球形環景影像(Spherical Panorama Images, SPIs) 擁有廣大的視場角(Field of View, FOV)，可提供豐富的影像資訊，近年來引起許多重視。當測站影像外方位資訊已知，可利用空間前方交會求得物點坐標，進行量測應用。本研究透過可攜式環景影像測繪系統(Portable Panoramic Image Mapping System, PPIMS) 來取得球形環景影像，並應用此影像來進行攝影測量和製圖，探討其應用於物點定位之可行性。此系統由一特製平台裝載八台相機以及一個GNSS接收儀所組成，每架設一站，能夠同時拍攝八張影像，並以e-GNSS系統來定位。經過系統方位率定後，可透過準確的相機相對關係，將PPIMS在同個時刻拍攝得的八張影像拼接成球形環景影像。此球形環景影像之外方位可透過多測站SPI之光束法平差來取得。
無論是在解算影像外方位或空間前方交會的過程中，都必須得知共軛點在影像間的位置。一般而言，影像匹配技術被認為是可快速獲得影像間共軛點位置的方式。本研究發展了一種基於區域式的多重影像匹配策略(Area-based Matching, ABM)，透過核幾何約制，在影像上進行自動化搜尋，獲取目標點位在其他張環景影像上之的共軛點位置，並求得該點坐標。使用SNCC (Sum of Normalized Cross Correlation)與YARD (Yet Another Reconstruction Dataprogram)兩種指標來進行相似度計算，並在搜尋範圍中產生相似曲線，其中相似度最高的位置即為物點搜尋的結果。然而，在像空間進行影像匹配容易受到影像尺度和拍攝視角不同的影響，造成匹配成果不完全理想。因此，本研究採用物空間匹配(Matching in the Object Space)的概念，藉由影像再取樣產生的假設面調整影像尺度與視角變化，提升匹配可靠度及物點定位精度。
為驗證本研究提出之應用PPIMS於物點定位理論與其所產生之環景影像的匹配策略，使用一室內實驗場來進行測試。實驗場地位於成功大學測量系館，共拍攝五張球形環景影像，影像外方位透過已知控制點進行光束法平差取得。比較空間前方交會與多測站SPI之光束法平差求得的檢核點坐標，在三軸的均方根誤差(RMSD)為±0.006m、±0.003m和±0.004m，顯示兩者成果的一致性。在影像匹配部分，使用原始影像與物空間影像搭配兩種相似度指標，對五個目標點位進行匹配與定位測試。其中，使用原始影像搭配SNCC指標得到之定位成果在三軸的均方根誤差為±0.014m、±0.033m與±0.005m，使用物空間影像後，均方根誤差下降至±0.009m、±0.002m與±0.004m；而使用原始影像搭配使用YARD 指標之定位成果差值為±0.008m, ±0.027m, ±0.005m，使用物空間影像後，均方根誤差下降至±0.010m、±0.008m與±0.005m，顯示出使用物空間影像可達到較良好的匹配與定位成果。
本研究證實應用PPIMS SPIs於物點定位之可行性，在測站外方位精度理想的情形下，物點定位成果可達公分級精度。而所發展出之匹配策略也在實驗成果中獲得驗證，透過物空間匹配可改善影像尺度和拍攝視角不同所造成的問題，提升影像匹配可靠度，求取球形環景影像間共軛點位置。

People are paying more attention to the use of Spherical Panorama Images (SPIs) for its main advantage of wide field of view. Providing accurate location and orientation can enhance more metric application using SPIs. While the exterior orientation parameters (EOPs) of image stations are known, the coordinates of interested points can be determined by space intersection of multiple SPIs. In this study, a special platform called portable panoramic image mapping system (PPIMS) is used to obtain SPIs, and applied for photogrammetric mapping. This system equips with eight single lens cameras and one GNSS receiver, capturing surrounding information simultaneously. After system platform calibration, the images captured with PPIMS at the same image station are combined to be a complete SPI, and then used for mapping application instead of using original images. The EOPs of image stations can be calculated by the network adjustment with multiple SPIs.
No matter in solving image station EOPs or space intersection process, conjugate points selection among overlapped images is necessary. Image matching is considered as an approach to obtain conjugate points much more efficient than manual measurement. In this study, an area-based image matching strategy for automatic conjugate point detection and point coordinate determination with multiple SPIs is proposed. The Sum of Normalized Cross-Correlation (SNCC) and Yet Another Reconstruction Dataprogram (YARD) index are used to check the similarity between images. Within the searching range, similarity profile is generated, and where the maximum similarity locates is regard as the object point position. To decrease the influence caused by scale variations and different FOV between images, the concept of matching in the object space is applied, which uses virtual surfaces for matching by adjusting the scale and perspective of original images, to enhance the matching accuracy.
A test field with five SPIs was set for validation. The root mean square difference (RMSD) of five target points in three directions are (±0.006m, ±0.003m, ±0.004m) in space intersection result, which validates the availability of measurement application using PPIMS. In image matching experiment, four cases with different matching indices and match image type were test. The average maximum similarity of four cases are 0.380, 0.574, 0.573, and 0.696. The RMSDs of selected target points decrease from (±0.014m, ±0.033m, ±0.005m) to (±0.009m, ±0.002m, ±0.004m) with SNCC index, and from (±0.008m, ±0.027m, ±0.005m) to (±0.010m, ±0.008m, ±0.005m) with YARD index. The results reveal that better performance may be achieved using object space matching.
This research shows the feasibility of spatial positioning of interested points with PPIMS SPIs in cm level accuracy, the proposed image matching strategy with PPIMS SPIs is applied and validated. The problem of scale variations and different FOV which causes problem in matching with original images can be improved by object space matching.

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