【摘要】：機(jī)載激光雷達(dá)(Airborne Laser Scanning,ALS),是綜合了多個(gè)子系統(tǒng)于一體的主動(dòng)式遙感器,具有實(shí)時(shí)快速、精度高、穿透性強(qiáng)等優(yōu)點(diǎn)。其中小光斑全波形系統(tǒng)照射到地面的光斑較小(光斑直徑在1m以內(nèi)),相比于傳統(tǒng)的大光斑全波形系統(tǒng)采集地物信息更為精細(xì)、精度更高,在農(nóng)業(yè)、林業(yè)、電力、軍事等領(lǐng)域具有較大應(yīng)用價(jià)值。由于其較為精細(xì)的采集特性,小光斑ALS全波形數(shù)據(jù)中存在較多地物回波疊加,同時(shí)又存在多種復(fù)雜噪聲的影響,因此對(duì)小光斑ALS全波形數(shù)據(jù)處理方法的要求較高,通常包括預(yù)處理、波形分解、組分信息(三維點(diǎn)云、強(qiáng)度、波寬等)解算等步驟,其中難點(diǎn)在于預(yù)處理和波形分解。在目前廣泛使用的小光斑ALS全波形數(shù)據(jù)處理方法(高斯分解法和反卷積法)中,都存在預(yù)處理時(shí)去噪效果與波形特征保留平衡度不高,波形分解效果不佳的問題。本文通過對(duì)小光斑ALS全波形數(shù)據(jù)處理技術(shù)的研究,在波形數(shù)據(jù)處理中選用波形分解能力較強(qiáng)的反卷積法。其中,在穩(wěn)定性、邊緣探測(cè)能力和對(duì)低信噪比數(shù)據(jù)的處理能力方面表現(xiàn)較好的是基于RL算法的反卷積方法(后文簡(jiǎn)稱RL反卷積法),但這種方法仍存在著諸如收斂速度慢、噪聲放大等問題;并且隨著迭代次數(shù)的增加,波寬增大的波形在分解過程中容易出現(xiàn)虛假波峰,從而影響波形分解準(zhǔn)確度。針對(duì)存在的問題,本文在預(yù)處理和波形分解方面進(jìn)行了一些改進(jìn):(1)在預(yù)處理中,使用小波閾值去噪,并針對(duì)小光斑ALS全波形數(shù)據(jù)處理實(shí)際,對(duì)去噪?yún)?shù)進(jìn)行優(yōu)選,實(shí)現(xiàn)了在取得較好去噪效果,降低噪聲對(duì)后期波形分解影響的同時(shí),更多地保留波形特征;(2)為降低波寬對(duì)RL算法分解波形數(shù)據(jù)準(zhǔn)確度的影響,本文通過在收斂曲線上設(shè)置特定截止變化率的方法,實(shí)現(xiàn)了對(duì)RL反卷積法迭代次數(shù)的有效控制;(3)為提高RL算法的收斂速度,本文在提高點(diǎn)擴(kuò)散函數(shù)構(gòu)造質(zhì)量的同時(shí),引入基于二階矢量外推的加速RL算法,使RL算法分解小光斑ALS全波形數(shù)據(jù)的速度提高了近六倍。最后,從小光斑ALS全波形數(shù)據(jù)處理效果和目標(biāo)提取應(yīng)用兩個(gè)方面,驗(yàn)證了本文對(duì)RL反卷積法的改進(jìn)效果。
[Abstract]:Airborne lidar (Airborne Laser Scannings-ALS) is an active remote sensor which integrates many subsystems. It has the advantages of fast real-time, high precision, strong penetration and so on. The whole waveform system of small spot is smaller (the diameter of light spot is less than 1 m). Compared with the traditional full-waveform system of large light spot, it is more precise and accurate. In agriculture, forestry, electric power, Military and other fields have great application value. Because of its fine collection characteristics, there are many ground objects echo superposition in the ALS full waveform data of small spot, and at the same time, there are many kinds of complex noise, so the method of processing the whole waveform data of the small spot ALS is very high. It usually includes pretreatment, waveform decomposition, component information (3D point cloud, intensity, wave width, etc.), and so on. The difficulty lies in pretreatment and waveform decomposition. In the widely used ALS full waveform data processing methods (Gao Si decomposition method and deconvolution method), there are some problems such as the poor balance of denoising effect and waveform characteristic retention, and the poor waveform decomposition effect. In this paper, the ALS full waveform data processing technique with small spot is studied, and the deconvolution method with strong waveform decomposition ability is chosen in waveform data processing. Among them, in terms of stability, edge detection ability and processing ability of low signal-to-noise ratio data, the method based on RL algorithm (RL deconvolution method) is better, but this method still has some problems such as slow convergence rate. Noise amplification and so on, and with the increase of iteration times, the wave with the increase of wave width is prone to appear false wave peaks in the decomposition process, thus affecting the waveform decomposition accuracy. Aiming at the existing problems, some improvements are made in the aspects of pretreatment and waveform decomposition. (1) in the pretreatment, wavelet threshold is used to de-noise, and the denoising parameters are optimized according to the actual data processing of ALS with small spot. In order to reduce the effect of RL algorithm on the accuracy of waveform decomposition, we can achieve better denoising effect and reduce the effect of noise on waveform decomposition, while retaining more waveform characteristics. (2) in order to reduce the influence of wave width on the accuracy of RL algorithm to decompose waveform data, In this paper, the effective control of the iteration times of RL deconvolution method is realized by setting the specific cutoff rate on the convergence curve. (3) in order to improve the convergence speed of the RL algorithm, the construction quality of the point diffusion function is improved. An accelerated RL algorithm based on second-order vector extrapolation is introduced, which improves the speed of decomposition of ALS full waveform data of small spot by nearly six times. Finally, the improvement of RL deconvolution method is verified from two aspects: the processing effect of ALS full waveform data and the application of target extraction.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN958.98

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 黃濤;胡以華;;遮蔽目標(biāo)的激光雷達(dá)回波信息處理[J];光電技術(shù)應(yīng)用;2011年01期

2 賴旭東;秦楠楠;韓曉爽;王俊宏;侯文廣;;一種迭代的小光斑LiDAR波形分解方法[J];紅外與毫米波學(xué)報(bào);2013年04期

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本文編號(hào)：2179875