A new dimensionality analysis algorithm for hyperspectral imagery

In the procedure of hyperspectral data dimensionality reduction (DR), intrinsic dimensionality (ID) of high-dimensional hyperspectral data is normally obtained through the linear dimensionality analysis methods. This article applies a kind of unsupervised learning method, manifold learning method, to the dimensionality analysis for hyperspectral data and gives a manifold-learning-based algorithm for dimensionality analysis of hyperspectral data. The experiments use ISOMAP, LLE, LE and LTSA algorithms to estimate the intrinsic dimensionality of hyperspectral simulated data and real data, get the two-dimension manifold figures of high-dimensional data and discuss the advantages and disadvantages of these algorithms in hyperspectral dimensionality analysis.