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基于機(jī)載激光雷達(dá)技術(shù)的茂密林地單株木識(shí)別
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國(guó)家高技術(shù)研究發(fā)展計(jì)劃（863計(jì)劃）資助項(xiàng)目(2007AA120501)和國(guó)家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃（973計(jì)劃）資助項(xiàng)目(2006CB701300)


Individual Trees Recognition in Dense Forest Based on Airborne LiDAR
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    摘要:

    提出一種利用LiDAR數(shù)據(jù)進(jìn)行單株木識(shí)別的方法,，首先利用廣義高斯模型分解全波形LiDAR數(shù)據(jù),，得到高密度的點(diǎn)云和相應(yīng)的波形參數(shù)，通過建立數(shù)字高層模型得到非地面點(diǎn)云,，然后計(jì)算點(diǎn)云的空間特征得到林木點(diǎn)云，最后在3D空間中利用馬爾可夫隨機(jī)場(chǎng)重新標(biāo)記得到單株木點(diǎn)云,。實(shí)驗(yàn)表明,，與傳統(tǒng)方法相比，本文方法能有效提高單株木識(shí)別的準(zhǔn)確性,，特別是對(duì)茂密林地中低矮,、細(xì)小林木識(shí)別效果明顯，平均識(shí)別精度達(dá)到75%,。

    Abstract:

    By analyzing the shortage of traditional approach,a new individual trees recognition method was proposed. Firstly, the generalized Gaussian function was used to analyze the fitting pulse shape LiDAR data, and the high density point cloud and the waveform parameters were obtained, then the non-ground points were gained by establishing DEM; secondly, the spatial characteristics of point cloud was computed to receive forest points; lastly, Markov random fields were exploited to label individual trees in 3D. The experimental results show that this method can effectively improve the recognition accuracy, especially in the low dense, small trees identification effect, and the average recognition accuracy is 75%. 

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劉峰,龔健雅.基于機(jī)載激光雷達(dá)技術(shù)的茂密林地單株木識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2011,42(7):200-203,209. Liu Feng, Gong Jianya. Individual Trees Recognition in Dense Forest Based on Airborne LiDAR[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(7):200-203,209.

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