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系統識別號 U0026-2508201313571500
論文名稱(中文) 加權共變異數矩陣之幾何特徵應用於光達點雲分類之研究
論文名稱(英文) 3D LiDAR Data Classification using Eigen-features of Weighted Covariance Matrix
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 101
學期 2
出版年 102
研究生(中文) 蘇柏霖
研究生(英文) Po-Lin Su
學號 P66004081
學位類別 碩士
語文別 英文
論文頁數 44頁
口試委員 指導教授-林昭宏
口試委員-曾義星
口試委員-徐百輝
口試委員-張智安
中文關鍵字 點雲分類  加權共變異數矩陣  幾何特徵 
英文關鍵字 point cloud classification  weighted covariance matrix  eigenfeature 
學科別分類
中文摘要 空載雷射測距技術(光達)能夠快速且精確的對大範圍區域進行掃描,以獲
取高解析度的地貌資訊,光達測繪技術已被廣泛的應用到遙測相關領域以及其
他相關應用。而點雲分類是點雲資料處理流程中一個相當重要的步驟,其分類
成果可用於製作數值地表模型、數值高程模型以及三維都市建模等應用。在點
雲分類的議題中,已有多篇相關之研究被提出,但由於不同物體在特定狀況下
可能具有類似的特性,因此分類精度之提升仍有值得討論的空間。分類成果的
優劣取決於使用之特徵(features)是否能有效地辨別不同物體,因此本篇研究著
重於建立可以充分描述物件幾何性質的特徵,以提升點雲分類的精度。在已知
用於點雲分類的特徵中,幾何特徵(eigenfeatures)可描述光達點雲的分布是趨近
於線狀(1D)、面狀(2D)或者是近似球狀分布(3D)。該種類的特徵一般是由樣本
共變異數矩陣(sample covariance matrix)以及樣本平均值(sample mean)計算求得。
然而,幾何特徵的計算容易受到點雲取樣分布以及資料中存在之大錯的影響,
進而使其計算成果不準確。因此,本研究提出一基於加權共變異數矩陣
(weighted covariance matrix)以及加權平均(weighted mean)之方法,用以計算更
為可靠的幾何特徵。由實驗結果顯示,無論是定量或定性分析,本研究所提出
的方法所計算之幾何特徵較傳統方法更加穩定、可靠,且分類成果也有所改善。
英文摘要 Light Detection and Ranging (LiDAR) sensors with the ability of acquiring high
spatial resolution and accuracy 3D data over a large area is increasingly being used
in the fields of remote sensing and surveying with many applications. The
classification of airborne LiDAR data is a fundamental and critical process in the
related applications such as digital terrain/elevation model (DTM/DEM) generation
and three-dimensional urban modeling. Although researchers have proposed many
classification methods for LiDAR data, the problem has not been fully solved due to
the similar characteristics possessed by different objects such as ground and nonground
objects. One of the keys to a successful classification is the features as well
as the feature space used in the separation of different objects. Therefore, this study
aims to develop advanced features to well describe the geometric characteristics of
objects for improving the classification accuracy. Among existing features,
eigenfeatures calculated from sample covariance matrix with sample mean are
popular in geometric description of LiDAR data. They can describe the local
geometric characteristics of a point cloud and indicate whether the local geometry is
linear, planar or spherical. However, they suffer from certain drawbacks; notably,
they are not robust statistics, meaning that they are sensitive to the sampling and the
outliers of data. To obtain reliable eigenfeatures from LiDAR data with sparse, noisy,
and incomplete sampling, we introduce a novel method to calculate and obtain
eigenfeatures based on weighted covariance matrix and weighted mean. Each point
in a neighborhood of tensor structure is assigned a weigh to balance the spatial
contributions of points. In the experiments, qualitative and quantitative analyses on
airborne LiDAR data show a clear superiority of the proposed method over the
III
classification using standard eigenfeatures.
論文目次 摘要 ..............................................................................................................................I
Abstract ...................................................................................................................... II
致謝 ........................................................................................................................... IV
Content ....................................................................................................................... V
List of Tables .......................................................................................................... VII
List of Figures ......................................................................................................... VII
1. Introduction .......................................................................................................... 1
2. Related Work ....................................................................................................... 4
3. Methodology ........................................................................................................ 6
3.1 Support vector machine ............................................................................... 6
3.2 The searching method .................................................................................. 8
3.3 Features in feature vector ............................................................................. 9
3.3.1 Height feature ........................................................................................... 9
3.3.2 Intensity feature ...................................................................................... 10
3.3.3 Echo-based features ................................................................................ 10
VI
3.3.4 Eigen-based features ............................................................................... 10
3.4 Principal component analysis ..................................................................... 12
3.4.1 Standard PCA ......................................................................................... 12
3.4.2 PCA With Weighting Strategy ............................................................... 14
3.4.3 Algorithm of PCA with Weighting Strategy .......................................... 16
3.5 Multi-scale strategy .................................................................................... 19
4. Experiment results and discussions .................................................................... 23
4.1 The data set ................................................................................................ 23
4.2 Weighting function .................................................................................... 27
4.3 Comparison of PCA ................................................................................... 27
4.4 Comparison of single-scale and multi-scale classifications ....................... 32
5. Conclusions and future work ............................................................................. 36
References ................................................................................................................. 38
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