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研究生: 鄭邦寧
Cheng, Pang-ning
論文名稱: 使用空載光達點雲求定數值地表高程模型之小波法
Wavelets-Based Method for Determining DEM by Using Airborne LiDAR Data
指導教授: 蔡展榮
Tsay, Jaan-rong
學位類別: 碩士
Master
系所名稱: 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 108
中文關鍵詞: 分類/過濾影像小波光達
外文關鍵詞: Image, Wavelet, LiDAR, Classification/Filtering
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  •   近年來,以空載光達點雲製作數值地表高程模型時,面臨到兩個最大的問題:(一)如何從複雜的光達點雲挑選落於地面的光達點,(二)如何在面對龐大的點雲資料時,提升過濾的品質。
      針對上述所述之兩點問題,本研究提出一個過濾局部光達覆蓋面的小波演算法,以規則網格化的資料結構及區塊內較少的點雲資料來進行空載光達點雲之分類、重建數值地表高程模型,演算法不僅具備小波描述真實複雜表面的能力,而且具有對光達點雲過濾的能力,使得演算法能夠在一個逼真的數學面下,透過建物邊緣零階不連續斷線、斷面處具有的特殊數學性質—吉布斯效應,結合GD法、高程差與高程差變化率判斷、重新篩選等過濾機制,逐步地將光達點雲分成地面點與非地面點二類。同時,演算法可納入數位空照彩色影像以獲取更可靠的地面點分類結果。進一步使用分類得到的地面點資料來計算產生數值地表高程模型。
      此外,本研究提出一個方法來處理橋樑區域的點雲過濾,利用橋樑在空間上的幾何特性,(一)橋面相較於橋樑二側,具有高程較高的特性,(二)橋樑表面是一個平坦且高程變化不大的區域,以和橋樑走向正交的掃描方式搜尋橋樑種子點,再配合區域成長法偵測橋樑的位置。
      二個不同類型實驗區經由過濾的流程後,由個別的總錯誤率0.5%及6%顯示本研究提出過濾點雲的處理流程,可以從大量空載光達點雲挑選出可靠的地面點群。

      Generating DEM by using airborne LiDAR data has two main problems: 1.How to select the terrain points from complex LiDAR data set, 2.How to increase the efficiency of LiDAR point classification / filtering.
      According to the above-mentioned problems, this study proposes a wavelets-based method for classifying LiDAR points in a local area to determine DEM. The method can not only well filter out the noise points, but also determine reliably a real and complex fractal surface. Considering the phenomenon of Gibbs effect located within the edge areas of the buildings, the method utilises geodesic dilation, the height difference and height difference increment between terrain points and non-terrain points, and then re-selects LiDAR points by generating a reference surface to select more reliable terrain points. Besides, digital aerial color images can be used for selecting more reliable terrain points in vegetation areas. Finally, those terrain points selected by the method are used to generate the DEM.
      Also, a method is proposed to exclude those non-terrain points in a bridge region. The concept of bridge detection is based on the geometry properties of the bridge surface, such as bridge surface is higher than its neighboring ground surface, and the bridge surface is often a flat area. A point scanning method is presented to sort the seeds located on the bridge surface. These seeds can be adopted for region growing process. Then all non-terrain points within the bridge area can be found.

    摘 要 I ABSTRACT II 誌 謝 III 目 錄 IV 表目錄 VI 圖目錄 VII 一、緒論 1 1.1 研究動機與目的 1 1.2 研究背景及文獻回顧 4 1.2.1 常見的點雲分類法之回顧 4 1.3 研究方法與流程 7 1.4 論文架構 8 二、空載光達點雲資料與空照影像 9 2.1 空載光達 10 2.2 空照彩色影像 12 2.1.1 影像方位元素 13 2.2.2 NDVI 15 2.2.3 綠色像元指標 16 三、資料處理流程設計 17 3.1 資料處理流程 17 3.1.1 空載光達點雲之預處理 19 3.1.2 小波重建面 20 3.1.3 GD法 24 3.1.3.1 設定GD法之下降參數值h 25 3.1.3.2 罩窗大小對GD法之影響 25 3.1.3.3 不同下降參數值h對GD法之影響 27 3.1.4 比較基準面 28 3.1.5 高程差及高程差變化率判斷 28 3.1.6 重新篩選 30 3.2 不同輸入資料之演算流程 31 四、實驗成果與分析 32 4.1 實驗數據之規格說明 32 4.1.1 空載光達資料 32 4.1.2 影像資料 33 4.2 實驗區類型之說明 35 4.3 實驗區一之光達點雲分類成果與分析 40 4.3.1 實驗區一之影像NDVI輔助點雲分類 40 4.3.2 實驗區一之影像GI輔助點雲分類 53 4.3.3 實驗區一未含影像輔助點雲分類的成果與分析 58 4.4 實驗區二之光達點雲分類成果與分析 61 4.4.1 橋樑偵測 61 4.4.1.1 篩選橋樑種子點 62 4.4.1.2 區域成長法偵測橋樑位置 64 4.4.2 加入橋樑偵測之後續點雲過濾流程 67 4.4.3 實驗區二影像NDVI輔助分類 69 4.4.4 實驗區二點雲分類結果綜合比較 76 4-5 實驗區一及實驗區二的實驗成果總結 79 五、結論與建議 85 參考文獻 88 附錄 92 附錄A、實驗區二之點雲分類成果展示圖 92 附錄B、程式碼 94

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