本文選題：全波形數(shù)據(jù) + 高斯分解��； 參考：《武漢大學(xué)》2017年碩士論文

【摘要】：機(jī)載LiDAR系統(tǒng)作為一種主動遙感技術(shù),可以直接量測激光掃描儀與地形之間的距離,獲取地面點的三維坐標(biāo),在森林參數(shù)估計、三維城市建模、電力線檢測和數(shù)字地面模型生成等方面有著廣泛的應(yīng)用。目前的機(jī)載LiDAR系統(tǒng)大多具有波形數(shù)字化能力,可以記錄發(fā)射脈沖的整個反射回波信號。用戶可以通過分析回波波形得到地物的額外特征信息,進(jìn)一步研究波形參數(shù)在植被提取、地物分類等方面的應(yīng)用具有很大意義。本文通過波形數(shù)據(jù)分解得到的波形參數(shù)特征,研究波形參數(shù)在點云濾波與地物分類方面的應(yīng)用,探索更加自動、精度更高的地物信息提取方法,從而發(fā)揮波形數(shù)據(jù)的應(yīng)用優(yōu)勢。本文的主要研究內(nèi)容如下:(1)機(jī)載LiDAR波形數(shù)據(jù)分解。在研究總結(jié)了波形數(shù)據(jù)高斯分解方法的基礎(chǔ)上,提出了一種波形橫向高斯分解初始參數(shù)估計方法,該方法對于復(fù)雜的多次疊加波分解,能夠得到較為精確的初始參數(shù)。在去除無效的高斯分量后,利用LM算法對初始參數(shù)進(jìn)行優(yōu)化,最后利用波峰位置作為地物點響應(yīng)位置解算得到生產(chǎn)點云。實驗表明,本方法能夠有效地檢測各種類型的回波信號,波形分解生成的點云數(shù)量更多,層次更加豐富。(2)波形特征輔助的點云濾波。由波形分解得到的不同地物點具有不同的波形特征,本文選擇波形寬度信息作為輔助特征以提高點云濾波質(zhì)量,首先利用寬度閾值去除明顯的非地物點,然后利用寬度信息計算每個點的權(quán)重,利用多級加權(quán)曲面濾波方法進(jìn)行點云濾波,實現(xiàn)高精度的地形重建。實驗表明,該方法能夠很好地去除低矮植被等非地面點的影響,一定程度上提高濾波的正確率。(3)融合波形特征和幾何特征的點云分類。在波形分解的基礎(chǔ)上,通過分析得到的參數(shù)信息(強(qiáng)度、寬度、回波次數(shù)),結(jié)合曲率、高程差幾何信息,選擇樣本區(qū)域進(jìn)行訓(xùn)練構(gòu)建決策樹,對濾波后的分解點云進(jìn)行地物分類,將非地面點分為建筑物、樹木和低矮植被三種類型。實驗表明,對測試集分類的總體精度達(dá)到了 94.86%,各類地物都取得了較好的分類效果。
[Abstract]:As an active remote sensing technology, airborne LiDAR system can directly measure the distance between laser scanner and terrain, obtain 3D coordinates of ground points, estimate forest parameters, and model 3D cities. Power line detection and digital ground model generation are widely used. At present, most airborne LiDAR systems have the ability of waveform digitization, which can record the whole reflected echo signal of the transmitted pulse. By analyzing the echo waveform, the user can obtain the additional feature information of the ground objects, and further study the application of waveform parameters in vegetation extraction, classification of ground objects and so on. In this paper, we study the application of waveform parameters in point cloud filtering and ground object classification by decomposing waveform data, and explore a more automatic and accurate method for extracting ground object information, so as to give play to the advantages of waveform data application. The main contents of this paper are as follows: 1) decomposition of airborne LiDAR waveform data. On the basis of studying and summarizing the Gao Si decomposition method of waveform data, a method for estimating the initial parameters of waveform transverse Gao Si decomposition is proposed. This method can obtain more accurate initial parameters for complex multiple superposition wave decomposition. After the invalid Gao Si component is removed, the initial parameters are optimized by LM algorithm, and the production point cloud is calculated by using the peak position as the response position of the ground object. Experiments show that this method can effectively detect various types of echo signals, and the number of point clouds generated by waveform decomposition is more, and the level is more abundant. Different ground objects obtained from waveform decomposition have different waveform characteristics. In this paper, the waveform width information is selected as the auxiliary feature to improve the quality of point cloud filtering. Firstly, the width threshold is used to remove the obvious non-ground points. Then the weight of each point is calculated by using the width information and the point cloud filtering is carried out by using the multi-level weighted surface filtering method to realize the high-precision terrain reconstruction. Experiments show that this method can remove the influence of non-ground points such as low vegetation and improve the accuracy of filtering to a certain extent. On the basis of waveform decomposition, through analyzing the parameter information (intensity, width, echo frequency), combining the geometric information of curvature and elevation difference, we select the sample area to train to construct the decision tree. The decomposed point cloud after filtering is classified into three types: buildings, trees and low vegetation. The experimental results show that the overall accuracy of the classification of the test set is 94.866.All kinds of ground objects have achieved better classification results.
【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P237

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