雲線擬合於全波形光達之特徵萃取與地物分類

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姓名

鄭亦修(Yi-Hsiu Cheng) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

雲線擬合於全波形光達之特徵萃取與地物分類
(Spline Curve Fitting of Full-waveform LIDAR Data and Feature Extraction for Land-cover Classification)

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摘要(中)

空載全波形光達為一主動式遙測系統，除了可快速取得地表之三維坐標外，還可將雷射回傳能量值透過密集取樣紀錄成連續波形。透過對回波的模擬與分析，可進一步推算雷射光束所涵蓋的範圍內地表物的物理特性，提供使用者豐富的資訊應用於地形三維重建與地物辨識之依據。
在應用全波形光達資料於地物分類時，首先須先對波形進行擬合(fitting)，進而分析其特性。本研究將濾除背景雜訊後的波形資料，以立方平滑雲線的方式進行擬合，透過二次微分法偵測波形波峰並萃取波形參數，包括波寬(width)、振幅(amplitude)。在波形存有多重回波的情況，可從中萃取多重回波特徵包括第一與最後回波之時間差、波峰數、平均振幅。利用萃取出的波形參數，配合正規化高程(normalized height)、強度(intensity)做為地物分類的特徵。本研究使用隨機森林(random forest)作為分類器，並將分類成果與常用的高斯分解法做比較，藉此探討立方平滑雲線應用於全波形光達資料的成效。
本研究結果顯示，立方平滑雲線相較與高斯法擬合誤差較小，並保留了更多波形資訊。在地物分類的部分，在容易混淆的建物邊緣與樹林，多重回波特徵能提高其分類精度。另外相較於高斯，立方平滑雲線除了分類精度較優外，能以更快速的方式來計算特徵，適合於擁有大量資料的全波形光達。

摘要(英)

Airborne Full-Waveform LiDAR (FW) is an active remote sensing system. It not only provides the three-dimensional coordinates about the ground objects but also record the whole return signal as the waveform. The physical properties of objects in a ray can be obtained by fitting and analyzing the waveform. It offers useful information to user for three dimensional reconstruction and land cover distinguishing.
In the processing of FW LiDAR data for land-cover classification, the waveform fitting and analysis are the first steps. In this study, the waveform data was fitted by cubic smoothing spline after eliminating the background noise. The amplitude and width was derived based on the peaks detected by second derivative method. In the case of the waveform with the multiple returns, the feature such as time difference of first and last return, peak numbers, and average amplitude was obtained. These waveform parameters combined with intensity and normalized height was utilized as the features for land-cover classification. The classifier used in this study was Random Forest. In order to discuss the effect of cubic smoothing spline, the classification result was compared to the Gaussian decomposition method which is a popular method in full-waveform application.
The experimental results indicate that cubic smoothing spline provide the smaller fitting error and keep more information in the waveform. The land cover classification results demonstrate that the multiple return features are helpful for the building edge and trees which are easily misclassified. In addition, cubic smoothing spline is suitable for full-waveform Lidar data with the better classification result and efficiency.

關鍵字(中)

★ 全波形光達
★ 波形擬合
★ 立方平滑雲線
★ 高斯分解
★ 地物分類
★ 隨機森林分類器

關鍵字(英)

★ Full-waveform LiDAR
★ fitting
★ cubic smoothing spline
★ Gaussian decomposition
★ Random Forest

論文目次

摘要 i
Abstract iii
致謝 v
目錄 vi
圖目錄 x
表目錄 xiii
第1章緒論 1
1-1研究背景 1
1-2研究動機與目的 3
1-3論文架構 4
第2章文獻回顧 5
2-1全波光達原理介紹 5
2-2全波形光達應用 9
2-2-1波形點偵測 9
2-2-2擬合函數 13
2-2-3全波形於分類上應用 17
2-3全波形光達儀器與資料介紹 21
2-3-1ALTM Pegasus 22
2-3-2 Lecia ALS系列 22
2-3-3 Riegl LMS系列 23
2-3-4 Trimble A80 24
2-4光達資料格式介紹 25
第3章研究方法 27
3-1資料前處理 28
3-2波形擬合 29
3-2-1立方平滑雲線 29
3-2-2高斯擬合 33
3-3 波形分析 36
3-3-1波峰偵測 36
3-3-2波形切割 38
3-3-3特徵萃取 40
3-4地物分類 42
3-4-1隨機森林分類法 42
3-4-2分類精度評估 44
第4章研究區域及資料 46
4-1測試資料一 48
4-2測試資料二 50
4-3測試資料三 52
第5章成果分析 53
5-1波形分析 53
5-1-1地物波形 53
5-1-2波形擬合 56
5-2分類成果分析 58
5-2-1測試資料一 58
5-2-2測試資料二 61
5-2-3測試資料三 63
第6章結論與建議 67
6-1結論 67
6-2建議 68
參考文獻 69

參考文獻

林郁珊，2012，應用空載全波形光達資料於波形分析與地物分類，碩士論文，國立交通大學土木工程學系。
盧佑樺，2013，二次微分法於空載全波形光達之特徵萃取與地物分類，碩士論文，國立中央大學土木工程學系。
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指導教授

蔡富安(Fuan Tsai)

審核日期

2014-7-31

推文