本文選題：LiDAR + 屋頂面��； 參考：《武漢大學學報(信息科學版)》2014年10期

【摘要】：區(qū)域增長法和隨機抽樣一致性(RANSAC)算法是從LiDAR數據提取屋頂面時常用的兩類方法,但這兩種方法都存在某些缺陷,使它們的應用受到了一定限制。針對LiDAR數據中建筑物腳點的特點,提出了一種融合以上兩種方法優(yōu)點于一體的合成算法。1根據腳點的法向量和粗糙度特征進行屋頂面粗提取;2在屋頂面粗提取結果的基礎上,利用基于先驗知識的局部采樣策略和區(qū)域增長方式對傳統(tǒng)隨機抽樣一致性算法進行擴展,實現(xiàn)屋頂面自動提取;3采用投票法解決屋頂面競爭問題,提高屋頂面的提取精度。實驗結果表明,本文設計的合成算法能夠有效地提取建筑物屋頂面。
[Abstract]:Regional growth method and random sampling conformance (RANSAC) algorithm are two kinds of methods commonly used to extract roof surface from LiDAR data. However, there are some defects in these two methods, which make their applications limited. In view of the characteristics of the foothold in the building of the LiDAR data, a combination of the advantages of the above two methods is proposed. The algorithm.1 extracts the roof surface according to the normal vector of the foot and the roughness feature. 2 on the basis of the rough extraction results of the roof surface, using the local sampling strategy and the regional growth mode based on the prior knowledge to expand the traditional random sampling consistency algorithm, the roof surface auto extraction is realized; 3 the roof competition is solved by voting method. The experimental results show that the synthetic algorithm designed in this paper can effectively extract the roof surface of buildings.

【作者單位】： 武漢大學遙感信息工程學院;
【基金】：國家自然科學基金資助項目(61378078) 國家科技支撐計劃資助項目(2012BAH34B02) 中央高校基本科研業(yè)務費專項基金資助項目(2012213020203,2012213020209)~~
【分類號】：TP391.41;TU746.3

【參考文獻】

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本文編號：1806964