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Spectral Mixture Analysis: Linear and Semi-parametric Full and Iterated Partial Unmixing in Multi- and Hyperspectral Image Data

机译:光谱混合分析:在多和超光谱图像数据中线性和半参数完全和迭代部分解混

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摘要

As a supplement or an alternative to classification of hyperspectral image data linear and semi-parametric mixture models are considered in order to obtain estimates of abundance of each class or end-member in pixels with mixed membership. Full unmixing based on both ordinary least squares (OLS) and non-negative least squares (NNLS), and the partial unmixing methods orthogonal subspace projection (OSP), constrained energy minimization (CEM) and an eigenvalue formulation alternative are dealt with. The solution to the eigenvalue formulation alternative proves to be identical to the CEM solution. The matrix inversion involved in CEM can be avoided by working on (a subset of) orthogonally transformed data such as signal maximum autocorrelation factors, MAFs, or signal minimum noise fractions, MNFs. This will also cause the partial unmixing result to be independent of the noise isolated in the MAF/MNFs not included in the analysis. CEM and the eigenvalue formulation alternative enable us to perform partial unmixing when we know one desired end-member spectrum only and not the full set of end-member spectra. This is an advantage over full unmixing and OSP. The eigenvalue formulation of CEM inspires us to suggest an iterated CEM scheme. Also the target constrained interference minimized filter (TCIMF) is described. Spectral angle mapping (SAM) is briefly described. Finally, semi-parametric unmixing (SPU) based on a combined linear and additive model with a non-linear, smooth function to represent end-member spectra unaccounted for is introduced. An example with two generated bands shows that both full unmixing, the CEM, the iterated CEM and TCIMF methods perform well. A case study with a 30 bands subset of AVIRIS data shows the utility of full unmixing. SAM, CEM and iterated CEM to more realistic data. Iterated CEM seems to suppress noise better than CEM. A study with AVIRIS spectra generated from real spectra shows (1) that ordinary least squares in this case with one unknown spectrum performs better than non-negative least squares, and (2) that although not fully satisfactory the semi-parametric model gives better estimates of end-member abundances than the linear model.
机译:作为一种补充或替代对高光谱图像数据线性和半参数混合模型的分类,以便获得具有混合成员资格的像素的每个类或最终成员的丰度的估计。基于普通最小二乘(OLS)和非负数最小二乘(NNL)的完全解混,以及部分解密方法正交子空间投影(OSP),约束能量最小化(CEM)和特征值配方替代。对特征值制剂的替代方案的溶液证明与CEM解决方案相同。 CEM中涉及的矩阵反演可以通过工作(诸如信号最大自相关因子,MAFS或信号最小噪声分数,MNFS)的正交变换数据(例如)正交变换的数据(诸如)正交变换的数据而避免。这也将导致部分解密结果与不包括在分析中不包括的MAF / MNFS中隔离的噪声无关。 CEM和特征值配方替代方案使我们能够在我们知道一个所需的最终成员谱时执行部分解混,而不是全套最终成员光谱。这是完全解密和OSP的优势。 CEM的特征值制定激励我们建议迭代的CEM计划。还描述了目标约束干扰最小化滤波器(TCIMF)。简要描述光谱角映射(SAM)。最后,基于组合线性和附加模型的半导体解密(SPU)引入了未占用的非线性平滑函数来表示未占用的最终成员光谱。具有两个生成频带的示例显示全部解密,CEM,迭代CEM和TCIMF方法表现良好。具有30个Aviris数据子集的案例研究显示了完全解密的效用。 SAM,CEM和迭代CEM到更现实的数据。迭代CEM似乎抑制了比CEM更好的噪音。来自真实光谱产生的Aviris光谱的研究(1)在这种情况下普通的最小二乘性比非负数最小二乘性更好地执行,并且(2)虽然没有完全令人满意,半参数模型提供更好的估计末端成员丰富而不是线性模型。

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