【摘要】：傾斜航空攝影技術(shù)是國(guó)際測(cè)繪領(lǐng)域近年發(fā)展十分迅速的一項(xiàng)高新技術(shù)，利用該技術(shù)獲取的傾斜影像可為三維模型重建提供豐富的紋理信息。傾斜航空攝影后期數(shù)據(jù)處理中采用多視影像匹配技術(shù)自動(dòng)化獲取海量匹配點(diǎn)數(shù)據(jù)，，但因傾斜影像的攝影比例尺不一致、分辨率差異明顯、地物遮擋嚴(yán)重等特點(diǎn)導(dǎo)致獲取的數(shù)據(jù)中含有較多的粗差，嚴(yán)重影響后續(xù)的多視影像空三加密精度。因此，探測(cè)傾斜航空攝影海量數(shù)據(jù)中的多維粗差顯得尤其重要。 目前現(xiàn)有的粗差檢測(cè)算法受其探測(cè)能力或處理效率的限制，無(wú)法準(zhǔn)確、高效的探測(cè)海量數(shù)據(jù)中的多維粗差；又由于傾斜航空影像的姿態(tài)角類(lèi)型多樣，致使影像間的相對(duì)位置關(guān)系復(fù)雜，傳統(tǒng)的連續(xù)相對(duì)定向模型不再適用。針對(duì)上述問(wèn)題，本文分別從粗差檢測(cè)算法和平差數(shù)學(xué)模型兩方面進(jìn)行研究解決。 1）本文基于相關(guān)分析粗差探測(cè)原則，提出了一種適合處理攝影測(cè)量領(lǐng)域海量數(shù)據(jù)的粗差檢測(cè)算法——攝影測(cè)量中的多維粗差同時(shí)定位和定值法，簡(jiǎn)稱(chēng)LEGEP法。通過(guò)模擬粗差實(shí)驗(yàn)證明，LEGEP法能夠準(zhǔn)確地定位海量數(shù)據(jù)中的多維粗差，同時(shí)求得各個(gè)粗差的數(shù)值大��；將LEGEP法與其他幾種具有代表性的粗差檢測(cè)算法進(jìn)行對(duì)比實(shí)驗(yàn)發(fā)現(xiàn)，LEGEP法能夠利用更少的迭代計(jì)算量探測(cè)出更多的粗差，顯著提高了平差精度，從而證明了該算法在探測(cè)能力和效率兩方面的優(yōu)越性。 2）本文提出采用直接解相對(duì)定向模型作為平差數(shù)學(xué)模型的方法。該模型的求解不需要任何參數(shù)的真值的近似值，即無(wú)需傾斜影像的姿態(tài)角初值，是一種適合傾斜航空影像匹配數(shù)據(jù)探測(cè)粗差的通用平差模型。 實(shí)驗(yàn)證明，本文提出的基于直接解相對(duì)定向模型，結(jié)合應(yīng)用LEGEP法的粗差探測(cè)方法，能夠有效地探測(cè)傾斜航空攝影海量數(shù)據(jù)中的多維粗差，切實(shí)地提高觀測(cè)數(shù)據(jù)的精度，在傾斜航空攝影實(shí)際數(shù)據(jù)處理中具有較高的應(yīng)用價(jià)值。
[Abstract]:Tilt aerial photography is a new and high technology developed rapidly in the field of international surveying and mapping in recent years. The tilt image obtained by this technique can provide rich texture information for 3D model reconstruction. In the later data processing of tilted aerial photography, the multi-view image matching technique is used to automatically acquire the massive matching point data. However, because of the inconsistency of the photographic scale of inclined aerial photography, the resolution difference is obvious. Because of the serious feature of object occlusion, there are many gross errors in the acquired data, which seriously affect the accuracy of space triple encryption in the subsequent multi-view images. Therefore, it is very important to detect the multi-dimensional gross error in the massive data of tilted aerial photography. At present, the existing gross error detection algorithms are limited by their detection ability or processing efficiency, so they can not accurately and efficiently detect the multi-dimensional gross error in massive data, and because of the variety of attitude angle types of inclined aerial images, Because of the complexity of the relative position relationship between images, the traditional continuous relative orientation model is no longer applicable. Aiming at the above problems, this paper studies and solves the problem from two aspects: gross error detection algorithm and mathematical model of error. 1) based on correlation analysis, gross error detection principle is used in this paper. This paper presents a gross error detection algorithm suitable for dealing with massive data in photogrammetry field, which is the simultaneous location and determination of multi-dimensional gross error in photogrammetry, which is referred to as LEGEP method. The simulation results show that the LGEP method can accurately locate the multi-dimensional gross error in the massive data and obtain the numerical value of each gross error at the same time. By comparing the LEGEP method with other typical gross error detection algorithms, it is found that the LEGEP method can detect more gross errors with less iterative computation, and the adjustment accuracy is improved significantly. Therefore, the superiority of the algorithm in detecting ability and efficiency is proved. 2) in this paper, a direct solution relative orientation model is proposed as a mathematical model of adjustment. The solution of the model does not require the approximate value of the true value of any parameter, that is, the initial value of the attitude angle of the tilt image, so it is a general adjustment model suitable for detecting gross error of the tilted aerial image matching data. Experimental results show that based on the direct solution relative orientation model and combined with the gross error detection method of LEGEP method, the multi-dimensional gross error can be effectively detected in large amount of inclined aerial photography data, and the accuracy of observation data can be improved effectively. It has high application value in the practical data processing of tilted aerial photography.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：P231

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