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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题名:	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
作者:	鄭亦修;Cheng,Yi-Hsiu
贡献者:	土木工程學系
关键词:	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;Full-waveform LiDAR;fitting;cubic smoothing spline;Gaussian decomposition;Random Forest
日期:	2014-07-31
上传时间:	2014-10-15 14:34:12 (UTC+8)
出版者:	國立中央大學
摘要:	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。 ;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.
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