【摘要】：傳統(tǒng)的線性預(yù)測(cè)濾波算法將目標(biāo)點(diǎn)云劃分為多個(gè)柵格,濾波在每一個(gè)柵格內(nèi)進(jìn)行,而濾波柵格大小,需要用戶手動(dòng)調(diào)整。針對(duì)此問題,該文提出一種從機(jī)載激光掃描數(shù)據(jù)中生成數(shù)字高程模型(DEM)的有效方法。引入統(tǒng)計(jì)學(xué)變量——面高程變異系數(shù),刻畫地形起伏特征,并建立線性預(yù)測(cè)濾波算法中柵格大小與面高程變異系數(shù)之間的函數(shù)關(guān)系。最后,利用幾組點(diǎn)云數(shù)據(jù)為研究對(duì)象驗(yàn)證該方法的有效性,實(shí)驗(yàn)結(jié)果表明,該方法能自適應(yīng)地根據(jù)地形的起伏特征調(diào)整濾波參數(shù),得到比較理想的地面點(diǎn)數(shù)據(jù),最終內(nèi)插得到高精度的DEM。
[Abstract]:The traditional linear predictive filtering algorithm divides the target point cloud into multiple grids, and the filter is carried out in each grid, and the size of the filter grid needs to be adjusted manually by the user. In order to solve this problem, an effective method for generating digital elevation model (DEM) from airborne laser scanning data is proposed in this paper. The statistical variable, the variation coefficient of surface elevation, is introduced to depict the characteristics of terrain fluctuation, and the functional relationship between the grid size and the coefficient of variation of surface elevation in linear predictive filtering algorithm is established. Finally, several groups of point cloud data are used to verify the effectiveness of the method. The experimental results show that the method can adaptively adjust the filtering parameters according to the undulating characteristics of the terrain, obtain the ideal ground point data, and finally insert the high precision DEM..
【作者單位】： 廣東省國(guó)土資源測(cè)繪院;國(guó)家超級(jí)計(jì)算深圳中心;南開大學(xué)周恩來政府管理學(xué)院;
【基金】：空間信息智能感知與服務(wù)深圳市重點(diǎn)實(shí)驗(yàn)室(深圳大學(xué))開放基金資助項(xiàng)目 中央高�；究蒲袠I(yè)務(wù)費(fèi)專項(xiàng)資金項(xiàng)目(NKZXB1483)
【分類號(hào)】：P208;P237

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