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空照數位像機拍攝高重疊影像匹配高密度點雲

3D Point Cloud Determination by Using Highly Overlapping Images Taken from Digital Aerial Cameras

摘要


空照數位像機可拍攝高前後重疊率80%~90%的空照影像,大幅減少影像遮蔽區,在航空攝影測量上,高重疊的空照數位影像對於自動化特徵點萃取、追蹤、匹配有其良好的應用潛力。 本文運用光流迭代法在30個像元以內可以成功追蹤同名像點的能力,針對現今高重疊率空照數位影像,設計出一套自動化萃取大量特徵點並加以追蹤、匹配其同名點群的方法,實驗成果驗證這一個新方法確實可以匹配出平均約1.5平方公尺(約9 x 9個像元)便有一個匹配點(本文的實驗影像的一個像元大小對應於地面是0.13m x 0.13m)的高密度點雲,而傳統航空攝影測量中不易產生連結點的區域,如遮蔽區、陰影區、樹表面等也可因此大幅提高匹配點密度,以本文遮蔽區爲例,平均約6.8平方公尺(約20 x 20個像元)便有一個匹配點,樹表面平均約0.2平方公尺(約3 x 3 個像元)便有一個匹配點。使用空中三角測量平差法來檢核新方法匹配點的正確性,此法追蹤匹配成果輸入空三計算,共6張影像7473個連結點,僅有50個錯誤點,正確率達99.3%。 此法成功匹配出高密度的大量三維點雲,可以多張影像立體判讀顯示並檢核之,具有多餘觀測、可互相檢核,大幅提升可靠度,可供後續各式的應用,例如求定數值地形模型、三維城市建模、重建樹木表面模型。

並列摘要


Digital aerial cameras can take highly overlapping aerial images with the end lap percentage of about 85%~90%. These images have less occlusion areas. For aerial photogrammetry, such aerial images possess a good application potential for automatic extraction, tracking and matching of feature points. This paper utilizes the successful tracking ability of the iterated optical flow approach within the tracking distance of less than 30 pixels, and develops a new method for image matching and determining the corresponding 3D points. Test results verify that it can determine about one point per 1.5m^2 (≈9×9pixels). Also, it can automatically determine one point per 6.8m^2 (≈20×20pixels) and one point per 0.2m^2 (≈3×3pixels) in occlusion and tree areas, respectively. Moreover, those matched points are checked by aero triangulation. For example, totally 7473 tie points on 6 images are regarded to be successfully matched from 6 overlapping images in a strip by the proposed method. Only 50 tie points are mismatched points detected by reliability theory. In other word, there is about 99.3% of the matched points which are correct and determined by the method. This study proposes the new method for image matching using highly overlapping aerial images. It provides more redundancy and more reliable matching results than the ones done by traditional stereo pair. A huge number of matched points can be then applied to determine a dense cloud of 3D object points for later applications such as DTM determination and 3D cyber city modeling.

被引用紀錄


黃美甄(2014)。地面控制點對無人飛行載具數值地形模型精度影響之評估〔碩士論文,國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841%2fNTUT.2014.00927
申承翰(2013)。無人飛行載具影像數值地形模型建置及精度評估〔碩士論文,國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841%2fNTUT.2013.00164
蔡欣穎(2015)。無人載具產製向陽國家森林遊樂區地表地形之研究〔碩士論文,國立屏東科技大學〕。華藝線上圖書館。https://doi.org/10.6346%2fNPUST.2015.00249
翁婕晞(2013)。應用多視角影像於UAV航拍遮蔽區之地形重建〔碩士論文,國立臺北大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0023-3107201317390600
連中豪(2013)。宜蘭清水溪流域河道變化及輸砂行為分析〔碩士論文,國立臺灣師範大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0021-0801201418030222

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