本文選題：經(jīng)驗(yàn)?zāi)B(tài)分解(EMD) + 特征模態(tài)函數(shù)(IMF)��； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：高光譜遙感具有高的光譜分辨率,能為像元提供幾乎連續(xù)的波譜曲線,高光譜遙感具備反演地物細(xì)節(jié)的能力。高光譜數(shù)據(jù)是復(fù)雜的非線性非平穩(wěn)信號(hào),經(jīng)驗(yàn)?zāi)B(tài)分解(Empirical Mode Decomposition,簡(jiǎn)稱EMD)是一種新的自適應(yīng)時(shí)頻分析方法,經(jīng)EMD分解后的各個(gè)特征模態(tài)函數(shù)能突出原始信號(hào)的局部特征,從而更加方便地對(duì)非線性非平穩(wěn)信號(hào)進(jìn)行處理與分析。因此,本文嘗試將EMD方法應(yīng)用于復(fù)雜的高光譜數(shù)據(jù)中。本文主要工作有:(1)仔細(xì)探究常見(jiàn)時(shí)頻分析方法及其局限性,深入研究經(jīng)驗(yàn)?zāi)B(tài)分解的基本原理以及Huang所提出的創(chuàng)新性概念--特征模態(tài)函數(shù),對(duì)EMD算法的分解過(guò)程進(jìn)行了詳盡的論述,以及該算法自身所具備的優(yōu)越性能,指出EMD分解過(guò)程中存在的問(wèn)題以及針對(duì)此提出的研究方向。(2)在高光譜數(shù)據(jù)的獲取過(guò)程中,由于受到各種因素的影響,產(chǎn)生大量噪聲,從而影響數(shù)據(jù)的最終分析結(jié)果�；诖�,根據(jù)隨機(jī)噪聲的特性,結(jié)合高光譜數(shù)據(jù)的變化特征,提出了一種基于自相關(guān)函數(shù)特性的EMD去噪方法。通過(guò)仿真實(shí)驗(yàn)結(jié)果表明,含噪信號(hào)經(jīng)EMD分解后,噪聲主要集中在高頻IMF分量中,將對(duì)應(yīng)含噪IMF分量進(jìn)行濾波后,與剩余IMF分量進(jìn)行重構(gòu),噪聲與信號(hào)進(jìn)行有效地分離,從而實(shí)現(xiàn)對(duì)高光譜數(shù)據(jù)濾波降噪的目的,通過(guò)比較,此方法優(yōu)于小波去噪。同時(shí),將該去噪方法應(yīng)用于野外實(shí)測(cè)的巖心高光譜數(shù)據(jù),結(jié)果顯示,去噪效果良好。(3)由于高光譜數(shù)據(jù)的大數(shù)據(jù)量以及巖心高光譜信號(hào)具有多變性的特點(diǎn),利用傳統(tǒng)的SAM、SCA等方法去識(shí)別高光譜數(shù)據(jù)特征并不適用。將信號(hào)進(jìn)行EMD分解,使信號(hào)分析真正實(shí)現(xiàn)了時(shí)頻局部化。經(jīng)EMD分解后的各個(gè)固有模態(tài)函數(shù)突出了原始信號(hào)的局部特征,以局部反映總體。本文嘗試將EMD應(yīng)用在高光譜數(shù)據(jù)的特征提取中,從而尋找一種能夠方便對(duì)近似光譜識(shí)別的方法,并確保此方法是有效可行的。以黃銅礦與黃鐵礦為實(shí)例,分解后,得到局部特征,經(jīng)過(guò)對(duì)比分析特征模態(tài)函數(shù),實(shí)現(xiàn)了對(duì)黃銅礦與黃鐵礦的識(shí)別。
[Abstract]:Hyperspectral remote sensing has high spectral resolution and can provide almost continuous spectral curves for pixels. Hyperspectral remote sensing is capable of retrieving the details of ground objects. Hyperspectral data is a complex nonlinear non-stationary signal. Empirical Mode decomposition (EMD) is a new adaptive time-frequency analysis method. Each characteristic modal function after EMD decomposition can highlight the local characteristics of the original signal. Therefore, it is more convenient to process and analyze nonlinear nonstationary signals. Therefore, this paper attempts to apply EMD method to complex hyperspectral data. The main work of this paper is to explore the common time-frequency analysis methods and their limitations, to study the basic principle of empirical mode decomposition and the innovative concept proposed by Huang, which is called eigenmode function. In this paper, the decomposition process of EMD algorithm is discussed in detail, and the superior performance of the algorithm itself is discussed. It is pointed out that the problems existing in the process of EMD decomposition and the research direction. Because of the influence of various factors, a great deal of noise is produced, which affects the final analysis result of the data. Based on this, according to the characteristics of random noise and the variation of hyperspectral data, a EMD denoising method based on autocorrelation function is proposed. The simulation results show that the noise is mainly concentrated in the high frequency IMF component after the noise signal is decomposed by EMD. After filtering the corresponding noisy IMF component, the noise is reconstructed from the remaining IMF component, and the noise is effectively separated from the signal. In order to achieve the purpose of noise reduction of hyperspectral data filtering, this method is better than wavelet de-noising by comparison. At the same time, the method is applied to the field measured core hyperspectral data. The results show that the denoising effect is good. Traditional methods such as SAMU SCA are not suitable to identify hyperspectral data features. The signal is decomposed by EMD, and the time-frequency localization is realized in the signal analysis. Each inherent mode function after EMD decomposition highlights the local characteristics of the original signal and reflects the population locally. This paper attempts to apply EMD to feature extraction of hyperspectral data in order to find a method that can easily identify approximate spectra and ensure that this method is effective and feasible. Taking chalcopyrite and pyrite as examples, the local features are obtained after decomposition, and the identification of chalcopyrite and pyrite is realized by comparing and analyzing the characteristic modal functions.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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