【摘要】：地面三維激光掃描在獲取面狀數(shù)據領域發(fā)揮的作用越來越大,其地位越來越高。用它采集滑坡區(qū)域的點云數(shù)據時,必然要求能夠精確地求得滑坡區(qū)域的沉降值;然而要想在硬件上取得突破,一般是比較難的。因此我們把精確地獲得滑坡區(qū)域沉降值的這一目標放在對它的數(shù)據處理上來。鑒于許多學者已使用了時間序列或者卡爾曼濾波等方法求取滑坡區(qū)域的沉降值,本文采用小波神經網絡分析方法實現(xiàn)精確地求得滑坡區(qū)域的沉降值。研究內容主要包括地面三維激光掃描數(shù)據的預處理部分和利用小波神經網絡的分析方法進行網絡訓練并且最終精確求得沉降值兩部分。地面三維激光掃描數(shù)據的預處理包括點云的拼接、濾波、縮減和分割四部分,其最終實現(xiàn)了把不同測站測得的點云坐標統(tǒng)一在同一個坐標系下,剔除了粗差比較大的點云。基于小波神經網絡分析方法進行曲面擬合,以及精確地求取滑坡區(qū)域的沉降值,首先利用了隱式曲面構造的基本原理,采用了雙三次插值擬合的方法構造出了關于點云數(shù)據的隱式曲面,發(fā)現(xiàn)采用這種方法擬合出的曲面精度不高,因此這種方法不適合針對大規(guī)模點云數(shù)據進行曲面擬合。采用小波神經網絡分析方法,經過網絡訓練后的點云數(shù)據就可以對這個神經網絡進行非線性的無限逼近,進而得到一個的隱式曲面;與此同時,也用BP神經網絡對點云數(shù)據進行了曲面重建,通過對比兩種網絡模型的曲面擬合精度,發(fā)現(xiàn)小波神經網絡具有比基于BP神經網絡的地表模型擬合表現(xiàn)出更高的精度,更高的收斂速度;利用MATLAB軟件平臺編寫了人機交互圖形化用戶使用界面GUI,求得了沉降值,驗證了利用小波神經網絡對曲面進行擬合這種方法的可靠性和科學性。
[Abstract]:3-D laser scanning plays a more and more important role in obtaining surface data, and its position is getting higher and higher. When it is used to collect point cloud data of landslide area, it is necessary to obtain the settlement value of landslide area accurately, however, it is difficult to make a breakthrough in hardware. Therefore, we put the goal of accurately obtaining the settlement value of landslide area on the processing of its data. In view of the fact that many scholars have used time series or Kalman filter to calculate the settlement value of landslide region, wavelet neural network analysis method is used in this paper to obtain the settlement value of landslide region accurately. The research includes two parts: the preprocessing part of the ground 3D laser scanning data and the training of the network by using the wavelet neural network analysis method and the final accurate calculation of the settlement value. The preprocessing of ground 3D laser scanning data consists of four parts: point cloud splicing, filtering, reduction and segmentation. Finally, the point cloud coordinates measured by different stations are unified in the same coordinate system, and the point clouds with large gross errors are eliminated. Based on wavelet neural network analysis method, surface fitting is carried out, and the settlement value of landslide area is calculated accurately. Firstly, the basic principle of implicit surface construction is used. The implicit surface of point cloud data is constructed by using bicubic interpolation fitting method. It is found that the accuracy of surface fitting by this method is not high, so this method is not suitable for surface fitting of large-scale point cloud data. Using wavelet neural network analysis method, the point cloud data after network training can be nonlinear infinite approximation to this neural network, and then an implicit surface can be obtained. At the same time, BP neural network is used to reconstruct the surface of point cloud data. By comparing the surface fitting accuracy of the two network models, it is found that the wavelet neural network has higher accuracy than the surface model fitting based on BP neural network. Higher convergence rate; The settlement value of graphical user interface (GUI,) for human-computer interaction is obtained by using MATLAB software platform, and the reliability and scientificity of this method for surface fitting using wavelet neural network is verified.
【學位授予單位】：長安大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：P225.2

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