應用空載傾斜攝影密匹配點雲於建物變遷分析

隨著都市化的發展，都市的環境變化急遽，為了掌控土地資源與土地利用之狀況，如何有效與快速的進行都市環境變遷與監控便顯得更重要。當城市空間資訊往第三維度快速增長時，傳統垂直航拍影像所提供的資訊已不敷使用。然而空載傾斜影像能拍攝較完整的地物立面資訊，且傾斜視角具立體感，人員不需專業訓練即可判釋各式地物，國際上已逐漸應用於都市地區的地物偵測，例如地震後房屋傾倒之全面清查。本文以無人飛行載具及有人機蒐集兩個時期的傾斜與垂直空載影像，利用自動化特徵匹配產生連結點，搭配地面控制點與空三平差求解影像絕對方位，並透過密集影像匹配技術產製兩時期之三維彩色點雲，尤其傾斜視角的建物牆面資訊能提供更細緻且密集的牆面點雲以利建物判釋。空載影像以物件導向式影像分類法進行地物分類，利用反投影公式搭配HPR(Hidden point removal)運算子提供三維點雲地物類別之資訊，並建立相同的區域體元(voxel)座標系統，在三維空間中針對建物進行變遷偵測，並計算獨立建物變遷區塊之體積。

關鍵字

空載傾斜影像；分類點雲；變遷偵測

並列摘要

Due to the rapidly urbanization development, to monitor the change of city environment is more and more important for urban land resource management. Different to traditional vertical aerial imagery (VAI), the oblique aerial imagery (OAI) is more stereoscopic for manually recognition, and has more information practically on building façade. Various studies has investigated the potential of its applications, such as building seismic damage assessment, building objects extraction and classification and so on. In this study, both vertical aerial imagery (VAI) and oblique aerial imagery (OAI) are collected from airborne and UAV platform in two different periods for building change detection purpose. Through photogrammetry techniques including orientation reconstruction and dense image matching, the colored high density 3D point clouds of two period data are generated from both vertical and oblique images. We apply object-based image analysis (OBIA) to classify the images into several classes of the cyber-city. Then, two period of point clouds with information of classes which is extracted from classified images are generated by back-projection and Hidden Point Removal opereator. To detect the change of building, two point clouds are normalized to local voxel coordinate system. Voxel-based change detection is performed to detect the building change in 3D space and calculate the volume.

並列關鍵字

Oblique image ； Classified Point cloud ； Change detection

國際替代計量

應用空載傾斜攝影密匹配點雲於建物變遷分析

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