is presented, which provides
better results than the method based on typical D-S theory.All algorithms presented are applied to real multisource image data. The experimental results show their validity and adaptability.
引文
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