本文選題：LiDAR + 高光譜　； 參考：《電子科技大學(xué)》2015年碩士論文

【摘要】：高速發(fā)展的遙感技術(shù),使得傳統(tǒng)光學(xué)遙感已經(jīng)無法滿足林木資源調(diào)查對空間信息和光譜信息的精度要求。近年來,由于激光雷達(dá)(Light Detection and Ranging,LiDAR)高精度的三維空間信息以及高光譜數(shù)據(jù)豐富的光譜信息,使二者迅速在各行各業(yè)得到了廣泛應(yīng)用。在LiDAR數(shù)據(jù)與高光譜數(shù)據(jù)空間信息與光譜信息互相補(bǔ)償?shù)臈l件下,協(xié)同兩種數(shù)據(jù)將更有利于提高林木參數(shù)反演精度。本文利用地面三維激光掃描儀Leica Scanstation C10和成像光譜儀SOC710協(xié)同獲取單木的三維空間信息和光譜信息,進(jìn)行了以下幾個方面的工作:(1)總結(jié)了國內(nèi)外針對LiDAR數(shù)據(jù)和高光譜數(shù)據(jù)的研究現(xiàn)狀,重點(diǎn)介紹了協(xié)同兩種數(shù)據(jù)在林業(yè)方面的研究現(xiàn)狀以及LiDAR與光學(xué)遙感數(shù)據(jù)配準(zhǔn)方法的研究現(xiàn)狀;(2)針對地面LiDAR點(diǎn)云數(shù)據(jù)的離散性和巨大的數(shù)據(jù)量,本文建立了基于四叉樹的LiDAR點(diǎn)云數(shù)據(jù)索引機(jī)制;(3)針對地面LiDAR數(shù)據(jù)與高光譜數(shù)據(jù)成像方式的不同,LiDAR點(diǎn)云數(shù)據(jù)是離散的三維空間信息,高光譜數(shù)據(jù)是二維光譜圖像,本文模擬相機(jī)成像方式,將LiDAR點(diǎn)云數(shù)據(jù)二維圖像化;(4)針對LiDAR自身強(qiáng)度信息和高光譜數(shù)據(jù)灰度信息的差異,本文研究了基于控制點(diǎn)、基于特征以及基于互信息的配準(zhǔn)方法,提出結(jié)合控制點(diǎn)和特征互信息的配準(zhǔn)方法,利用基于控制點(diǎn)的配準(zhǔn)方法實現(xiàn)LiDAR圖像與高光譜圖像的粗配準(zhǔn),然后將得到的粗配準(zhǔn)參數(shù)作為Powell算法搜索的初始化參數(shù),利用特征互信息作為相似性度量,該方法實現(xiàn)了LiDAR圖像與高光譜圖像的精配準(zhǔn)。(5)針對利用地面LiDAR自身強(qiáng)度信息無法實現(xiàn)葉片與枝干分割,基于幾何信息實現(xiàn)LiDAR數(shù)據(jù)的葉片與枝干分割,計算量非常大,本文根據(jù)樹的葉片和枝干光譜曲線的區(qū)別,協(xié)同高光譜數(shù)據(jù)的光譜信息實現(xiàn)了單木LiDAR數(shù)據(jù)枝干與樹葉的分割,最后利用樹葉LiDAR點(diǎn)云數(shù)據(jù),實現(xiàn)了基于VCP(Voxel-based Canopy)算法的葉面積密度估計�？傊�,本文針對地面LiDAR點(diǎn)云數(shù)據(jù)和高光譜數(shù)據(jù)的特點(diǎn),充分利用基于控制點(diǎn)的圖像配準(zhǔn)方法、基于特征點(diǎn)的圖像配準(zhǔn)方法和基于互信息的配準(zhǔn)方法,實現(xiàn)了基于控制點(diǎn)和特征互信息的地面LiDAR點(diǎn)云數(shù)據(jù)與高光譜圖像的混合配準(zhǔn),最后協(xié)同地面LiDAR與高光譜圖像估計單棵樹的葉面積密度。
[Abstract]:With the rapid development of remote sensing technology, traditional optical remote sensing has been unable to meet the precision requirements of spatial and spectral information in forest resource survey. In recent years, because of the high precision three-dimensional spatial information and rich spectral information of LiDAR, both of them have been widely used in various industries. Under the condition that the spatial information and spectral information of LiDAR data and hyperspectral data are mutually compensated, it is more advantageous to improve the retrieval accuracy of tree parameters by cooperating the two kinds of data. In this paper, the 3D spatial and spectral information of a single tree is obtained by using Leica Scanstation C10, a 3D laser scanner, and the imaging spectrometer SOC710. In this paper, we have done the following work: 1) summarize the research status of LiDAR data and hyperspectral data at home and abroad. In this paper, the present situation of forestry research of two kinds of cooperative data and the research status of LiDAR and optical remote sensing data registration method are introduced in detail) aiming at the discreteness and huge amount of data of ground LiDAR point cloud data. In this paper, the indexing mechanism of LiDAR point cloud data based on quadtree is established. Aiming at the different imaging modes of ground LiDAR data and hyperspectral data, the point cloud data of LiDAR is discrete three-dimensional information, and the hyperspectral data is two-dimensional spectral image. In this paper, we simulate the camera imaging mode and transform the LiDAR point cloud data into two dimensional images. Aiming at the difference between the intensity information of LiDAR and the gray level information of hyperspectral data, the registration method based on control point, feature and mutual information is studied in this paper. A registration method combining control point and feature mutual information is proposed. The rough registration of LiDAR image and hyperspectral image is realized by using the registration method based on control point, and then the coarse registration parameters are used as initialization parameters searched by Powell algorithm. Using feature mutual information as similarity measure, the method realizes fine registration of LiDAR image and hyperspectral image. According to the difference of the spectral curves of the leaves and branches of the LiDAR data, the spectral information of the single tree LiDAR data can be segmented according to the difference of the spectral curves of the leaves and branches of the tree, and the spectral information of the hyperspectral data can be used to realize the segmentation of the branches and the leaves of the single tree, which is based on the geometric information. Finally, the leaf area density estimation based on the VCP(Voxel-based Canopy algorithm is realized by using the leaf LiDAR point cloud data. In a word, according to the characteristics of ground LiDAR point cloud data and hyperspectral data, this paper makes full use of the image registration method based on control point, the image registration method based on feature point and the registration method based on mutual information. The mixed registration of ground LiDAR point cloud data and hyperspectral images based on mutual information of control points and features is realized. Finally, the leaf area density of a single tree is estimated in collaboration with ground LiDAR and hyperspectral images.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN957.52

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 黃先鋒,陶闖,江萬壽,龔健雅;機(jī)載激光雷達(dá)點(diǎn)云數(shù)據(jù)的實時渲染[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2005年11期

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本文編號：1974775