近年來，影像匹配技術已發展至密匹配 (dense matching)、甚至為逐像元匹配 (pixelwise matching) 的新紀元，藉由密匹配成果來產製DTM、正射影像、城市模型等攝影測量相關產品為目前的新趨勢，為了提升密匹配後續應用成果之精度和可靠度，密匹配成果之偵錯與品質評估，將成為必要之步驟。然而密匹配成果輸出之資料，為了縮短後續產品製作時間，而將密匹配點經前方交會，輸出物點雲三維地面坐標，此時密匹配偵錯與品質評估面臨一些瓶頸，包括(1) 無原始匹配點像坐標、(2) 匹配點數量龐大、(3) 相鄰匹配點之距離太近而產生相關參數的高相關，導致解算不穩定之現象。本文利用目視檢查、相對方位計算、像片三角計算以及獨立測量偵錯法，由人工介入至自動化方法進行密匹配成果之偵錯與品質評估，亦透過求得相對方位五個元素相同解與密點雲疏化，以解決密匹配偵錯之瓶頸問題。
本文使用17張空照影像進行測試，經匹配演算法SMM、SfM、DAISY、SGM得到的匹配點密度分別為5.66×10-5、1.31×10-4、3.18×10-2、6.69 點/像元。RO演算法自動挑選出932個均勻分布之匹配點，可得到相對方位五個元素相同解，再對所有匹配點進行偵錯與品質評估，成果顯示SMM、SGM錯誤率分別為2.82 %、2.36 %；共面不符值的均方根值分別為0.36 mm2、0.0006 mm2，密匹配錯誤點的位置 (和比例) 分別為：0階不連續面 (47.00 %)、1階不連續面 (50.72 %)、均調區 (0.05 %) 與樹林區 (2.23 %)。密點雲經由罩窗之篩選，以分批進行像片三角計算，可降低點雲資料量，並解決相鄰匹配點之距離短，導致像片三角測量參數高相關和解算不穩定的問題。相對方位與像片三角計算之平均偵錯速度，分別約14,984 匹配點/秒、292 匹配點/秒。獨立測量成果顯示，SGM密點雲與佈標點高程之差值絕對值，最大值為0.935 GSD、最小值0.006 GSD、平均值0.315 GSD、RMSD等於0.238 GSD，GSD為0.168 m。由以上之實驗說明，本文提出的四種偵錯法可有效解決密匹配成果偵錯之瓶頸問題。

This study presents four methods for blunder detection and quality evaluation on dense matching results. They are visual check, relative orientation (RO) using a huge number of tie points, bundle block adjustment, and comparing with ground truth data. To detect blunders, not only 3D object points but also 2D image points need image information. Therefore, there are bottlenecks, which include (1) no original image coordinates of matching points, (2) a huge number of matching points, and (3) the matching points in close distance making the calculation of bundle block adjustment unstable. The most probable values of RO five unknown elements are calculated and sparse dense points are selected to solve the bottlenecks in blunder detection. In this study, test data are the results of four matching algorithms. They are SIFT-based Multi-image Matching (SMM), Structure from Motion (SfM), DAISY and Semi-Global Matching (SGM). The result shows that dense point clouds in areas with break lines, roof ridge lines and shadow are prone to have more blunders than other areas. According to RO computation, the matching error percentage is 2.82% and 2.36% by SMM and SGM, respectively. From aerial triangulation, the matching accuracy and error percentage of SMM are 0.23pixel and 3.97%. The computation speed of RO and bundle block adjustment are 14,984 points/second and 292 points/second, separately. We compare the dense points determined by SGM with ground truth data.The absolute elevation differences show that the maximum is 0.935GSD, minimum is 0.006GSD, average is 0.315GSD and root mean square difference is 0.238GSD, where GSD is 0.168m.

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