Low-dimension Manifold Feature Extraction of Hyperspectral Imagery Using Dimension Reduction with Isomap

Nonlinear dimensionality reduction on hyperspectral imagery can be achieved using the isometric mapping(Isomap) method.We explore the spectral interpretations of Isomap manifold coordinates through observing and comparing the changing trends between manifold coordinates and spectral signatures.The study aims to extract desired low-dimension manifold features from Isomap manifold maps.Two cases study are designed to testify the capacity of manifold maps in extracting low-dimension manifold features.The results show that the Isomap manifold maps can be used to extract low-dimension manifold features.Moreover,the results prove that the spectral interpretations of manifold coordinates are feasible.This will be helpful for the applications of the Isomap method in hyperspectral imagery fields.