本文選題：高光譜圖像 + 混合像元分解　； 參考：《長(zhǎng)安大學(xué)》2013年碩士論文

【摘要】：隨著高分辨率遙感衛(wèi)星的發(fā)射，海量遙感影像獲得，但是實(shí)時(shí)分析處理能力欠缺，其中最欠缺的是各種有效的算法。對(duì)于高光譜遙感影像，急需要解決的一個(gè)問題便是混合像元分解，其直接制約了影像的實(shí)際應(yīng)用。但僅僅解決混合像元問題是不夠的，其只能獲得端元豐度圖，不能確定亞象元的空間位置分布，因此還需要解決亞象元定位問題。至此，高光譜影像才能真正得到普遍應(yīng)用。 本文闡述了高光譜遙感的基本概念；研究了高光譜圖像的特點(diǎn)；總結(jié)了現(xiàn)有的混合像元分解技術(shù)，并著重分析研究了幾種常見的端元提取算法；同時(shí)也總結(jié)了現(xiàn)有的亞象元定位技術(shù)，并用程序?qū)崿F(xiàn)了一種亞象元定位算法。最后，通過總結(jié)研究現(xiàn)有的高光譜混合像元分解技術(shù)，提出了基于頂點(diǎn)成分分析的端元優(yōu)化算法。 頂點(diǎn)成分分析算法（VCA）的本質(zhì)是一種純數(shù)學(xué)方法，具有良好的理論基礎(chǔ)，取得了良好效果。但是VCA算法具有三方面缺陷：沒有考慮圖像空間信息，對(duì)于噪聲較大的高光譜圖像其有效性可能會(huì)降低；算法需要預(yù)先確定端元數(shù)目，但是預(yù)先確定正確的端元數(shù)目很困難；VCA算法多次運(yùn)行結(jié)果不穩(wěn)定。針對(duì)以上問題，本文提出改進(jìn)VCA的算法(Improve-VCA)，其指定候選端元數(shù)，用候選端元區(qū)間的迭代計(jì)算、結(jié)合圖像空間信息以及病態(tài)矩陣規(guī)避的優(yōu)化機(jī)制，實(shí)現(xiàn)了VCA算法的改進(jìn)。 為定量評(píng)價(jià)算法，充分印證本文算法思想的正確性與有效性，模擬生成了高光譜數(shù)據(jù)，對(duì)常用的端元提取算法(N-FINDR、SGA、VCA、ACEEHIIU)及本文算法(Improve-VCA)進(jìn)行同條件對(duì)比實(shí)驗(yàn)與檢驗(yàn)，并進(jìn)行嚴(yán)格的定量分析和說明。定量研究指標(biāo)采用平均光譜角mSAD、平均光譜信息散度mSID、組分平均夾角mAAD以及豐度反演得到的組分總體均方根誤差mARMSE進(jìn)行綜合評(píng)價(jià)和分析。通過對(duì)比分析可知，本文算法能夠自動(dòng)確定端元數(shù)目，準(zhǔn)確提取端元，在很多方面可以和常用的端元提取算法相媲美，甚至在某些方面更優(yōu)于常用的端元提取算法。 最后，本文用代碼實(shí)現(xiàn)了一種基于正則MAP模型的高光譜影像亞象元定位算法，對(duì)亞象元定位進(jìn)行了初探，為以后的研究奠定了基礎(chǔ)。
[Abstract]:With the launch of high-resolution remote sensing satellite, massive remote sensing images are obtained, but the ability of real-time analysis and processing is lacking, among which various effective algorithms are the most deficient. For hyperspectral remote sensing images, a problem that needs to be solved urgently is mixed pixel decomposition, which directly restricts the practical application of images. However, it is not enough to solve the mixed pixel problem. It can only obtain the endmember abundance graph and can not determine the spatial distribution of the sub-pixel, so it is necessary to solve the sub-pixel localization problem. At this point, hyperspectral images can really be widely used. This paper describes the basic concept of hyperspectral remote sensing, studies the characteristics of hyperspectral images, summarizes the existing mixed pixel decomposition techniques, and focuses on the analysis of several common End-element extraction algorithms. At the same time, the existing sub-pixel localization technology is summarized, and a sub-pixel localization algorithm is implemented by program. Finally, by summarizing and studying the existing hyperspectral mixed pixel decomposition techniques, an end element optimization algorithm based on vertex component analysis is proposed. The essence of Vertex component Analysis (VCA) is a pure mathematical method with good theoretical foundation and good results. However, the VCA algorithm has three defects: it does not consider the spatial information of the image, and its validity may be reduced for the noisy hyperspectral image, and the algorithm needs to determine the number of endpoints in advance. However, it is difficult to determine the correct number of endpoints in advance. In order to solve the above problems, this paper proposes an improved VCA algorithm, which specifies the number of candidate endpoints, and implements the improvement of VCA algorithm by the iterative calculation of candidate endmember interval, the combination of image spatial information and the optimization mechanism of ill-conditioned matrix evasion. In order to evaluate the algorithm quantitatively and fully verify the correctness and validity of the algorithm in this paper, the hyperspectral data are generated by simulation. Experiments and tests are carried out on the same conditions for the common endmember extraction algorithm (N-FINDRN SGASGAACEEEHIIUU) and the improved prove-VCAA algorithm in this paper. And carries on the strict quantitative analysis and the explanation. The quantitative study indexes were evaluated and analyzed by means of average spectral angle mSAD, average spectral information divergence mSID, component mean inclusion angle mAAD and mARMSE of root mean square error obtained by abundance inversion. Through comparison and analysis, we can see that the algorithm can automatically determine the number of end elements, accurately extract the end elements, in many ways can be compared with the commonly used end element extraction algorithm, and even better than the common end element extraction algorithm in some aspects. Finally, this paper implements a hyperspectral image sub-pixel localization algorithm based on canonical MAP model, and makes a preliminary study of sub-pixel localization, which lays a foundation for future research.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TP751;P237

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