結合空載光達點雲與航照影像進行房屋模型之變遷偵測

三維房屋模型可提供真實物空間三維資訊與決策支援。針對有變遷的房屋區域進行重新建置，將可減少資料更新所需成本且提升效率。傳統上常利用多時期影像之光譜差異進行變遷偵測，此方法僅有二維光譜資訊而缺乏三維形狀資訊。隨著空載光達系統成熟，使三維形狀資訊取得容易。本研究使用後期空載光達點雲及航照影像進行前期房屋模型之變遷偵測。主要工作項目包含資料前處理及判定房屋模型變遷型態。資料前處理工作包括資料套合、偵測地面與植生區域、剔除地面與植生區域之光達點雲、且計算前後期高程差。判定房屋模型變遷型態工作主要是結合光譜及形狀資訊進行判定；本研究設定五種變遷型態，分別爲未改變、主結構改變、副結構改變、拆除、及植生遮蔽。本研究成果於判定房屋模型變遷型態可達85%整體精度。爲詳細了解影響研究成果的因素，文中針對錯誤例進行分析。造成錯誤的原因中，研究方法的限制佔38%、資料本身之限制佔11%、資料品質造成的影響佔51%。

關鍵字

變遷偵測；房屋模型；空載光達點雲；航照影像

並列摘要

Three dimensional building models provide spatial information for decision support. It is more preferable to maintain a building database by the firstly detecting the changes followed by a reconstruction procedure. Change detection is traditionally done using multi-temporal images through the spectral analyses. Those images provide two-dimensional spectral information without including shape in the third dimension. As the availability and quality of emerging airborne LIDAR systems that make the acquisition of shape information convenient, we use new airborne LIDAR point clouds and aerial photos to detect changes for building models. The proposed scheme comprises data pre-processing and change detection on building areas. In the first step spatial registration is performed, ground area and vegetation area are detected, the Lidar points on the ground surface and in vegetation areas are removed, and height differences are calculated. In the second step, shape and spectral information are integrated to determine types of change. The validation for determination of change types shows that the results can reach 85% overall accuracy. To provide comprehensive observations, those unreliable results are scrutinized. The ratios of the ”limitation of method”, ”limitation of data”, and ”data quality effect” categories are 38%, 11%, and 51%, respectively.

並列關鍵字

Change Detection ； Building Model ； Airborne Lidar ； Aerial Imagery

國際替代計量

結合空載光達點雲與航照影像進行房屋模型之變遷偵測

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