Landmark selection using homogeneity on nonlinear manifolds for unmixing hyperspectral data

Abstract

Spectral unmixing methods that exploit nonlinearity in hyperspectral data are promising, but face significant computational challenges. Global dimensionality reduction methods such as ISOMAP have significant computational overhea, while local methods such as Locally Linear Embedding (LLE), are computationally less demanding, but may not be robust. We propose a new landmark selection method for spectral unmixing that exploits spectral and spatial information, and embed it in LLE, resulting in a hybrid method whose structure shares characteristics with both global and local manifolds. Performance of the method is compared to that of several landmark selection methods in terms of mean of reconstruction error and corresponding variance, processing time, and visual inspection of the fully unmixed scene.