隨著三維地理資訊系統的發展與電子地圖的演進，數碼城市的建置已變成現今重要的課題。我們能透過網路平台瀏覽各大城市的三維樣貌，業者或研究人員亦能透過數碼城市進行三維空間分析，且比起傳統二維平面地圖，三維地圖能提供更多空間資訊，觀看起來也更為直覺。近幾年來，隨者影像匹配技術與攝影系統的發展，我們可以透過多視角的航照影像來輔助數碼城市的建立，相較於傳統垂直航照，傾斜視角的航照影像能同時提供地物的側面資訊，故常被利用於牆面紋理貼圖及建物模型的重建等用途。搭配密集匹配技術，我們亦可經由影像獲得地表點雲，若配合傾斜航照影像亦可得到建物牆面的點雲，相較於空載光達系統，此種資料來源較為經濟且密集。本研究因此提出利用多視角航照影像進行密集匹配所產生的大量點雲作為地物分類時的輔助高程資訊，並針對多視角航照原始影像以物件導向影像分析技術進行都市區地物分類，以協助後續數碼城市語意資訊之萃取。
近十年來，物件導向影像分類技術逐漸取代了傳統上以像元為基礎的分類方法，其主要特點是比起根據像元本身的特性或鄰近像元間的關係，以具有同質性的像元組成的物件，可提供更多便於辨識的物件特徵，例如形狀、紋理與拓樸關係等，故可更準確且合理的偵測出目標類別，且分類成果也能更趨近人眼判識的結果。在本研究中，我們將物件導向影像分析技術應用於都市區傾斜航照原始影像之分類。一開始先透過影像分割演算法將影像分成若干區塊，又稱為物件。再根據各物件的光譜、幾何、紋理及物件間之拓樸關係等特性，訂定階層式的分類規則，由粗至細逐步將影像分為草地、樹木、牆面、屋頂、道路、窗戶及其他等七類。研究中亦加入了高度圖、梯度圖與邊緣圖等輔助資料，使具有高度變化的地物能被順利偵測。其中，高度圖與梯度圖來自多視角航照影像密集匹配所獲得的大量地表點雲，故我們亦將其與空載光達點雲比較，以驗證匹配點雲的可靠度與誤差等級。研究成果顯示整體分類精度可達81%，而Kappa指標為0.75，證明本研究所提出之分類方式具有相當高的正確性，尤其是傾斜航照部分地物高差移位現象相當嚴重，若未使用高度圖與梯度圖，將無法區分牆面、屋頂與道路。此外，本研究提出之分類方法亦可應用於垂直航照與窗戶之偵測上，此富含語意資訊的分類成果對於數碼城市的建立與三維地理資訊系統的應用皆有很大的幫助。

With the evolution of three dimensional GIS (Geospatial Information Systems) and digital map, nowadays, cyber-city modeling becomes a major topic in related research. We can take in every city at a glance with the 3D views on the network platform and implement some spatial assessment through the cyber-city. Comparing to the 2D maps, 3D maps provide much more information and intuitive visual response. In recent years, with the development of image matching technique and camera system, cyber-city models can be reconstructed through the multi-view aerial image. In addition to use the vertical aerial image only, the oblique views provide both the top and side information of the surface objects. For this reason, the multi-view aerial images are often utilized in building modeling researches such as façade texturing and building model reconstruction. Moreover, we can also acquire amount surface points from those images with the dense image matching technique. Comparing to the aerial LiDAR points, this kind of point cloud is much cheaper and denser. In our study, we performed an object-based image classification rule set on multi-view aerial imagery in urban area to extract the semantic information of the cyber-city. The images are finally classified into Grass, Tree, Façade, Roof, Road, Window and Others classes with a hierarchical coarse to fine rule set. For classifying the surface object more correctly, we also utilized the photogrammetric point cloud which generated by the multi-view imagery to produce the auxiliary height information.
Over the last decade, the object-based image analysis (OBIA) has substituted the pixel-based classification method gradually. With the multiresolution image segmentation algorithm, objects are produced by merging the pixels with shape and color homogeneity. The object contains more features such as texture or shape indices in identifying the target class that lead the classification result closer to the human interpretation result, whereas the pixel only with spectral information. In the classification, the image will separate into several parts at first, namely the “objects”, through image segmentation algorithm. Then, we defined a coarse to fine rule set to classify the objects hierarchically according to the spectrum, geometry and class-related features indices. The auxiliary feature layers which include the original image, “height map” and “gradient map” are applied for detecting the target class. Besides, we also add the beneficial “edge map” in segmentation layers. Considered that the “height map” and “gradient map” are come from the photogrammetric point cloud, we also verified their reliability and correctness when comparing to the ALS (Airborne Laser Scanning) point cloud. Our experiment result shows the overall accuracy can achieve 81% and the kappa index is 0.75 which proved that the proposed classification method has a high percent correctness, especially in separating the roof, road and façade from the severe relief displacement successfully. Moreover, the semantic classification result is significant for cyber-city modeling and 3D GIS applications.

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