| DC 欄位 |
值 |
語言 |
| DC.contributor | 土木工程學系 | zh_TW |
| DC.creator | 黃祐祥 | zh_TW |
| DC.creator | Yu-Hsiang Huang | en_US |
| dc.date.accessioned | 2010-7-18T07:39:07Z | |
| dc.date.available | 2010-7-18T07:39:07Z | |
| dc.date.issued | 2010 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=983202078 | |
| 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 | 三維地理資訊系統為了描述現實世界，常使用物空間環境特徵。其構成元素包含房屋、道路、植被等，其中房屋為最顯著者。重建三維房屋模型，最常見的資料來源之一為航照影像。使用航照影像重建三維房屋模型，處理過程分為三大部分：房屋偵測、影像匹配及模型重建。
本研究結合多航帶高重疊數位航照影像，進行房屋模型重建，處理重點為多重影像匹配及模型重建。處理屋頂形狀包含山型屋、女兒牆房屋及具有屋頂副結構之平頂屋。研究中在主影像中概略圈選房屋工作區，利用輪廓特徵偵測房屋資訊，以便後續影像匹配。利用多重影像匹配技術來搜尋共軛特徵，經由二維特徵的線追蹤並加入三維資訊進行判斷，得到三維房屋邊緣線段，配合著利用房屋特徵點所偵測出屋頂內部資訊進行房屋模型重建。
實驗成果顯示，多重影像匹配有效提升匹配精度，利用屋頂面資訊也可改善模型高程精度，房屋模型與參考資料相比，三軸方向之均方根誤差(RMSE)可達40公分內。
| zh_TW |
| dc.description.abstract | Three dimensional geographic information systems describe the reality with environmental elements such as buildings, roads, vegetation etc. Among those elements, building is the most prominent. Aerial images are commonly used in building reconstruction. In building reconstruction with aerial images, building detection, image matching, and building modeling are the three major works.
This study uses multiple strips acquired from highly overlapped aerial images to perform 3D building modeling. Multiple image matching and building modeling are the major works in this study. Test cases include different types of building roofs. We first select the working area in the master image to extract building features. Then, the detection of building information with line features can aid the image matching. Multiple image matching employs multi-windows with multi-epipolar constraint to find conjugate positions in the same procedure. In the generation of line segments, line tracing is performed in the image space with 3D information. Building modeling is made by the integration building boundaries and roof surface information.
The results show that multiple images matching can improve the matching accuracy. Model refinement with 3D roof point clouds can work well. Compared with reference data, the RMSE of building model in most of case can be smaller than 0.4m.
| en_US |
| DC.subject | 房屋重建 | zh_TW |
| DC.subject | 多視窗匹配 | zh_TW |
| DC.subject | 整合式匹配 | zh_TW |
| DC.subject | 多重影像匹配 | zh_TW |
| DC.subject | Multi-windows matching | en_US |
| DC.subject | Multi-eipolar matching | en_US |
| DC.subject | Building reconstruction | en_US |
| DC.subject | Multiple images matching | en_US |
| DC.title | 多重影像匹配於房屋模型重建 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Multiple images matching for 3D building modeling | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |