本文選題：建筑物輪廓　切入點(diǎn)：建筑物輪廓規(guī)則化　出處：《西南交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：建筑物是城市重要的組成,是"數(shù)字城市"不可缺少的組成部分。從機(jī)載Lidar(Light Detection And Ranging)點(diǎn)云中提取建筑物是城市建模的關(guān)鍵問(wèn)題之一,而建筑物的輪廓?jiǎng)t是表達(dá)建筑物的關(guān)鍵信息。目前建筑物輪廓提取的研究大都是針對(duì)完整的建筑物點(diǎn)云。針對(duì)由于相鄰高大樹(shù)木等對(duì)建筑物的遮擋,使得建筑物點(diǎn)云存在部分缺失,建筑物輪廓不完整的情況,目前還沒(méi)有有效的輪廓提取方法。本文針對(duì)建筑物被遮擋的情況下準(zhǔn)確提取建筑物輪廓開(kāi)展兩方面研究:1)濾波,將原始Lidar點(diǎn)云中,地面點(diǎn)和非地面點(diǎn)分開(kāi);2)建筑物輪廓提取,從非地面點(diǎn)云中提取建筑物點(diǎn),并提取建筑物輪廓。首先在現(xiàn)有的偏度平衡濾波(SKF)算法的基礎(chǔ)上,利用局部擬合高差代替點(diǎn)的高程,提出基于高差的偏度平衡濾波(SKF-HD)算法。該算法保持了對(duì)高大地物提取效果的同時(shí),提高了低矮地物的提取效果,且顯著提高了地形起伏區(qū)域的適應(yīng)性。三組不同地形、不同區(qū)域的實(shí)驗(yàn)結(jié)果表明該算法能夠更好的提取低矮地物、適用于不同程度的起伏地形。與偏度平衡濾波算法相比較,提出的算法在平坦區(qū)域、地形起伏較小區(qū)域和地形顯著起伏區(qū)域的總體精度分別增大4.8%、5.1%和13.3%。在不同地形條件下,尤其是在地形顯著起伏區(qū)域,新算法的濾波精度得到了明顯的提高。然后改進(jìn)MBR算法,針對(duì)被遮擋的規(guī)則多邊形建筑物提出多級(jí)最小外接矩形(MMBR)算法,準(zhǔn)確的提取建筑物的輪廓。該方法不僅可以準(zhǔn)確得到建筑物沒(méi)有遮擋部位的輪廓的同時(shí)還能得到準(zhǔn)確的被遮擋部位的輪廓。三組不同遮擋情況、不同形狀的實(shí)驗(yàn)區(qū)域的實(shí)驗(yàn)結(jié)果表明該算法能夠準(zhǔn)確的得到被遮擋區(qū)域的建筑物輪廓。與直角約束的迭代最小二乘(HLSPC)算法相比較,提出的算法在規(guī)則矩形建筑物、L型復(fù)雜多邊形建筑物和復(fù)雜多邊形建筑物中都能準(zhǔn)確的提取建筑物輪廓,且Vd值分別減小31.8%、14.3%和12.5%。在不同遮擋情況下,MMBR算法的精度都較高。
[Abstract]:Building is an important part of a city and an indispensable part of "digital city". Extracting buildings from airborne Lidar(Light Detection And moving cloud is one of the key problems in city modeling. The outline of the building is the key information to express the building. At present, the research of extracting the outline of the building is mostly aimed at the complete point cloud of the building. There is no effective method to extract the building contour because of the partial absence of the building point cloud and the incomplete outline of the building. In this paper, when the building is occluded, two aspects of the research: 1) filtering are carried out to accurately extract the building contour. The building contour is extracted from the original Lidar point cloud, the ground point and the non-ground point are separated, and the building contour is extracted from the non-ground point cloud. Firstly, based on the existing skewness balance filtering algorithm, the structure contour is extracted from the non-ground point cloud. By using local fitting height difference instead of elevation, a skewness balanced filter SKF-HD algorithm based on height difference is proposed, which not only keeps the extraction effect of tall ground objects, but also improves the extraction effect of low ground objects. The experimental results of three groups of different terrain and different regions show that the algorithm can extract low ground objects better, and is suitable for different degree of undulating terrain. The overall accuracy of the proposed algorithm increases by 4.8% and 13.3% respectively in the flat region, the small relief area and the significant relief area. Under different terrain conditions, especially in the terrain significant undulating area, the proposed algorithm increases the accuracy of the proposed algorithm by 4.8% and 13.3%, respectively, under different terrain conditions, especially in the region with significant topographic relief. The filtering accuracy of the new algorithm is improved obviously, and then the MBR algorithm is improved, and the multilevel minimum outer rectangle MMBR algorithm is proposed for the occluded regular polygon building. This method can not only accurately get the contour of the unoccluded part of the building, but also get the contour of the occluded part. The experimental results of different shapes show that the proposed algorithm can accurately obtain the building contour of the occluded region, which is compared with the iterative least squares (HLSPC) algorithm with rectangular constraints. The proposed algorithm can accurately extract the building contours in regular rectangular buildings with L-shaped complex polygon and complex polygonal buildings, and the V _ d value decreases by 31.8- 14.3% and 12.5% respectively. The accuracy of MMBR algorithm is higher under different occlusion conditions.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P237

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