使用光達資料與航照影像以漸進式屋頂面搜尋法重建房屋模型;Progressive Searching of Roof Planes for Building Reconstruction Using Lidar Data and Aerial Imagery

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/53653

Title:	使用光達資料與航照影像以漸進式屋頂面搜尋法重建房屋模型;Progressive Searching of Roof Planes for Building Reconstruction Using Lidar Data and Aerial Imagery
Authors:	林昀柔;Lin,Yun-Jou
Contributors:	土木工程研究所
Keywords:	航照影像;光達資料;房屋重建;屋頂面分類;Building Reconstruction;Lidar Data;Aerial Imagery;Roof Patch Classification
Date:	2012-08-01
Issue Date:	2012-09-11 18:04:49 (UTC+8)
Publisher:	國立中央大學
Abstract:	航照影像與光達點雲所含的幾何資訊具有互補性質，空載影像具有良好的地物邊界線但高程資訊較內隱，反之，光達點雲含有準確的高程資訊但地物邊界較不明確。本篇研究提出利用空載光達資料及單張航照影像重建三維房屋模型。由於多數房屋具規則幾何外型，本研究以重建由平面所組成的房屋為主。本研究工作包含五個部分: (1)屋頂面組成，(2)屋頂面分類，(3)結構線定位，(4)二維線段組成以及(5)三維房屋模型重建。將屋頂上的光達點雲萃取出並利用共面分析組成屋頂面，再使屋頂面分類成，平頂屋頂、多斜面屋頂以及單斜面屋頂。然而，光達點雲具有誤差，對於由緩斜面組成的多斜面屋頂，緩斜面難以偵測。因此，本篇利用漸進式的搜取法分析光達點雲求得最佳的斜面。之後，利用屋頂面的資訊獲得初始邊界線以及屋頂結構線的區間。將物空間所求得的初始邊界線反投影至像空間建立工作區，於工作區中進行直線偵測並組成屋頂結構線段以及候選邊界線段。精確的邊界線段萃取後再將組成的線段投影回物空間以重建三維房屋模型。以實際量測之房屋模型與研究結果所產生的模型比較以驗證成果的精確度。測試資料包括: (1)航照DMC 影像，空間解析度為16 公分以及(2) 空載光達掃描系統：Leica ALS 50 之光達點雲資料，點雲密度為10 points/m²。所得成果品質之誤差於X方向為±0.242公尺、Y方向為±0.246公尺以及Z方向為±0.260公尺。Aerial imagery and lidar point clouds are complementary in terms of geometric informationcontents. Aerial imagery has good definition of object edges but the information of objectelevation is implicit. Lidar point clouds, on the other hand, explicitly record the 3Dinformation of scanned object points but object edges are less clear than that in an aerialimagery. This study proposes a method that integrates single imagery and lidar point clouds toreconstruct 3D building models. Most buildings have multi-facet shapes, so that this papermainly focuses on the reconstruction of polyhedral buildings.The proposed scheme is composed of five major parts, (1) Segmentation of Roof Patches, (2)Roof Patch Classification, (3) Determination of Structure Lines, (4) 2D Line Segmentation,and (5) 3D Building Model Reconstruction. The roof patches were segmented via thecoplanirity analysis with the lidar points on the roofs. Then, the roof patches were classifiedinto flat, multi-pitched and mono-pitched roof patches. Considering the errors of Lidar data,the localization of low-pitched roofs from multiple slope ones could be difficult. Thus, weanalyzed the point clouds to find the optimal roof patches progressively. Once the patcheswere found, we determined the initial building boundaries and the zones of roof structure linesin the third step. The initial boundaries and the zones of roof structure lines were thenprojected to the image space for the determination of a work area. Next, we detected the edgesin the working areas to find the edges and vectorized the edges to form the roof structure lineiiisegments and candidate boundaries segments. The refined boundaries were extracted and then,the line segments were projected to object space to reconstruct 3D building models.The accuracy of the results was validated by examining the discrepancy between the manuallymeasured building models and generated ones. The test data included (1) DMC aerial imagerywith a spatial resolution of 16 cm, and (2) Lidar point clouds from Leica ALS 50. Experimentresults indicate that the accuracies are ±0.242m in X-dir, ±0.246m in Y-dir, and ±0.260m inZ-dir.
Appears in Collections:	[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

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