本文選題：特征點(diǎn)匹配　切入點(diǎn)：尺度比　出處：《西南交通大學(xué)》2017年碩士論文

【摘要】：影像匹配技術(shù)是影像處理的一項(xiàng)重要技術(shù),被廣泛應(yīng)用于多個(gè)領(lǐng)域。在遙感領(lǐng)域中,遙感技術(shù)已經(jīng)應(yīng)用于災(zāi)害、資源環(huán)境、農(nóng)業(yè)、林業(yè)等多個(gè)方面,伴隨著遙感技術(shù)的快速發(fā)展,遙感技術(shù)的應(yīng)用范圍在人類生活中將會(huì)進(jìn)一步擴(kuò)大。多數(shù)影像的處理與應(yīng)用,需要多源數(shù)據(jù)、多幅影像的共同應(yīng)用,如影像融合提高影像分別率,通過不同時(shí)段的同一地區(qū)影像疊加進(jìn)行變化檢測,立體像對(duì)空中三角測量等。在多源、多幅影像處理中,影像匹配技術(shù)是一項(xiàng)基本的處理技術(shù),影像匹配中最常用且穩(wěn)定的特征匹配是基于影像特征點(diǎn)的匹配。因此,穩(wěn)定且性能好的影像特征點(diǎn)匹配可以實(shí)現(xiàn)影像的自動(dòng)匹配,使影像得到更好的應(yīng)用。影像特征點(diǎn)自動(dòng)匹配中多采用Lowe D G提出的尺度不變特征變換(Scale-Invariant Feature Transform,SIFT)方法實(shí)現(xiàn)對(duì)影像的特征點(diǎn)提取并描述,進(jìn)而實(shí)現(xiàn)特征點(diǎn)匹配。由于其性能優(yōu)越,在影像匹配中得到廣泛應(yīng)用。然而,在影像紋理重復(fù)性高、影像非線性灰度差異等情況下,基于特征點(diǎn)描述的匹配方法可能出現(xiàn)特征點(diǎn)的錯(cuò)誤匹配,將無法滿足實(shí)際需求。為了實(shí)現(xiàn)影像特征點(diǎn)正確匹配,并且獲得更多的正確匹配點(diǎn)對(duì),以滿足影像匹配的需求,本論文從特征點(diǎn)匹配階段著手,優(yōu)化了特征點(diǎn)匹配性能。本論文的具體工作如下:(1)在影像特征點(diǎn)匹配中,影像特征點(diǎn)匹配階段采用特征向量的最近鄰與次近鄰比值約束,將會(huì)出現(xiàn)正確匹配點(diǎn)的遺漏以及錯(cuò)誤匹配點(diǎn)的誤檢。為了實(shí)現(xiàn)正確匹配點(diǎn)盡量多地被檢測出來且不存在特征點(diǎn)的錯(cuò)誤匹配,論文充分利用特征點(diǎn)的尺度與定位信息,以尺度比和坐標(biāo)偏移約束實(shí)現(xiàn)影像特征點(diǎn)的匹配。(2)以尺度比和坐標(biāo)偏移約束進(jìn)行影像特征點(diǎn)的匹配,并與SIFT方法進(jìn)行實(shí)驗(yàn)比較。不同時(shí)間、存在模糊變化以及尺度與旋轉(zhuǎn)變化影像的實(shí)驗(yàn)結(jié)果表明論文方法的最終特征點(diǎn)正確匹配數(shù)得到增加,匹配點(diǎn)的均方根誤差有所下降。(3)在影像特征點(diǎn)匹配中,剔除匹配點(diǎn)集中錯(cuò)誤的匹配點(diǎn)對(duì),才能保證影像匹配的正確性。為了剔除誤匹配的特征點(diǎn),論文分析了特征點(diǎn)的空間關(guān)系,將空間關(guān)系融入到特征點(diǎn)匹配中,保證特征點(diǎn)集中匹配點(diǎn)的正確性。(4)論文在初始特征匹配點(diǎn)集的基礎(chǔ)上,融入特征點(diǎn)的空間關(guān)系對(duì)初始點(diǎn)集進(jìn)行優(yōu)化,并進(jìn)行了多組實(shí)驗(yàn)驗(yàn)證論文方法的可靠性。對(duì)存在尺度與旋轉(zhuǎn)、模糊與尺度變化、異源的影像進(jìn)行實(shí)驗(yàn),并與GTM方法、RSOC方法進(jìn)行實(shí)驗(yàn)對(duì)比,實(shí)驗(yàn)結(jié)果表明論文方法有較高的穩(wěn)定性。
[Abstract]:Image matching is an important technology in image processing, which is widely used in many fields.In the field of remote sensing, remote sensing technology has been applied to disasters, resources, environment, agriculture, forestry and other aspects. With the rapid development of remote sensing technology, the application of remote sensing technology in human life will be further expanded.The processing and application of most images require the common application of multi-source data and multi-image, such as image fusion to improve the image separation rate, image superposition of different periods of time to detect changes, stereoscopic aerial triangulation and so on.In multi-source and multi-image processing, image matching technology is a basic processing technology. The most common and stable feature matching in image matching is based on image feature points.Therefore, the feature point matching with stable and good performance can realize automatic image matching and make the image be better applied.Scale-Invariant Feature transform (sift) method proposed by Lowe D G is used to extract and describe feature points in image feature points automatic matching, and then feature point matching is realized.Because of its superior performance, it is widely used in image matching.However, in the case of high texture repeatability and nonlinear gray difference, the matching method based on the description of feature points may have a false matching of feature points, which will not meet the actual needs.In order to achieve the correct matching of image feature points and obtain more correct matching point pairs to meet the needs of image matching, this paper starts with the feature point matching stage, and optimizes the feature point matching performance.The specific work of this paper is as follows: (1) in the image feature point matching, the nearest neighbor and the next nearest neighbor ratio of the feature vector are used in the image feature point matching stage, and the omission of the correct matching point and the false detection of the wrong matching point will occur.In order to realize the error matching of correct matching points which are detected as much as possible and there are no feature points, the paper makes full use of the scale and location information of feature points.The scale ratio and coordinate migration constraints are used to match the image feature points. (2) the scale ratio and coordinate migration constraints are used to match the image feature points, and the experimental results are compared with the SIFT method.At different times, the experimental results show that the correct matching number of the final feature points is increased, and the root mean square error (RMS) error of the matching points is decreased in the image feature point matching.The correctness of image matching can be ensured only by eliminating the matching point pairs of mismatch points in the set of matching points.In order to eliminate the feature points of mismatch, the spatial relationship of feature points is analyzed, and the spatial relationship is integrated into the matching of feature points to ensure the correctness of matching points in feature points set.The initial point set is optimized with the spatial relationship of feature points, and the reliability of the method is verified by many experiments.Experiments are carried out on images with scale and rotation, fuzzy and scale changes, and heterogenous images, and compared with GTM method. The experimental results show that the proposed method has high stability.
【學(xué)位授予單位】：西南交通大學(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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