地面场景光学图像辅助导航技术研究
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摘要
图像辅助导航是未来飞行器组合导航发展的重要方向,寻找适应性强、精度高、计算快的不同传感器图像匹配算法一直是其研究的核心问题。论文围绕红外与可见光图像匹配的难点问题,光学图像辅助导航中若干关键技术进行了研究,本文主要完成以下工作:
(1)分析红外和可见光图像的成像特征和差异,比较两者共同区域的边缘类型,并对其边缘进行数学描述,对常用的边缘提取算法进行讨论和分析,提出一种基于Tsallis熵的边缘检测方法,利用图像的局部的熵信息,计算滑动窗口内图像的Jensen-Tsallis散度-方向对,由Jensen-Tsallis散度确定候选边缘像素,再利用Jensen-Tsallis的方向信息细化并连接边缘像素,实验表明该方法能有效提取红外与可见光图像的共性边缘特征。
(2)研究基于边缘特征的图像匹配算法。研究了基于3-4DT的边缘匹配方法和ESD相关匹配方法,提出一种基于距离变换的相似性度量方法,并比较它们在匹配适应性、匹配精度等方面性能,给出了实验结果。穷尽搜索匹配位置计算量非常大,本文采用非遍历寻优的搜索策略,利用蚁群算法的快速全局寻优能力,提出一种多维空间蚁群算法的图像匹配方法,以大幅减少计算量。
(3)研究Fourier变换的平移、旋转、缩放特性在图像匹配中的应用,以及Fourier-Mellin图像匹配方法的实现,讨论双谱的性质,提出一种基于双谱的图像匹配方法,该方法不受高斯噪声的影响,实验结果表明,该方法成功实现了具有旋转、尺度、平移变换关系的图像之间的匹配。
(4)研究SIFT特征描述符及其降维问题,本文利用核主成分分析的特征提取方法,对每个特征点的SIFT描述符进行降维处理,然后建立它们的初始匹配,进而结合数字地形图,建立导航参数的约束方程,提出一种基于蚁群算法的最小截取二乘鲁棒估计方法,估计飞行器载相机的位置和姿态,实现运动平台的图像辅助导航。
Image aided navigation is inevitable tendency of integrated navigation system of vehicle.How to design an algorithm with high adaptability, precision and computing efficiency is the kernel study topic.Following the difficulties of IR/Optical images matching, this dissertation researches the key technologies of image aided navigation.The important contributions and creative achievements are summarized as follows:
(1)By analyzing the character and difference of IR image and optical image,the edge types that exist in common area of images are described as mathematical models.Some common extraction methods are discussed and analyzed. A edge detection algorithm based on Tsallis entropy is presented. The algorithm uses local entropy information,and the divergence-direction is obtained by sliding a window over an image. The edge pixel is linked from candidate pixels using direction information. Some experimental results have been provided to show the edge detection capability of the proposed algorithm.
(2)The edge-based image matching algorithms are studied. The algorithms include the similarity measurement based on 3-4DT, edge based similar distance(ESD),and the proposed similarity measurement based on distance transformation. Their performance on adaptability and precision are analysed and compared.The experiment results are also given. High computational complexity due to pixel-by-pixel search strategy is one major drawback. In this dissertation, a much faster algorithm to approximate the matching position between two images of a scene is described. Reduction on the computional complexity is achieved by using an the improved ACA in continuous space based search strategy,due to the non-exhaustive search nature of the algorithm.
(3)The image matching technique based on fourier transform and Fourier-Mellin algorithm can be used to register images which are misaligned due to rotation, scaling and translation.It presents a method of image matching algorithm based on bispectrum, it uses the property that bispectrum is not sensitive to Gauss noise, which can exactly estimates the parameters between images suffered from translation, rotation and scaling distortion.
(4)SIFT Operator has been proven to be the most robust local invariant feature descriptor,which has been widely thought of a success in image matching.The SIFT volumes of keypoints are reduced by kernel principal component analysis(KPCA).The coarse matching is established between two successive frames. A kind of highly robust estimation is presented,named least trimmed squares, on the data basis of the digital terrain map.The vehicle’s position ,attitude and motion parameters is estimated by the proposed algorithm.
(1)分析红外和可见光图像的成像特征和差异,比较两者共同区域的边缘类型,并对其边缘进行数学描述,对常用的边缘提取算法进行讨论和分析,提出一种基于Tsallis熵的边缘检测方法,利用图像的局部的熵信息,计算滑动窗口内图像的Jensen-Tsallis散度-方向对,由Jensen-Tsallis散度确定候选边缘像素,再利用Jensen-Tsallis的方向信息细化并连接边缘像素,实验表明该方法能有效提取红外与可见光图像的共性边缘特征。
(2)研究基于边缘特征的图像匹配算法。研究了基于3-4DT的边缘匹配方法和ESD相关匹配方法,提出一种基于距离变换的相似性度量方法,并比较它们在匹配适应性、匹配精度等方面性能,给出了实验结果。穷尽搜索匹配位置计算量非常大,本文采用非遍历寻优的搜索策略,利用蚁群算法的快速全局寻优能力,提出一种多维空间蚁群算法的图像匹配方法,以大幅减少计算量。
(3)研究Fourier变换的平移、旋转、缩放特性在图像匹配中的应用,以及Fourier-Mellin图像匹配方法的实现,讨论双谱的性质,提出一种基于双谱的图像匹配方法,该方法不受高斯噪声的影响,实验结果表明,该方法成功实现了具有旋转、尺度、平移变换关系的图像之间的匹配。
(4)研究SIFT特征描述符及其降维问题,本文利用核主成分分析的特征提取方法,对每个特征点的SIFT描述符进行降维处理,然后建立它们的初始匹配,进而结合数字地形图,建立导航参数的约束方程,提出一种基于蚁群算法的最小截取二乘鲁棒估计方法,估计飞行器载相机的位置和姿态,实现运动平台的图像辅助导航。
Image aided navigation is inevitable tendency of integrated navigation system of vehicle.How to design an algorithm with high adaptability, precision and computing efficiency is the kernel study topic.Following the difficulties of IR/Optical images matching, this dissertation researches the key technologies of image aided navigation.The important contributions and creative achievements are summarized as follows:
(1)By analyzing the character and difference of IR image and optical image,the edge types that exist in common area of images are described as mathematical models.Some common extraction methods are discussed and analyzed. A edge detection algorithm based on Tsallis entropy is presented. The algorithm uses local entropy information,and the divergence-direction is obtained by sliding a window over an image. The edge pixel is linked from candidate pixels using direction information. Some experimental results have been provided to show the edge detection capability of the proposed algorithm.
(2)The edge-based image matching algorithms are studied. The algorithms include the similarity measurement based on 3-4DT, edge based similar distance(ESD),and the proposed similarity measurement based on distance transformation. Their performance on adaptability and precision are analysed and compared.The experiment results are also given. High computational complexity due to pixel-by-pixel search strategy is one major drawback. In this dissertation, a much faster algorithm to approximate the matching position between two images of a scene is described. Reduction on the computional complexity is achieved by using an the improved ACA in continuous space based search strategy,due to the non-exhaustive search nature of the algorithm.
(3)The image matching technique based on fourier transform and Fourier-Mellin algorithm can be used to register images which are misaligned due to rotation, scaling and translation.It presents a method of image matching algorithm based on bispectrum, it uses the property that bispectrum is not sensitive to Gauss noise, which can exactly estimates the parameters between images suffered from translation, rotation and scaling distortion.
(4)SIFT Operator has been proven to be the most robust local invariant feature descriptor,which has been widely thought of a success in image matching.The SIFT volumes of keypoints are reduced by kernel principal component analysis(KPCA).The coarse matching is established between two successive frames. A kind of highly robust estimation is presented,named least trimmed squares, on the data basis of the digital terrain map.The vehicle’s position ,attitude and motion parameters is estimated by the proposed algorithm.
引文
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