Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)
Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE)
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DOI：
10.1109/igarss.2005.1525342
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发表时间：
2005-07
期刊：
影响因子：
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通讯作者：
T. Han;D. Goodenough
中科院分区：
文献类型：
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作者：
T. Han;D. Goodenough
特征提取是高光谱遥感数据信息提取必不可少的预处理步骤。在本文中，我们介绍了一种非线性特征提取算法，称为局部线性嵌入（LLE），并定制它的高光谱遥感应用。与基于数据协方差矩阵特征向量的线性特征提取算法不同，LLE在约简空间中保持了高光谱数据的局部拓扑结构。这种保存对于保持输入数据的非线性特性是重要的，这有利于进一步的信息提取。为了研究LLE在高光谱遥感应用中的有效性，从空间信息保存和纯像元识别两个方面对LLE进行了研究。初步结果表明，该方法在空间信息保存方面优于主成分分析。此外，通过散点图，它在纯像素识别上超过了PCA。索引条款-特征提取，高光谱，降维，主成分分析，局部线性嵌入，信息量。
Feature extraction is an indispensable preprocessing step for information extraction from hyperspectral remote sensing data. In this paper, we introduce a nonlinear feature extraction algorithm, called Locally Linear Embedding (LLE), and customize it for hyperspectral remote sensing applications. Unlike the linear feature extraction algorithms based on eigenvectors of data covariance matrix, LLE preserves local topology of hyperspectral data in the reduced space. This preservation is important to maintain the nonlinear properties of the input data that benefits further information extraction. To investigate its effectiveness for hyperspectral remote sensing applications, LLE was examined in terms of spatial information preservation and pure pixel identification. The preliminary result of this study demonstrated that it compared favorably with PCA on spatial information preservation. In addition, it exceeded PCA on pure pixel identification through scatter plots. Index Terms – feature extraction, hyperspectral, dimensionality reduction, Principal Component Analysis, Locally Linear Embedding, information content.