摘要
随着人类社会的快速发展,人们迫切需要各种真实空间三维信息用于生活服务、科研模拟、辅助决策支持等多种实际应用,而各种空间信息数据载体中,最容易获取的测绘基础数据是各种航空光学影像数据。如何有效的利用这些航空影像数据,实现影像数据到真实空间三维信息的转化,进而为各种科学研究提供基础数据支持,成了迫切需要解决的难题。传统的摄影测量研究领域所采用的各种特征点检测方式,提取的同名点对数目相对较少,对影像像素的利用率过低。与此同时,计算机视觉领域近些年发展的稠密立体匹配技术可以在核线改正图像间进行稠密立体匹配,实现整幅图像的逐像素匹配,从而提取出大量同名像素点,这大幅度提高了影像像素的利用率。因此,有必要在航空核线影像的基础上进行逐像素稠密立体匹配技术研究,进而识别出大量的同名像素点对,为航空影像的空间信息自动提取研究提供一种新的解决方法。
在航空核线影像间进行逐像素稠密立体匹配研究,与传统的计算机视觉中的稠密立体匹配有所不同。这种不同,不仅仅是因为算法本身存在的适应性问题,更是因为航空影像面临着更多的干扰：影像分辨率高、同名像素点存在严重色差、大面积的低纹理与重复纹理、移动物体等。在国内外研究中,逐像素稠密立体匹配算法主要应用于各种计算机视觉领域的低分辨率图像处理中,应用到航空影像等高分辨率影像处理的成熟案例极少。本文从这一难题出发,充分考虑计算机视觉中各种稠密立体算法的匹配正确率、算法拓展性、计算加速可行性等多方面特性,最终以置信度传播算法为基础进行拓展,并进行了基础理论研究与实验验证。本文的具体研究工作主要包括了以下三个方面：
1)拓展置信度传播算法用于航空核线影像的逐像素稠密立体匹配处理。在传统置信度传播算法的基础上,针对航空核线影像的同名像素点密集匹配问题,本文提出了一种多通道置信度传播算法。算法对航空核线影像中同名点存在严重色差这一主要干扰源进行针对性处理,充分利用了影像的各颜色分量,提高了背景区域像素点的匹配正确率,从而自动隔离出了移动物体等多种局部干扰源；同时,算法设计时减少了控制参数数目,简化了影像逐像素稠密立体匹配处理中的参数设置问题。
2)研究各种立体匹配控制技术的集成方法,进一步提升逐像素稠密立体的匹配正确率。在多通道置信度传播算法的基础上,1,研究集成彩色分割结果图控制地物边界；2,提出一种基于窗口模式的匹配代价生成技术进一步降低同名点色差的影响；3,使用视差图后处理技术提高同名点匹配的可靠度,提出一种基于彩色分割图的视差图增强技术,并最后进行亚像素估计。
3)研究并行计算加速技术以减少稠密立体匹配的处理时间。本文研究了任务并行处理与数据并行处理两种计算模式,结果表明两种并行处理模式都能大幅度缩短匹配算法的处理时间,取得了一定的加速比。相对于任务并行模式,数据并行模式可以提供更高的加速比,其数据可以分布式存储与处理,更合适用于现今广泛投入使用的各种并行处理设备中,从而充分利用并行机中多节点的计算能力。
With the rapid development of society, there are urgent needs for real spatial3-dimentional information to sustain practical applications, such as life services? science research, and decision support. Of all types of spatial information carriers, optical images are easiest-requiring only basic surveying and mapping data. Aerial images are plentiful and widely used given the rapid development of aerial technologies. Problems which need to be urgent solved, however, include the effective use aerial images and techniques to convert image data into real spatial information that provides basic data support for science research.
Traditional Photogrammetry research typically focuses on feature point detection technologies to search corresponding pixels. However, these detected corresponding pixels are always few in number, and therefore, utilization of images is low. Separately at the same time, many dense stereo matching technologies using dense matching between rectified pictures were developed in the Computer Version (CV) field. These technologies can identify large quantities of corresponding pixels from rectified pictures, allowing greater use of images. Thus, there is an unexplored potential to apply dense stereo matching technologies to indentify a large number of corresponding pixels in aerial epipolar images, this method provides a new solution for spatial information automatic acquiring from aerial images.
Dense stereo matching technologies are often applied in low-resolution picture processing in CV, and are rarely applied in high-resolution aerial image matching processes. Dense stereo matching between aerial epipolar images is somewhat different from dense stereo matching in CV field. The differences, are not only caused by the adaptation of algorithms, but also caused by aerial images themselves. There is more interference in aerial images, given that:the resolutions are much higher, the corresponding pixels have serious color deviation, larger texture-less areas or texture-repetitive areas, moved objects, and so on.
Taking the matching accuracy, expansibility, and computational acceleration feasibility of the dense matching algorithm into consideration, this paper presents a belief propagation algorithm for basic theoretical research and experimental verification. The research work in this paper has the three following parts.
First, a multi-channel belief propagation theory is developed for dense stereo matching between aerial epipolar images. Evolved from the traditional Belief Propagation (BP) algorithm, a multi-channel belief propagation algorithm makes full use of all the three color components, R, G and B, and simplifies the process. The impact of the obvious color differences between corresponding pixels is the greatest interference in aerial image matching processing. In order to reduce this impact, a multi-channel belief propagation algorithm reduces the sensitivity of central pixels and increases the contribution of neighboring pixels.
Second, many control technologies are integrated into the proposed multi-channel belief propagation algorithm for accuracy improvement. Based on the basic multi-channel belief propagation algorithm, a color segment result is used for controlling object boundaries, a window-based matching cost generation technology is applied to suppress interference caused by serious color deviation, and a disparity space images post-processing technology is applied for disparity classification and sub-pixel estimation.
Three, parallel computational technologies are tested. The execution time of BP based algorithms is always very long and the resolution of aerial images is very high, this means that a multi-channel belief propagation algorithm needs computation acceleration technology to reduce execution time. Task parallelism and data parallelism are tested and compared. Both technologies can reduce execution time drastically. Data parallelism provided a larger speedup. Because of image data's distributed storage and distributed processing, data parallelism is more suitable for current large parallel hardware, especially for multi-node-containing computers.
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