本研究的動機在整合攝影測量與網際網路技術，主旨為研究經由網路瀏覽立體影像之分析。為製作地物之立體影像，研究中使用非量度性數位相機，於地面垂直拍攝二棟建物，以補色原理製成數值像對，接著建置研究區之導覽網站，並將立體影像連結上網，提供立體觀察。本研究亦以Java程式設計立體浮測標，提供觀察者瞭解地物之遠近或高低。 　　實驗呈現若以柯達DC280數位相機進行近景攝影測量，前方交會地物檢核點之空間位置可達像空間上次像元之精度，像比例尺為1/1680時，相對精度可達物距之1/8400。研究結果顯示：結合攝影測量與網際網路可充分展現影像之三維空間。 / The ideal of this study is to put together the knowledge of Photogrammetry and web technology and then provide the 3D pictures for web browsers. 　　A non-metric digital camera is used in this research to shoot two building on the ground vertically. The color-contracting theory is applied to construct the website and create a stereoscopic viewing on the targets within the experimental range. In addition to color-contracting theory, floating mark designed with Java program is also used to help the web users better understand the distance and altitude in proportional to the surrounding objects. 　　According to the experiment in this research, pictures taken with Kodak digital camera model #DC280 together with help of close-range photogrammetry. turn out to beas accutate as those taken with sub-pixel standard (1:1680 proportion versus 1:8400 accuracy). Photogrammetry joins with Internet will successfully disclose the 3D concept and will promote the idea of Photogrammetry.

點雲（point cloud）為以大量三維坐標描述地表實際情形的資料形式，其中包含其三維坐標及相關屬性。通常點雲資料取得方式為光達測量，其以單一波段雷射光束掃描獲取資料，以光達獲取點雲，常面臨掃描時間差、缺乏多波段資訊、可靠邊緣線及角點資訊、大量離散點雲又缺乏語意資訊（semantic information）難以直接判讀及缺乏多餘觀測量等問題。 攝影測量藉由感測反射自太陽光或地物本身放射之能量，可記錄為二維多光譜影像，透過地物在不同光譜範圍表現之特性，可輔助分類，改善分類成果。若匹配多張高重疊率的多波段影像，可以獲取包含多波段資訊且位於明顯特徵點上的點雲，提供光達以外的點雲資料來源。 傳統空中三角測量平差解算地物點坐標及產製數值表面模型（Digital Surface Model, DSM）時，多採用可見光影像為主；而目前常見之高空間解析度數值航照影像，除了記錄可見光波段之外，亦可蒐集近紅外光波段影像。但較少採用近紅外光波段影像，以求解地物點坐標及建立DSM。 因此本研究利用多波段影像所蘊含的豐富光譜資訊，以取像方式簡易及低限制條件的近景攝影測量方式，匹配多張可見光、近紅外光及紅外彩色影像，分別建立可見光、近紅外光及紅外彩色之DSM，其目的在於探討加入近紅外光波段後，所產生的近紅外光及紅外彩色DSM，和可見光DSM之異同；並比較該DSM是否更能突顯植被區。 研究顯示，以可見光點雲為檢核資料，計算近紅外光與紅外彩色點雲的均方根誤差為其距離門檻值之相對檢核方法，可獲得約21%的點雲增加率；然而使用近紅外光或紅外彩色影像，即使能增加點雲資料量，但對於增加可見光影像未能匹配的資料方面，其效果仍屬有限。 / Point cloud represents the surface as mass 3D coordinates and attributes. Generally, these data are usually collected by LIDAR (LIght Detection And Ranging), which acquires data through single band laser scanning. But the data collected by LIDAR could face problems, such as scanning process is not instantaneous, lack of multispectral information, breaklines, corners, semantic information and redundancies. However, photogrammetry record the electromagnetic energy reflected or emitted from the surface as 2D multispectral images, via ground features with different characteristics differ in spectrum, it can be classified more efficiently and precisely. By matching multiple high overlapping multispectral images, point cloud including multispectral information and locating on obvious feature points can be acquired. This provides another point cloud source aparting from LIDAR. In most studies, visible light (VIS) images are used primarily, while calculating ground point coordinates and generating digital surface models （DSM） through aerotriangulation. Although nowadays, high spatial resolution digital aerial images can acquire not only VIS channel, but also near infrared (NIR) channel as well. But there is lack of research doing the former procedures by using NIR images. Therefore, this research focuses on the rich spectral information in multispectral images, by using easy image collection and low restriction close range photogrammetry method. It matches several VIS, NIR and color infrared (CIR) images, and generate DSMs respectively. The purpose is to analyze the difference between VIS, NIR and CIR data sets, and whether it can emphasize the vegetation area, after adding NIR channel in DSM generation. The result shows that by using relative check points between NIR, CIR data with VIS one. First, VIS point cloud was set as check point data, then, the RMSE (Root Mean Square Error) of NIR and CIR point cloud was calculated as distance threshold. Its data increment is 21% ca. However, the point cloud data amount can be increased, by matching NIR and CIR images. But the effect of increasing data, which was not being matched from VIS images are limited.

近來三維數值城市及數碼城市（Cyber City）為各界極欲發展及研究的課題，為了要增加三維數值城市的擬真性及美觀程度，通常是將建物模型敷貼真實拍攝之牆面影像，增加三維模型的細緻化程度。而欲精確的敷貼牆面紋理影像，必須嚴密地將所拍攝之近景影像定位定向，一般採用光束法區域平差解算，此時需加上適當的控制點控制資訊才能完成，因此控制點控制資訊若來自地面測量將相當耗費成本。多年來，各地方政府製作大比例尺地形圖時已拍攝相當多的航照影像，可用來做為上述的控制資訊，亦即航空影像控制實體，若能使用這些航空影像控制實體作為控制資訊，不但可有效利用資源，亦能減少控制點取得所需花費的成本。因此，本研究將使用航空影像控制實體所提供的控制資訊做為控制來源。 本研究探討以航空影像控制實體作為控制資訊時，使用非量測型相機以類似傳統航測拍攝方式及旋轉多基線交向拍攝方式拍攝涵蓋建物牆面的目標區影像後，於最少控制且不同控制分布時，對光束法區域平差精度之影響。因使用非量測型相機，故本研究先以iWitnessPRO近景攝影測量軟體率定相機參數，接著以PHIDIAS近景攝影測量軟體解算光束法區域平差。過程中探討使用航空影像控制實體作為控制資訊時，於最少控制且不同控制分布時，加入附加參數解算的自率光束法區域平差與與一般光束法區域平差之精度。根據實驗結果，低樓層取像的光束法區域平差之檢核點RMSE精度，其結果大多可應用於LOD 3精度等級的牆面敷貼。另，因都市地區高樓林立，狹小巷弄多，有鑒於此，本研究使用旋轉多基線交向攝影，結果顯示其將有機會運用於近景攝影測量LOD 3精度等級的牆面紋理敷貼。 / Recently, the studies about the cyber city have become a popular topic. For improving the level of detail of cyber city, photo-realistic textures from images are mapped onto the surfaces of 3D building models. Before the accurate texture mapping, bundle block adjustment can be performed to recover the parameters of exterior orientation for each close-range images more accurate and more precise, where the control information is necessary. For the past years, many aerial photogrammetry projects were done by local governments for the mapping of 1/1000 topographic maps. Those historic aerial images can be used as control information to reduce the cost and increase the efficiency. Therefore, this study investigates the accuracy of bundle block adjustment about non-metric close-range images, taken from the ways similar to the traditional aerial photogrammetry and the rotating multi-baseline photogrammetry, by using control entities from historic aerial images as the minimal controls under various control distributions. Since the non-metric camera is used for collecting the close-range images, the iWitnessPRO software is utilized for camera calibration. After that, the PHIDIAS software, a close-range photogrammetry software, is employed to performed the bundle block adjustment. During performing the bundle block adjustment, the camera parameters are regarded as unknowns and determined, called as self-calibration bundle adjustment. The results of self-calibration bundle adjustment will be compared with conventional bundle adjustment. The test results show that the accuracy of most self-calibration bundle adjustment about close-range images covered with low buildings can be used for the application of LOD 3 texture mapping. Moreover, the test results of using close-range images from rotating multi-baseline photogrammetry in urban areas show the potential possibility for LOD 3 texture mapping in urban areas with high buildings and narrow alleys.