Automatic modeling of nonlinear signal source variations in hyperspectral data

Nonlinear effects in hyperspectral data complicate classification and other data analysis procedures. Transforming the data onto manifolds can help to improve the results while simultaneously reducing the dimensionality due to the high correlation among the spectral bands. Methods like ISOMAP or Locally Linear Embedding are not ideal when the data is degraded by noise. In this paper, a method is introduced to automatically generate support points for skeletonizing a high-dimensional point cloud. The skeleton is identified with multiple signal source variations of distinct materials and can be used to transform the data to improve further analysis procedures.