【摘要】：在高光譜像元解混應(yīng)用中,好的端元光譜矩陣初始化方法對(duì)于提高盲信號(hào)分解精度具有重要意義。針對(duì)空間分辨率較高的高光譜數(shù)據(jù),提出了一種新的面向非負(fù)矩陣分解(non-negative matrix factorization,NMF)的初始化方法。該方法通過(guò)計(jì)算像元在譜線形狀和信息量差異等方面的參數(shù),利用像元譜線峭度、KL散度和光譜角等參量,從眾多混合像元中識(shí)別出純像元;并分辨出不同類(lèi)型純像元(或類(lèi)純像元)之間的差別,從中選擇最適合代表每一類(lèi)型端元的純像元(或類(lèi)純像元)作為算法的初值像元,完成端元矩陣的初始化。將此方法分別用于模擬數(shù)據(jù)和真實(shí)數(shù)據(jù)的實(shí)驗(yàn)結(jié)果表明,該方法能夠明顯提高高光譜混合數(shù)據(jù)的NMF精度,相比其他常用初始化方法具有更好的效果。
[Abstract]:In the application of high-spectral image element, a good end-element spectral matrix initialization method is of great significance to improve the accuracy of blind signal decomposition. A new method for initializing non-negative matrix factorization (NMF) is proposed for high spectral data with higher spatial resolution. The method comprises the following steps of: calculating a parameter of an image element in a spectral line shape and an information quantity difference and the like, identifying a pure image element from a plurality of mixed image elements by using a parameter such as a spectral line similarity, a KL divergence angle and a spectral angle, and distinguishing the difference between different types of pure image elements (or quasi-pure image elements), A pure image element (or quasi-image element), which is most suitable for representing each type of end element, is selected as the initial value image element of the algorithm, and the initialization of the end element matrix is completed. The experimental results show that the method can obviously improve the NMF accuracy of the high-spectral mixed data, and has a better effect than the other common initialization methods.
【作者單位】： 南陽(yáng)理工學(xué)院數(shù)學(xué)與統(tǒng)計(jì)學(xué)院;南陽(yáng)理工學(xué)院經(jīng)濟(jì)管理學(xué)院;
【基金】：河南省高等學(xué)校重點(diǎn)科研項(xiàng)目“Smith正規(guī)型在有限域上有理點(diǎn)個(gè)數(shù)中的應(yīng)用”(編號(hào):17A110010)資助
【分類(lèi)號(hào)】：TP391.41

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