本文選題：高光譜圖像解混　切入點：全變分模型　出處：《電子科技大學》2017年碩士論文

【摘要】：高光譜成像是將成像技術(shù)與光譜技術(shù)相結(jié)合的技術(shù),是遙感應用中一個快速發(fā)展的領(lǐng)域。高光譜圖像在軍事目標辨別、遠程控制、生物醫(yī)學、食品安全以及環(huán)境監(jiān)測等領(lǐng)域都有重要應用。但由于高光譜成像光譜儀空間分辨率較低,使得每個高光譜像元可能由多種不同物質(zhì)的光譜混合構(gòu)成,因此混合像元廣泛存在于高光譜圖像中。混合像元導致科研實踐中一些應用分類不準確,因此對混合像元進行分解是高光譜遙感應用亟待解決的核心問題。本文中首先介紹了兩種光譜混合模型:線性和非線性光譜混合模型。線性模型假設(shè)觀察到的像元信號是所有的純光譜信號的線性組合。與之相反,非線性模型則考慮到多種物質(zhì)反射光之間的物理相互影響。其次,本文對高光譜圖像解混的幾種經(jīng)典模型進行介紹。在這些模型中詳細介紹了本文的對比模型全變分模型(SUnSAL-TV),該模型利用高光譜圖像空間關(guān)系構(gòu)建了對端元豐度的正則項,這使高光譜圖像解混問題在數(shù)值結(jié)果和視覺效果上都有較大提升。但全變分模型的缺點是解混后豐度圖中原平滑區(qū)域中伴有階梯效應現(xiàn)象,視覺效果欠佳。本文采用重疊組稀疏全變分作為端元豐度正則項,并采用交替方向乘子法對模型進行求解,將原問題轉(zhuǎn)化為一系列較易求解的子問題,進而得到原問題的全局解。在應用交替方向乘子法進行求解過程中,關(guān)于梯度域重疊組稀疏的子問題采用采用優(yōu)化最小化方法進行求解。通過合成數(shù)據(jù)和真實數(shù)據(jù)的實驗證明,采用本文提出的新方法處理后圖像視覺效果和數(shù)值效果相比SUnSAL-TV方法有明顯提升,并且可以有效減弱SUnSAL-TV模型的階梯效應,使處理后豐度圖更加平滑,視覺效果更佳。
[Abstract]:Hyperspectral imaging, which combines imaging technology with spectral technology, is a rapidly developing field in remote sensing applications.Hyperspectral images have important applications in military target identification, remote control, biomedicine, food safety and environmental monitoring.However, because of the low spatial resolution of hyperspectral imaging spectrometer, each hyperspectral pixel may be composed of multiple spectral mixtures of different substances, so mixed pixels are widely used in hyperspectral images.Mixed pixels lead to inaccurate classification of some applications in scientific research practice, so decomposition of mixed pixels is the core problem to be solved urgently in hyperspectral remote sensing applications.In this paper, we first introduce two kinds of spectral mixing models: linear and nonlinear spectral mixing models.The linear model assumes that the observed pixel signal is a linear combination of all pure spectral signals.In contrast, the nonlinear model takes into account the physical interaction between the reflected light of a variety of substances.Secondly, several classical models of hyperspectral image unmixing are introduced in this paper.In these models, the contrasting model, total variational model, SUnSAL-TVN, is introduced in detail. By using the spatial relation of hyperspectral images, the canonical terms of opposite-end Yuan Feng degree are constructed.This improves the numerical results and visual effects of hyperspectral image demultiplexing.However, the disadvantage of the total variational model is that there is a step effect in the original smooth region in the unmixed abundance map, and the visual effect is not good.In this paper, the sparse total variation is used as the regular term of abundance of the end element, and the alternating direction multiplier method is used to solve the model. The original problem is transformed into a series of subproblems which are easy to solve, and the global solution of the original problem is obtained.In the process of solving the problem using alternating direction multiplier method, the optimal minimization method is used to solve the sparse subproblem of overlapped groups in gradient domain.The experimental results of synthetic data and real data show that the visual effect and numerical effect of the new method proposed in this paper are much better than that of SUnSAL-TV method, and the step effect of SUnSAL-TV model can be effectively reduced.After processing, the abundance map is smoother and the visual effect is better.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

【參考文獻】

相關(guān)期刊論文 前1條

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本文編號：1705358