發(fā)布時(shí)間：2018-06-29 22:01

  本文選題：多角度遙感影像 + 有理函數(shù)模型優(yōu)化　； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著衛(wèi)星立體成像能力的逐漸成熟,衛(wèi)星應(yīng)用數(shù)據(jù)需求也逐漸受到人們的廣泛關(guān)注,衛(wèi)星數(shù)據(jù)也從傳統(tǒng)的立體像對(duì)過渡為多角度影像。與傳統(tǒng)的二維平面圖像不同,通過衛(wèi)星影像和相應(yīng)的參數(shù)文件,可以對(duì)影像覆蓋區(qū)域完成三維重建。建筑物作為人類生活區(qū)域中最常見的一類地物,一直與我們息息相關(guān)。因此,本文利用多角度遙感影像,對(duì)建筑物區(qū)域完成立體匹配及重建,主要圍繞遙感影像通用成像模型,立體匹配及重建這三部分展開,其中,對(duì)通用傳感器模型的討論主要側(cè)重于有理函數(shù)模型優(yōu)化,立體匹配則針對(duì)建筑物自身特點(diǎn)采用針對(duì)性的技術(shù)手段完成影像聯(lián)合立體匹配工作,在完成上述流程的基礎(chǔ)上,利用基于有理函數(shù)模型的最小二乘算法和DSM數(shù)據(jù)融合插值技術(shù),實(shí)現(xiàn)對(duì)建筑物區(qū)域立體重建。在整個(gè)立體重建系統(tǒng)流程中,遙感影像傳感器模型建立了立體影像二維圖像坐標(biāo)到三維大地坐標(biāo)的坐標(biāo)變換關(guān)系。本文從最常見的廣義傳感器模型——有理函數(shù)模型入手,分析其優(yōu)勢(shì)與不足,介紹了兩種經(jīng)典模型優(yōu)化算法。進(jìn)一步的,針對(duì)優(yōu)化過程中控制點(diǎn)不足及非均勻分布問題,提出解決方案;最后,結(jié)合經(jīng)典算法,采用虛擬地面控制點(diǎn),實(shí)現(xiàn)了有理多項(xiàng)式系數(shù)的精化,為后文近似核線影像生成和立體重建奠定了基礎(chǔ)。在研究立體匹配算法之前,首先對(duì)多角度影像添加核線約束,獲得近似水平核線影像,保證多角度影像中同名像點(diǎn)處于行搜索范圍內(nèi)。進(jìn)而,以中間影像為基準(zhǔn)影像,針對(duì)建筑物區(qū)域包含大量的角點(diǎn)及邊緣信息,因此,采用點(diǎn)特征提取與匹配技術(shù)確定目標(biāo)影像匹配搜索范圍。同時(shí),對(duì)直線特征提取過程做出了針對(duì)性的改善,利用線特征匹配視差及線段分布位置關(guān)系,獲得最大最小視差分布圖,并以此約束區(qū)域匹配過程,建立基準(zhǔn)影像與左右目標(biāo)影像間稠密視差圖的生成。最后,為了實(shí)現(xiàn)真實(shí)場(chǎng)景下建筑物區(qū)域立體重建,首先研究了基于有理函數(shù)模型的最小二乘算法,實(shí)現(xiàn)匹配視差圖到三維信息的轉(zhuǎn)換,獲得稠密DSM點(diǎn)云數(shù)據(jù)。然后針對(duì)匹配過程中匹配不完全導(dǎo)致DSM數(shù)據(jù)值部分缺失問題,在物方空間實(shí)現(xiàn)DSM數(shù)據(jù)融合,并在保持建筑物邊緣特征的基礎(chǔ)上完成插值等后處理過程。與同一場(chǎng)景下Lidar數(shù)據(jù)以及立體像對(duì)數(shù)據(jù)相比較,獲得較好的實(shí)驗(yàn)結(jié)果。
[Abstract]:With the maturity of satellite stereo imaging ability, the demand for satellite application data has been paid more and more attention, and satellite data has been transformed from traditional stereo image to multi-angle image. Different from the traditional two-dimensional plane image, the 3D reconstruction of the image covering area can be completed by satellite image and the corresponding parameter file. As one of the most common features in human living areas, buildings are closely related to us all the time. Therefore, this paper uses multi-angle remote sensing image to complete the stereo matching and reconstruction of the building area, mainly focusing on the general imaging model, stereo matching and reconstruction of remote sensing image. The discussion of the universal sensor model mainly focuses on the rational function model optimization, and the stereo matching is based on the above process, and the corresponding technology is adopted to complete the image joint stereo matching according to the characteristics of the building itself. The least square algorithm based on rational function model and DSM data fusion interpolation technique are used to realize the stereoscopic reconstruction of building area. In the whole system of stereo reconstruction, the model of remote sensing image sensor establishes the coordinate transformation relationship between 2D image coordinate and 3D geodetic coordinate of stereo image. This paper begins with the most common generalized sensor model-rational function model, analyzes its advantages and disadvantages, and introduces two classical model optimization algorithms. Furthermore, to solve the problem of deficiency and non-uniform distribution of control points in the optimization process, a solution is proposed. Finally, the rational polynomial coefficients are refined by using virtual ground control points in combination with classical algorithms. It lays a foundation for the generation of approximate kernel line image and stereo reconstruction. Before the stereo matching algorithm is studied, the kernel line constraint is added to the multi-angle image firstly, and the approximate horizontal kernel line image is obtained to ensure that the image with the same name in the multi-angle image is within the range of row search. Furthermore, with the intermediate image as the reference image, the building area contains a lot of corner and edge information, so the point feature extraction and matching technology is used to determine the target image matching search range. At the same time, the line feature extraction process has been improved, using line feature matching parallax and line segment distribution relationship, the maximum and minimum parallax distribution map is obtained, and the matching process of constrained region is obtained. Build the dense parallax image between the reference image and the left and right target image. Finally, in order to realize the stereo reconstruction of the building area in the real scene, the least square algorithm based on rational function model is studied firstly, and the matching parallax map is transformed into 3D information, and the dense DSM point cloud data is obtained. Then to solve the problem of partial missing of DSM data value caused by incomplete matching, the DSM data fusion is realized in the physical space, and the post-processing process such as interpolation is completed on the basis of preserving the building edge features. Compared with Lidar data and stereo pair data in the same scene, good experimental results are obtained.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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本文編號(hào)：2083514