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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Hyperspectral Data Geometry-Based Estimation of Number of Endmembers Using $p$-Norm-Based Pure Pixel Identification Algorithm
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Hyperspectral Data Geometry-Based Estimation of Number of Endmembers Using $p$-Norm-Based Pure Pixel Identification Algorithm

机译：使用基于$ p $ -Norm的纯像素识别算法基于高光谱数据几何的末端成员数量估计

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摘要

Hyperspectral endmember extraction is a process to estimate endmember signatures from the hyperspectral observations, in an attempt to study the underlying mineral composition of a landscape. However, estimating the number of endmembers, which is usually assumed to be known a priori in most endmember estimation algorithms (EEAs), still remains a challenging task. In this paper, assuming hyperspectral linear mixing model, we propose a hyperspectral data geometry-based approach for estimating the number of endmembers by utilizing successive endmember estimation strategy of an EEA. The approach is fulfilled by two novel algorithms, namely geometry-based estimation of number of endmembers—convex hull (GENE-CH) algorithm and affine hull (GENE-AH) algorithm. The GENE-CH and GENE-AH algorithms are based on the fact that all the observed pixel vectors lie in the convex hull and affine hull of the endmember signatures, respectively. The proposed GENE algorithms estimate the number of endmembers by using the Neyman–Pearson hypothesis testing over the endmember estimates provided by a successive EEA until the estimate of the number of endmembers is obtained. Since the estimation accuracies of the proposed GENE algorithms depend on the performance of the EEA used, a reliable, reproducible, and successive EEA, called $p$-norm-based pure pixel identification (TRI-P) algorithm is then proposed. The performance of the proposed TRI-P algorithm, and the estimation accuracies of the GENE algorithms are demonstrated through Monte Carlo simulations. Finally, the proposed GENE and TRI-P algorithms are applied to real AVIRIS hyperspectral data obtained over the Cuprite mining site, Nevada, and some conclusions and future directions are provided.
机译：高光谱最终成员提取是一种从高光谱观测值估计最终成员特征的过程，旨在研究景观的潜在矿物成分。但是，在大多数终端成员估计算法（EEA）中通常假定先验已知的估计终端成员的数量仍然是一项艰巨的任务。在本文中，假设采用高光谱线性混合模型，我们提出了一种基于高光谱数据几何的方法，以利用EEA的连续端成员估计策略来估计端成员的数量。该方法由两种新颖的算法实现，即基于几何的末端成员数量估计-凸包（GENE-CH）算法和仿射包（GENE-AH）算法。 GENE-CH和GENE-AH算法基于以下事实：所有观察到的像素向量分别位于端成员签名的凸包和仿射包中。提出的GENE算法通过对连续EEA提供的最终成员估计值使用Neyman-Pearson假设检验来估计最终成员数量，直到获得最终成员数量的估计值为止。由于所提出的GENE算法的估计精度取决于所用EEA的性能，因此提出了一种可靠的，可重复的和连续的EEA，称为基于$ p-norm的纯像素识别（TRI-P）算法。通过蒙特卡洛仿真证明了所提出的TRI-P算法的性能以及GENE算法的估计精度。最后，将提出的GENE和TRI-P算法应用于在内华达州Cuprite矿场获得的真实AVIRIS高光谱数据，并提供了一些结论和未来的方向。

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