Nonlinear feature extraction of hyperspectral data based on Locally Linear Embedding (LLE)

Feature extraction is an indispensable preprocessing step for information extraction from hyperspectral remote sensing data. In this paper, we introduce at nonlinear feature extraction algorithm, called Locally Linear Embedding (LLE), and customize it for hyperspectral remote sensing applications. Unlike the linear feature extraction algorithims 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.