基于点线特征匹配的无人机影像拼接技术
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摘要
无人机遥感具有机动、灵活、高效、低成本等特点,能有效改善多云雾地区高分辨率遥感数据缺乏的现状,在土地和矿产资源管理、地质环境评估与灾害防治、空间信息快速采集和地形图局部更新等领域有着广泛的应用前景。但是无人机航高一般较低,所获单幅影像覆盖范围较小,为了解决影像视场范围与分辨率之间的矛盾,及时、准确地反映整个区域情况,需要将所获取的影像进行匹配并拼接形成全图。
本论文首先介绍了无人机遥感系统的组成,详细的分析了无人机航线规划参数的设置,并对获取的影像进行了畸变差校正及匀光处理。根据无人机影像的特点及规律,对影像的重叠区域进行估算与处理,有效地减小了匹配时的计算量。对于特征不明显或特征点较为稀少的影像,则采用点特征与线特征相结合的方法进行匹配。在进行大量无人机影像拼接时,为了控制误差累积,提出了最佳重叠度、最佳基准面及最佳拼接路径等策略。具体来讲,本论文的主要研究工作和创新性成果有以下几点：
1、根据无人机飞行参数,初步估算出相邻影像的重叠区域M1,利用相位相关法计算出相邻影像重叠区域M2,最终影像的重叠区域取两者的均值M。该方法即避免了M1的扩大,又控制了M2过分缩小。通过计算影像的重叠区域,减小了产生特征点的影像范围,缩短了产生特征点的时间。通过测试获取了适用于无人机影像的最优高斯核尺寸,实验证明在该尺寸下产生的特征点的数量和精度都是最优的,同时该尺寸相对于固定核尺寸,其时间效率提高了近20%。
2、针对一些特殊地区的无人机影像,即地物特征稀少或不明显的影像,本论文提出了点特征与线特征相结合的匹配方法,在利用点特征完成初步匹配的基础上,以线特征作为补充进一步对其进行精匹配。该方法充分利用点特征与线特征互补的特性,使得影像匹配的鲁棒性提高了10%左右。
3、针对无人机影像拼接过程中的误差累积问题,本论文提出了最佳重叠度、最佳基准面及最佳拼接路径三个控制策略。通过实验测试,获得了无人机影像最佳重叠度范围是25%-37.5%,根据这一数据,对无人机影像在拼接之前做出了“抽稀”处理。将遥感影像的信息熵作为选择最佳基准面的依据,实验证明利用信息熵选择出的基准影像都能够产生丰富的特征点,易于同相邻影像匹配。采用蚁群算法搜索影像的拼接路径,实验表明该算法能够准确快速的搜索出最佳拼接路径。
4、通过对无人机航线规划中各个参数的分析,找出了各个参数之间的相互关系,并根据它们之间的关系提出了最佳航线设计的方法。利用该方法设计的航线获取的无人机影像质量得到了有效的改善,为影像匹配提供了良好的数据源。采用附加参数光束法平差的系统畸变模型对无人机影像中的畸变差进行校正,校正后的影像消除了畸变对匹配造成的影响；采用直方图调整的方法对影像进行灰度调整,通过灰度调整,消除了曝光不均对匹配造成的影响。
The UAV (Unmanned Aerial Vehicle) remote sensing has advantages of obvious flexibility, maneuvering, high efficiency, low cost and the ability to improve the status of the lack of high resolution remote sensing data in cloudy and misty areas effectively, and thus make it be broadly applied in the field of management of land and mineral resources, geological environment evaluation and disaster prevention, rapid acquisition of space information and topographic map update. Therefore, the low altitude photogrammetry based on UAV remote sensing platform and ordinary digital camera has become a hot research pot in the field of remote sensing and photogrammetry both domestic and overseas. However, because the flying height of UAV is low in general, single image obtained has less coverage. In order to solve the contradiction between image coverage and resolution, and to reflect the situation over the study area accurately and in time, it is needed to match the obtained images and mosaic them together to form a full scenes.
This dissertation first introduces the composition of UAV remote sensing system, analyzes the planning parameter setting of UVA flight route, and carries out distortion correction and dodging for the obtained images. Then SIFT algorithm is introduced into the UAV image matching, and the thorough research to the algorithm is carried on. According to the characteristics and patterns of the UAV images, this dissertation estimates the overlap area of the images before using the SIFT algorithm in image matching, which reduces the computation burden of the matching effectively. For UAV images in difficult areas, in terms of the image feature is not obvious or relatively scarce, we adopt a method combines point and line features for the matching. When doing UAV image stitching, splicing error accumulations is a problem that cannot be ignored. In order to control error accumulations, three strategies are proposed:the best overlapping degree strategy, the best datum strategy and the best stitching path strategy. Specifically, the research work and innovations in this dissertation are mainly the following several aspects:
1. According to the parameters of UAV (Unmanned Aerial Vehicle), estimates the overlap of adjacent images firstly, calculates the overlap areas of adjacent images by using phase correlation method, take the average of the overlap areas of final images. This method not only avoids the expanding, but also controls the excessive the narrowing. Through the calculation of overlap area of images, reduces the scope of image which produced the feature points, shorts the time cost of production of feature points. Obtaining the optimal Gaussian nuclear size that applies to the images of UAV, the experiment shows that the feature points' number and precision are optimal which are produced under this size, and this size relative to the fixed nuclear size, its time efficiency to increase by almost20%.
2. Aiming to the UAV images in some difficult areas, that is to say the object features of images are scarce or not obvious, this dissertation proposes a match method which combined point features and line features, on the basis of completing preliminary matches by using the point features, carrying out the further fine matching by using line features as a supplement. The method makes full use of complementary characteristic of point features and line features, and makes the robustness of image matching is improved by about10%.
3. Aiming the error accumulation problem in the process of UAV images stitching, there are three strategies is proposed:the best overlapping degree, the best datum and best stitching path. Through the experiment testing, the range of the best overlap degree of UAV image is25%～37.5%, according to this number, applying rarefies to the UAV images before joining together. Remote sensing image information entropy as a basis to choose the best datum, experiments prove that using information entropy to choose the reference images are able to produce rich feature points, and it is easy to be with adjacent image matching. Searching the path of image splicing by using Ant Colony Algorithm, experiments show that this algorithm can quickly and accurately search the best stitching path.
4. Through the analysis of the various parameters in UAV route planning, finds out the relationship between each parameter, based on the relationship between them, and proposes the method to best route design. The quality of UAV images which are obtained by using the route is designed by this method is improved effectively, provides a good data source for image matching. The distortion of UAV images is corrected by the system distortion model which is adjusted by the beam method of additional parameters, corrected images eliminates the impact of distortion to matching. Carrying on gray-scale adjustment to images by using the method of histogram adjustment, through gray-scale adjustment, eliminates the impact of uneven exposure to matching.
本论文首先介绍了无人机遥感系统的组成,详细的分析了无人机航线规划参数的设置,并对获取的影像进行了畸变差校正及匀光处理。根据无人机影像的特点及规律,对影像的重叠区域进行估算与处理,有效地减小了匹配时的计算量。对于特征不明显或特征点较为稀少的影像,则采用点特征与线特征相结合的方法进行匹配。在进行大量无人机影像拼接时,为了控制误差累积,提出了最佳重叠度、最佳基准面及最佳拼接路径等策略。具体来讲,本论文的主要研究工作和创新性成果有以下几点：
1、根据无人机飞行参数,初步估算出相邻影像的重叠区域M1,利用相位相关法计算出相邻影像重叠区域M2,最终影像的重叠区域取两者的均值M。该方法即避免了M1的扩大,又控制了M2过分缩小。通过计算影像的重叠区域,减小了产生特征点的影像范围,缩短了产生特征点的时间。通过测试获取了适用于无人机影像的最优高斯核尺寸,实验证明在该尺寸下产生的特征点的数量和精度都是最优的,同时该尺寸相对于固定核尺寸,其时间效率提高了近20%。
2、针对一些特殊地区的无人机影像,即地物特征稀少或不明显的影像,本论文提出了点特征与线特征相结合的匹配方法,在利用点特征完成初步匹配的基础上,以线特征作为补充进一步对其进行精匹配。该方法充分利用点特征与线特征互补的特性,使得影像匹配的鲁棒性提高了10%左右。
3、针对无人机影像拼接过程中的误差累积问题,本论文提出了最佳重叠度、最佳基准面及最佳拼接路径三个控制策略。通过实验测试,获得了无人机影像最佳重叠度范围是25%-37.5%,根据这一数据,对无人机影像在拼接之前做出了“抽稀”处理。将遥感影像的信息熵作为选择最佳基准面的依据,实验证明利用信息熵选择出的基准影像都能够产生丰富的特征点,易于同相邻影像匹配。采用蚁群算法搜索影像的拼接路径,实验表明该算法能够准确快速的搜索出最佳拼接路径。
4、通过对无人机航线规划中各个参数的分析,找出了各个参数之间的相互关系,并根据它们之间的关系提出了最佳航线设计的方法。利用该方法设计的航线获取的无人机影像质量得到了有效的改善,为影像匹配提供了良好的数据源。采用附加参数光束法平差的系统畸变模型对无人机影像中的畸变差进行校正,校正后的影像消除了畸变对匹配造成的影响；采用直方图调整的方法对影像进行灰度调整,通过灰度调整,消除了曝光不均对匹配造成的影响。
The UAV (Unmanned Aerial Vehicle) remote sensing has advantages of obvious flexibility, maneuvering, high efficiency, low cost and the ability to improve the status of the lack of high resolution remote sensing data in cloudy and misty areas effectively, and thus make it be broadly applied in the field of management of land and mineral resources, geological environment evaluation and disaster prevention, rapid acquisition of space information and topographic map update. Therefore, the low altitude photogrammetry based on UAV remote sensing platform and ordinary digital camera has become a hot research pot in the field of remote sensing and photogrammetry both domestic and overseas. However, because the flying height of UAV is low in general, single image obtained has less coverage. In order to solve the contradiction between image coverage and resolution, and to reflect the situation over the study area accurately and in time, it is needed to match the obtained images and mosaic them together to form a full scenes.
This dissertation first introduces the composition of UAV remote sensing system, analyzes the planning parameter setting of UVA flight route, and carries out distortion correction and dodging for the obtained images. Then SIFT algorithm is introduced into the UAV image matching, and the thorough research to the algorithm is carried on. According to the characteristics and patterns of the UAV images, this dissertation estimates the overlap area of the images before using the SIFT algorithm in image matching, which reduces the computation burden of the matching effectively. For UAV images in difficult areas, in terms of the image feature is not obvious or relatively scarce, we adopt a method combines point and line features for the matching. When doing UAV image stitching, splicing error accumulations is a problem that cannot be ignored. In order to control error accumulations, three strategies are proposed:the best overlapping degree strategy, the best datum strategy and the best stitching path strategy. Specifically, the research work and innovations in this dissertation are mainly the following several aspects:
1. According to the parameters of UAV (Unmanned Aerial Vehicle), estimates the overlap of adjacent images firstly, calculates the overlap areas of adjacent images by using phase correlation method, take the average of the overlap areas of final images. This method not only avoids the expanding, but also controls the excessive the narrowing. Through the calculation of overlap area of images, reduces the scope of image which produced the feature points, shorts the time cost of production of feature points. Obtaining the optimal Gaussian nuclear size that applies to the images of UAV, the experiment shows that the feature points' number and precision are optimal which are produced under this size, and this size relative to the fixed nuclear size, its time efficiency to increase by almost20%.
2. Aiming to the UAV images in some difficult areas, that is to say the object features of images are scarce or not obvious, this dissertation proposes a match method which combined point features and line features, on the basis of completing preliminary matches by using the point features, carrying out the further fine matching by using line features as a supplement. The method makes full use of complementary characteristic of point features and line features, and makes the robustness of image matching is improved by about10%.
3. Aiming the error accumulation problem in the process of UAV images stitching, there are three strategies is proposed:the best overlapping degree, the best datum and best stitching path. Through the experiment testing, the range of the best overlap degree of UAV image is25%～37.5%, according to this number, applying rarefies to the UAV images before joining together. Remote sensing image information entropy as a basis to choose the best datum, experiments prove that using information entropy to choose the reference images are able to produce rich feature points, and it is easy to be with adjacent image matching. Searching the path of image splicing by using Ant Colony Algorithm, experiments show that this algorithm can quickly and accurately search the best stitching path.
4. Through the analysis of the various parameters in UAV route planning, finds out the relationship between each parameter, based on the relationship between them, and proposes the method to best route design. The quality of UAV images which are obtained by using the route is designed by this method is improved effectively, provides a good data source for image matching. The distortion of UAV images is corrected by the system distortion model which is adjusted by the beam method of additional parameters, corrected images eliminates the impact of distortion to matching. Carrying on gray-scale adjustment to images by using the method of histogram adjustment, through gray-scale adjustment, eliminates the impact of uneven exposure to matching.
引文
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