【摘要】：目的像方無(wú)人機(jī)影像多視匹配方法忽視了影像之間的幾何關(guān)系,而以MVLL(multi-view vertical line locus)為代表的物方多視匹配方法缺乏對(duì)地形之間相互約束的考慮。為此構(gòu)建一種融合兩類(lèi)多視匹配方法優(yōu)點(diǎn)的無(wú)人機(jī)影像物方多視匹配算法。方法在MVLL匹配結(jié)構(gòu)的基礎(chǔ)上添加半全局匹配的相容性約束,不僅繼承了原半全局算法對(duì)有弱紋理區(qū)域匹配效果好和物體邊緣突出的優(yōu)點(diǎn),而且擺脫了需制作核線影像的繁瑣過(guò)程;采用物方窗口SNCC(summed normalized cross correlation)一致性匹配測(cè)度計(jì)算方法,有效降低攝影角度和遮擋對(duì)匹配結(jié)果的影響;采用金字塔分層的策略以提高匹配的速度和可靠性。結(jié)果選取自主研制的旋翼無(wú)人機(jī)三軸穩(wěn)定平臺(tái)獲取了高分辨率無(wú)人機(jī)影像作為實(shí)驗(yàn)數(shù)據(jù),從匹配效果、新匹配測(cè)度性能和匹配精度3個(gè)方面對(duì)算法進(jìn)行了測(cè)試實(shí)驗(yàn)。本文算法整體匹配效果良好,物方窗口SNCC一致性匹配測(cè)度可有效消除匹配測(cè)度中的粗差,經(jīng)過(guò)測(cè)定本文匹配算法生成的點(diǎn)云數(shù)據(jù)的高程精度為0.049 m,即約為1個(gè)GSD(ground space resolution)對(duì)應(yīng)的地面大小。結(jié)論本文算法充分利用了無(wú)人機(jī)影像的多視信息進(jìn)行匹配計(jì)算,具有匹配效果好、魯棒性強(qiáng)和匹配精度高的優(yōu)勢(shì)。
[Abstract]:Objective the image multi-view matching method of image square UAV neglects the geometric relationship between images, while the object multi-view matching method represented by multi-view vertical line locus) lacks the consideration of the mutual constraint between terrain. In this paper, an object-square multi-view matching algorithm for UAV images is proposed, which combines the advantages of two kinds of multi-view matching methods. Based on the MVLL matching structure, the compatibility constraint of semi-global matching is added, which not only inherits the advantages of the original semi-global algorithm, but also has the advantages of good matching effect on weakly textured regions and prominent edges of objects. Moreover, it gets rid of the tedious process of making core line image, adopts the method of object window SNCC (summed normalized cross correlation) consistency matching measure, effectively reduces the influence of camera angle and occlusion on the matching result. Pyramid stratification strategy is adopted to improve the speed and reliability of matching. Results High-resolution UAV images were obtained from a self-developed three-axis stabilized platform for rotors. The algorithm was tested from three aspects: matching effect, performance of new matching measure and matching accuracy. The overall matching effect of this algorithm is good, and the coarse error in the matching measure can be effectively eliminated by the object window SNCC consistency matching measure. The height accuracy of the point cloud data generated by the matching algorithm in this paper is 0.049 m, that is, the ground size corresponding to about 1 GSD (ground space resolution). Conclusion the algorithm makes full use of the multi-view information of UAV images and has the advantages of good matching effect, strong robustness and high matching accuracy.
【作者單位】： 信息工程大學(xué);地理信息工程國(guó)家重點(diǎn)實(shí)驗(yàn)室;航空遙感技術(shù)國(guó)家測(cè)繪地理信息局重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(41501482) 地理信息工程國(guó)家重點(diǎn)實(shí)驗(yàn)室開(kāi)放研究基金項(xiàng)目(SKLGIE 2015-M-3-6和SKLGIE 2014-M-3-1) 航空遙感技術(shù)國(guó)家測(cè)繪地理信息局重點(diǎn)實(shí)驗(yàn)室開(kāi)放基金項(xiàng)目(2014B02)~~
【分類(lèi)號(hào)】：P231

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