A comparison study of dimension estimation algorithms

摘要

The inherent dimension of hyperspectral data is commonly estimated for the purpose of dimension reduction.However, the dimension estimate itself may be a useful measure for extracting information about hyperspectraldata, including scene content, complexity, and clutter. There are many ways to estimate the inherent dimensionof data, each measuring the data in a different way. This paper compares a group of dimension estimation metricson a variety of data, both full scene and individual material regions, to determine the relationship between thedifferent estimates and what features each method is measuring when applied to complex data.