| 摘要: | 房屋模型提供了三維的空間資訊，其應用之層面非常廣泛，如都市規劃、房地產管理等位置基礎的應用。為了應用的時效，房屋模型需要進行更新，而更新變遷區域之房屋模型較重建全區域之房屋模型更有效率。這種方式需要先進行變遷之偵測，再進行重建之步驟。本研究之目標在於融合光達點雲資料以及航照影像進行三維房屋模型的變遷偵測。 本研究包含了三個步驟：(1)資料套合，(2)房屋模型變遷偵測以及(3)偵測新建房屋區域。資料套合，為進行多元資料之間套合處理的步驟。房屋模型變遷偵測，藉由檢驗航照影像的光譜資訊、光達點雲與房屋模型的高程差資訊以及航照影像之線型特徵資訊，使用規則式的判斷方法逐一對每個房屋模型進行變遷偵測。並且為了克服規則式判斷中，使用單一門檻會遇到的門檻高敏感度問題，本研究導入了雙重門檻的策略。偵測新建房屋區域，藉由去除植生、地面以及舊時期房屋區域來偵測出屬於新建房屋區域的光達點雲，使用區域成長的方法將點雲區分成不同群組，最後使由邊界追蹤的方法得到新建房屋的區域。 成果驗證中，本研究於房屋模型變遷偵測的部份，導入雙重門檻策略可使整體精度從93.1% 提升至95.9%。於偵測新建房屋區域的部份，正確偵測率為100%。並且，為了瞭解影響房屋模型變遷偵測的因素，本研究亦針對各種不同的案例進行分析。 Building models are built to provide three dimensional (3D) spatial information, which is needed for varieties of applications, such as city planning, construction of location-based services, and the like. However, three dimensional building models need to be updated from time to time. Rather than reconstructing building models for the entire area, it would be more effective to only revise the parts that have changed. This approach might be achieved by detecting changes first, followed by a reconstruction procedure. In this study, we aim at finding changes with 3D building models through a combination of LIDAR data and aerial imagery. The proposed scheme comprises three steps, namely, (1) data registration, (2) change detection of three dimensional building models, and (3) detection of new building models. The first step performs data registration for multi-source data. The second step performs the rule-based change detection, it include examination of spectrum from aerial images, examination of height difference between building models and LIDAR points, and examination of linear features from aerial images. A double-threshold strategy is applied to cope with the highly sensitive thresholding often encountered when using the rule-based approach. In the third step, we detect the LIDAR point clouds in the new building areas by removing vegetation, ground and old building areas. We then use region growing to separate the LIDAR point clouds into different groups. Finally, we use boundary tracing to get the new building areas. Ground truth data are used for validation. The experimental results indicate that the double-threshold strategy improves the overall accuracy from 93.1% to 95.9%. The results for new building area detection reach 100% overall accuracy. To provid comprehensive observations, the different cases are scrutinized. |
