| DC 欄位 |
值 |
語言 |
| DC.contributor | 土木工程學系 | zh_TW |
| DC.creator | 楊金融 | zh_TW |
| DC.creator | Chin-Jung Yang | en_US |
| dc.date.accessioned | 2012-7-13T07:39:07Z | |
| dc.date.available | 2012-7-13T07:39:07Z | |
| dc.date.issued | 2012 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=993202087 | |
| dc.contributor.department | 土木工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 合成孔徑雷達與光學影像是環境遙測之主要資料，整合此兩種感測器資料可獲取更多有用的資訊。本研究從幾何的觀點出發，結合此兩種異質感測器資料，以獲取共軛影像點之三維資訊。使用衛星影像進行三維定位量測的先決條件是建立幾何模式，以連結影像與地面。有理函數模式具有標準化幾何模型的優點，便於描述影像與地面之數學關係，因此本研究使用有理函數模式整合光學與雷達資料進行三維定位。
本研究所提的方法有四個主要處理步驟：(1)建立感測器幾何模型、(2)有理函數轉換係數求解、(3)精化有理函數模式與(4)三維坐標定位。由於大部分的雷達衛星公司及一部分的光學衛星僅提供衛星星曆資料並無提供有理函數轉換模式，此時必須從感測器的幾何模式進而求解有理函數轉換係數；接著以地面控制點精化有理函數模式，使物像空間轉換更加嚴密。最後在光學與雷達影像上量測共軛點，並以有理函數模式建立觀測方程式求解三維坐標。本研究主要有三個實驗分析，(1)雷達衛星影像之模式誤差分析、(2)三維定位精度分析與(3)模擬幾何交會精度。實驗成果顯示整合光學與雷達影像確實可達到三維定位的能力。
| zh_TW |
| dc.description.abstract | Synthetic Aperture Radar (SAR) and optical images are two major data sources in environment remote sensing. The integration of these two datasets can help us to obtain more object information. From geometric point of view, these two types of data may be combined for 3D positioning. Orientation modeling for satellite images is an important task for 3D positioning. To link an image point with its counterpart on the ground, Rational Function Model (RFM) has advantages of standardization for satellite image processing and is easy to implement. Thus, we use RFM to integrate SAR and optical sensor orientation data for 3D positioning.
There are four steps in this study: (1) establishment of geometric model, (2) generation of Rational Polynomial Coefficients (RPCs), (3) RFM refinement, and (4) 3D object positioning. A part of high-resolution optical satellite companies and most SAR satellite image providers only distribute the imagery with ephemeris data. Thus the establishment of geometric model for optical and SAR sensors is the first step. Then, the generation of RPCs for RFM starts from geometric model. Then we employ the ground control points to adjust the RFM for two sensor images. For a pair of conjugate points in SAR and optical images, we have four equations to determine the 3D object coordinates. The experiments include three parts: (1) model error analysis for SAR satellite images, (2) validation for 3D positioning, and (3) geometric simulation. Experimental results showed that the integration of SAR and optical images can achieve 3D object positioning.
| en_US |
| DC.subject | 光學影像 | zh_TW |
| DC.subject | 雷達影像 | zh_TW |
| DC.subject | 有理函數模式 | zh_TW |
| DC.subject | 幾何套合 | zh_TW |
| DC.subject | 三維坐標定位 | zh_TW |
| DC.subject | Geometry Integration | en_US |
| DC.subject | 3D Positioning | en_US |
| DC.subject | Rational Function Model | en_US |
| DC.subject | Optical Imagery | en_US |
| DC.subject | SAR imagery | en_US |
| DC.title | 整合雷達與光學衛星影像進行三維定位 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Combined Adjustment of Optical and SAR Images for 3D Object Positioning | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |