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  本文選題：混合像元 + 端元提取��； 參考：《中南大學》2013年碩士論文

【摘要】：高光譜遙感技術(shù)的核心依據(jù)是地物對電磁波的發(fā)射、吸收與反射特性。高光譜遙感就是最大限度地提高光譜分辨率,以光譜差異為核心依據(jù)實現(xiàn)地物精細分類的新興遙感技術(shù)。正因為其光譜分辨率高、波段數(shù)多、數(shù)據(jù)量大的特點,高光譜遙感數(shù)據(jù)的空間分辨率往往不夠高�；旌舷裨毡榇嬖谟诟吖庾V遙感影像中,一定程度上阻礙了分類精度的提高�；旌舷裨纸馐沁M一步提高高光譜遙感分類精度必須面對的問題�；旌舷裨纸夥譃槎嗽崛『凸庾V解混兩個過程,本文著重對端元提取進行了研究,并將提取的端元應用到光譜解混模型中,對光譜解混結(jié)果進行了對比分析。 高光譜遙感影像數(shù)據(jù)中的像元在光譜空間中呈凸面幾何分布,而端元就位于凸面幾何體的頂點位置,這是端元提取的理論基礎。本文介紹了現(xiàn)有的光譜混合模型和端元提取方法,并指出了每種模型方法的優(yōu)缺點,并用PPI算法進行了端元提取,利用線性波譜分離技術(shù)進行了完整的光譜解混。 本文以最大距離法為基礎,針對該方法在提取初始端元上的不足,深入研究端元像元的光譜特性,將其轉(zhuǎn)化為數(shù)學中的坐標進行分析,發(fā)現(xiàn)了端元具有的坐標特性,從而得出一種快速識別初始端元的方法,提取了五種礦物的端元波譜。用純像元指數(shù)和波段分析工具對端元波譜進行了驗證,并進行了光譜解混。 同時,考慮了遙感影像在獲取和處理過程中產(chǎn)生的誤差,引入距離閾值概念,旨在提取與端元距離小于距離閾值的像元組成一組樣本,求這些樣本的平均光譜來代替原始端元。將這些平均光譜與USGS光譜庫中對應礦物波譜進行匹配,證明了平均光譜的相似度較單一像元的端元有較大的提高,并進行了光譜解混。把三種方法提取的端元以及對應的光譜解混結(jié)果進行了對比分析,實驗表明使用平均光譜能提高提取的端元的可靠性以及光譜解混的精度。
[Abstract]:The core of hyperspectral remote sensing technology is the emission, absorption and reflection characteristics of electromagnetic waves. Hyperspectral remote sensing is a new remote sensing technology which can maximize spectral resolution and realize fine classification of ground objects based on spectral difference. The spatial resolution of hyperspectral remote sensing data is often not high enough because of its high spectral resolution, large number of bands and large amount of data. Mixed pixels are widely used in hyperspectral remote sensing images, which to some extent hinder the improvement of classification accuracy. Mixed pixel decomposition is a problem that must be faced to improve the classification accuracy of hyperspectral remote sensing. The mixed pixel decomposition is divided into two processes: End-component extraction and spectral unmixing. In this paper, the End-component extraction is studied and applied to the spectral de-mixing model, and the results of spectral unmixing are compared and analyzed. The pixel in hyperspectral remote sensing image is geometric distribution of convex surface in spectral space, and the endmember is located at the vertex position of convex geometry, which is the theoretical basis of endmember extraction. In this paper, the existing spectral mixing models and end-component extraction methods are introduced, and the advantages and disadvantages of each method are pointed out. The end component extraction is carried out using PPI algorithm and the complete spectral unmixing is carried out by using linear spectral separation technique. In this paper, based on the maximum distance method, the spectral characteristics of the end element pixel are deeply studied in order to solve the shortcomings of the method in extracting the initial end element, and the coordinate characteristic of the end element is found out by transforming it into the coordinate in mathematics. Thus, a fast method for identifying the initial end components is obtained, and the end component spectra of five minerals are extracted. The end-element spectrum was verified by pure pixel exponent and band analysis tool, and the spectral unmixing was carried out. At the same time, taking into account the errors in the acquisition and processing of remote sensing images, the concept of distance threshold is introduced in order to extract a group of samples from pixels whose distance is less than the distance threshold, and to find out the average spectrum of these samples to replace the original endpoints. By matching these spectra with the corresponding mineral spectra in the USGS spectral library, it is proved that the similarity of the average spectra is much higher than that of the end elements of a single pixel, and the spectral unmixing is carried out. The results of the three methods are compared and analyzed. The experimental results show that the average spectrum can improve the reliability and the precision of spectral unmixing.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P237

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