發(fā)布時間：2018-07-01 07:52

  本文選題：機載LiDAR + 建筑物提取　； 參考：《應用科學學報》2016年01期

【摘要】：基于Li DAR數(shù)據(jù),提出一種由粗到細的面向?qū)ο蟮慕ㄖ镒詣犹崛》椒?首先通過機載Li DAR數(shù)據(jù)構建出歸一化數(shù)字表面模型(normalized digital surface model,n DSM),利用首尾兩次回波高程計算出歸一化差值(normalized difference,ND),并采用形態(tài)學運算消除邊緣特殊回波點.基于n DSM和ND數(shù)據(jù),依據(jù)建筑物的高程及穿透性信息,用閾值分割法進行建筑物粗提取.結合n DSM和ND數(shù)據(jù)以及強度信息,對粗提取得到的備選建筑物采取多尺度分割,合并亮度值相差較小的鄰近分割結果對象,達到對分割結果的優(yōu)化處理.最后利用目標對象的亮度、形狀、面積和空間關系等特征,完成建筑物的精提取.實驗結果表明,該方法可得到較高精度的建筑物信息,是基于機載Li DAR數(shù)據(jù)提取建筑物的新思路.
[Abstract]:Based on Li Dar data, an object-oriented automatic building extraction method from coarse to fine is proposed. Firstly, a normalized digital surface model (normalized digital surface modeln DSM) is constructed from airborne Li Dar data, and the normalized difference (normalized difference ND) is calculated by using the first and last echo heights, and the edge special echo points are eliminated by morphological operation. Based on the data of n DSM and ND, the rough extraction of buildings is carried out by threshold segmentation method according to the height and penetration information of buildings. Based on the data of n DSM and ND and intensity information, multi-scale segmentation is adopted for the rough extracted alternative buildings, and the adjacent segmentation objects with small difference in luminance are combined to achieve the optimal processing of the segmentation results. Finally, using the brightness, shape, area and spatial relationship of the target object, the fine extraction of the building is completed. The experimental results show that this method can obtain high accuracy building information and is a new idea for building extraction based on airborne Li Dar data.
【作者單位】： 中國礦業(yè)大學環(huán)境與測繪學院;
【基金】：國家自然科學基金(No.41331175)資助
【分類號】：TU198;TN958.98

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本文編號：2087079