本文關(guān)鍵詞： LiDAR 濾波 分類 二面角濾波 決策樹　出處：《首都師范大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：激光雷達(dá)(LiDAR, Light Detection and Ranging)是一種向目標(biāo)發(fā)射激光束,將接收的目標(biāo)返回信號與發(fā)射信號比較,通過處理得到地物三維信息和地面空間特征信息的雷達(dá)系統(tǒng)[1-2]。近年來,因LiDAR技術(shù)可快速獲取高空間分辨率地表三維信息、高自動(dòng)化數(shù)據(jù)采集效率,從而廣泛應(yīng)用于地形測繪、城市建模等多個(gè)領(lǐng)域[3-5]。激光雷達(dá)數(shù)據(jù)是離散的三維點(diǎn)云,點(diǎn)云數(shù)據(jù)的應(yīng)用明顯滯后于激光雷達(dá)系統(tǒng)的硬件發(fā)展。如何快速地處理大量三維點(diǎn)云數(shù)據(jù),獲取關(guān)注的建筑物三維信息,實(shí)現(xiàn)建筑物的提取工作,是學(xué)者們研究的重點(diǎn)和難點(diǎn)。 基于以上觀點(diǎn),本文深入了解總結(jié)了近十多年來激光LiDAR點(diǎn)云數(shù)據(jù)濾波和分類的相關(guān)方法。重點(diǎn)研究LiDAR點(diǎn)云數(shù)據(jù)的濾波和分類算法兩部分,首先根據(jù)首、末次回波高程值,去除植被點(diǎn),將剩下的點(diǎn)云數(shù)據(jù)規(guī)則格網(wǎng)化,提升點(diǎn)云的處理效率,隨后,提出一種新的考慮到相鄰三點(diǎn)間高程變化快慢程度的二面角濾波法,進(jìn)行濾波,然后,通過濾波前后的高程值變化對實(shí)驗(yàn)區(qū)進(jìn)行區(qū)域分割,引入地物二面角均值屬性特征,并結(jié)合回波次數(shù)、高程值和回波強(qiáng)度三個(gè)參數(shù)作為判定地物分類的約束條件,構(gòu)建決策樹;最后,采用Alpha Shapes算法提取建筑物輪廓線,進(jìn)行地表三維顯示。 本文的主要研究內(nèi)容如下： 1.深入地對機(jī)載雷達(dá)系統(tǒng)的定位原理以及點(diǎn)云的數(shù)據(jù)結(jié)構(gòu)進(jìn)行了介紹；并且,總結(jié)了激光LiDAR點(diǎn)云數(shù)據(jù)的處理流程。根據(jù)首、尾次回波高程值,去除植被點(diǎn),進(jìn)一步將點(diǎn)云數(shù)據(jù)進(jìn)行規(guī)則格網(wǎng)化,提升了LiDAR點(diǎn)云的處理效率。 2.在規(guī)則格網(wǎng)存儲的LiDAR點(diǎn)云基礎(chǔ)上,鑒于傳統(tǒng)的計(jì)算兩點(diǎn)間坡度值的濾波算法,在地形變化劇烈時(shí)難以確定坡度閾值的情況,本文提出一種新的考慮到相鄰三點(diǎn)間高程變化快慢程度的二面角濾波法,首次將空間二面角的平面角余弦值,表達(dá)空間中相鄰兩平面相對位置的概念引入LiDAR點(diǎn)云數(shù)據(jù)濾波中。首先,基于地表的連續(xù)性提取LiDAR點(diǎn)云數(shù)據(jù)中的高程突變點(diǎn),然后,分別統(tǒng)計(jì)高程突變點(diǎn)和非突變點(diǎn)的二面角余弦頻數(shù)分布,采用交點(diǎn)處對應(yīng)的余弦值和提取高程突變點(diǎn)迭代的最小坡度閾值來判定地面點(diǎn)、非地面點(diǎn),最后引入數(shù)學(xué)形態(tài)學(xué)“開”算子,去除低矮植被,最終得到可靠的濾波結(jié)果。本文方法,針對復(fù)雜城區(qū)環(huán)境,在濾除大型建筑物的同時(shí),能準(zhǔn)確快速地獲取地面點(diǎn)集。 3.本文通過濾波前后LiDAR點(diǎn)云數(shù)據(jù)的高程值變化進(jìn)行區(qū)域分割：在點(diǎn)云數(shù)據(jù)濾波后,LiDAR數(shù)據(jù)點(diǎn)被分為地面點(diǎn)及非地面點(diǎn),于非地面點(diǎn)集中采用區(qū)域增長法分割點(diǎn)云；采用二面角均值、回波次數(shù)、高程值和回波強(qiáng)度四個(gè)參數(shù)構(gòu)建決策樹,將實(shí)驗(yàn)區(qū)地物分類為建筑物、植被、地面、道路四個(gè)屬性,在此基礎(chǔ)上,采用Alpha Shapes算法提取建筑物輪廓線,實(shí)現(xiàn)地表的三維顯示 4.本文選擇了2塊海地太子港的局部LiDAR點(diǎn)云數(shù)據(jù)作為試驗(yàn)區(qū),在Visual studio2010中采用二面角濾波法,進(jìn)行點(diǎn)云數(shù)據(jù)的濾波,并在現(xiàn)有濾波方法中,與“漸進(jìn)三角網(wǎng)法”(TerraSolid-Scan軟件)進(jìn)行對比分析,驗(yàn)證了本文算法的可行性。統(tǒng)計(jì)了分類結(jié)果混淆矩陣及Kappa系數(shù),對本文的分類精度進(jìn)行評估。最后,在Visual studio2010中實(shí)現(xiàn)了實(shí)驗(yàn)數(shù)據(jù)建筑物的提取和三維顯示。
[Abstract]:Laser radar ( LiDAR , Light Detection and Radar ) is a kind of radar system which emits laser beam to the target , compares the received target return signal with the transmission signal , and obtains the three - dimensional information of the ground object and the surface space feature information by processing . In recent years , the application of the LiDAR technology can quickly acquire the three - dimensional information of high spatial resolution surface and high automation data acquisition efficiency . The laser radar data is a discrete three - dimensional point cloud , and the application of point cloud data is obviously lagging behind the hardware development of the laser radar system . Based on the above viewpoint , this paper deeply understands the correlation method of laser LiDAR point cloud data filtering and classification for more than ten years . It focuses on the filtering and classification algorithms of LiDAR point cloud data . Firstly , according to the first and last echo elevation values , the vegetation points are removed , the remaining point cloud data rules are meshed and the processing efficiency of the point cloud is promoted . Then , a new method is put forward , which takes the three parameters of the echo frequency , the elevation value and the echo intensity to be used as the constraint conditions for judging the classification of the ground objects , and finally , the surface three - dimensional display is carried out by using the Alpha Shapes algorithm to extract the building outline . The main contents of this paper are as follows : 1 . The positioning principle of airborne radar system and the data structure of point cloud are introduced in - depth ; and the processing flow of laser LiDAR point cloud data is summarized . According to the first and tail echo elevation values , the vegetation points are removed , and the point cloud data is further regularly meshed , so that the processing efficiency of the LiDAR point cloud is improved . 2 . On the basis of the LiDAR point cloud stored in the regular grid , in view of the traditional filtering algorithm for calculating the slope value between two points , it is difficult to determine the slope threshold value when the terrain changes violently . 3 . This paper divides the elevation value of LiDAR point cloud data before and after filtering : After the point cloud data is filtered , the LiDAR data points are divided into the ground point and the non - ground point , and the point cloud is divided by the regional growth method in the non - ground point set ; 4 . In this paper , the local LiDAR point cloud data in Port - au - Prince of Haiti is selected as the test area . In Visual Studio2010 , the filtering of point cloud data is carried out , and compared with TerraSolid - Scan software in the existing filtering method , the feasibility of the algorithm is verified . The classification accuracy is evaluated by using the classification result confusion matrix and Kappa coefficient . Finally , the extraction and three - dimensional display of experimental data buildings are realized in Visual Studio2010 .

【學(xué)位授予單位】：首都師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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