近年來對於三維空間資訊的需求與日俱增，無論在集水區的開發、森林大火的截斷及復育等，三維空間資訊皆扮演了不可或缺的角色。因此，取得原始的數值高程模型(DEMs)是日後應用上的第一步。
光達為近年來發展良好的遙測技術，在產生高解析高精度三維地形模型方面有相當大的潛力，光達所獲得的資料包含從地形及地物反射的所有起伏，而建構數值高程模型則必須將地物點濾除。諸多學者分別以曲面、區塊、斜率等觀點提出相關演算法，這些方法針對濾除地形起伏上多數地物發展而來，然而在特定的區域仍有產生不易處理的情形。
不同演算法從不同角度觀點切入，實際上發揮的程度亦各有所長，如能恰如其分的採納各演算法的優點，將可使得在對地形的描述上更加完整。知識庫系統即藉由專業領域知識與經驗上的累積，來解決特定領域中相對應的問題。黃明哲(2007)即提出以知識庫為基礎的方法，採用高解析度影像結合光達資料，進行都會區的地形分類，分類精度約93%，良好的成果也為後續的研究開啟了先機。本研究延續其精神，提出以知識庫為基礎的光達資料過濾演算法KBLF (Knowledge-Based LiDAR Filtering)，適當的將各家演算法納為知識庫中的規則，期望整合各家優勢，將對於地物的描述能力提高，亦同時提高過濾的能力，合理的分出地面點與地物點，再以地面點為參考點，經由距離平方反比加權法（IDW, Inverse Distance Weighting）與最鄰近指定法（NN, Nearest Neighbor）將地物點高度復原，產生數值高程模型DEM。

There is an increasing need for three-dimensional description for various applications such as the development of catchment areas, forest fire control and restoration. Three-dimensional information plays an indispensable role; therefore acquisition of the digital elevation models (DEMs) is the first step in these applications.
LiDAR is a recent development in remote sensing with great potential for providing high resolution and accurate three-dimensional point clouds for describing terrain surface. The acquired LiDAR data represents the surface where the laser pulse is reflected from the height of the terrain and object above ground. These objects should be removed to derive the DEMs. Many LiDAR data-filtering studies are based on surface, block, and slope algorithms. These methods have been developed to filter out most features above the terrain; however, in certain situations they have proved unsatisfactory.
The different algorithm based on different point of view to describe the terrain surface. The appropriate adoption of the advantages from these algorithms will develop a more complete way to derive DEMs. Knowledge-based system is developed to solve some specific problems according to the given appropriate domain knowledge. Huang (2007) proposed a Knowledge-based classification system in urban feature classification using LiDAR data and high resolution aerial imagery with 93% classification accuracy. This research proposed a knowledge-based LiDAR filtering (KBLF) as a follow-up study of Huang’s study. KBLF integrates various knowledge rules derived from experts in the area of ground feature extraction using LiDAR data to increase the capability of describing terrain and ground feature classification. The filtering capability of KBLF is enhanced as expected to get better quality of referenced ground points to recover terrain height and DEMs using Inverse Distance Weighting (IDW) and Nearest Neighbor (NN) methods.

摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 XI
第一章 緒論 1
1-1 前言 1
1-2 研究動機與目的 1
1-3 研究構想與流程 2
1-4 論文架構 4
第二章 文獻回顧 5
2-1 過濾演算法 5
2-2 知識庫 9
2-3 地物特徵提取 10
第三章 基於知識庫的光達資料地物濾除與地形復原 12
3-1 資料前處理 12
3-1-1 原始資料網格化 12
3-2 概略趨勢面 13
3-3 正規化高度 14
3-4 知識庫建置 15
3-4-1 低高度層判別規則 17
3-4-1-1 回波強度 18
3-4-1-2 平坦度 19
3-4-1-3 穿透度 20
3-4-2 中高度層判別規則 21
3-4-2-1 回波強度 23
3-4-2-2 穿透度 24
3-4-3 高高度層判別規則 24
3-4-3-1 最小房屋面積 26
3-4-3-2 非等向性濾波 27
3-4-3-3 常態化差異 30
3-4-3-4 不平坦區域 31
3-5 分類結果過濾 32
3-6 地形復原 33
3-6-1 最鄰近指定法 35
3-6-2 距離平方反比加權法 36
3-7 模型重建 37
第四章 實驗成果與分析 38
4-1 實驗區資訊 38
4-2 參數設定 45
4-3 過濾結果 47
4-3-1 都會實驗區(一) 47
4-3-2 都會實驗區(二) 50
4-3-3 山林實驗區(一) 53
4-3-4 山林實驗區(二) 56
4-3-5 混和實驗區 59
4-4 地形復原與模型重建 62
4-4-1 都會實驗區(一) 62
4-4-2 都會實驗區(二) 63
4-4-3 山林實驗區(一) 64
4-4-4 山林實驗區(二) 65
4-4-5 混和實驗區 66
4-5 檢視成果 67
4-5-1 都會區 67
4-5-2 山林區 68
第五章 結論與建議 69
5-1 結論 69
5-2 建議 72
參考文獻 73

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