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A Comparison Study of Dimension Estimation Algorithms
June 2010
Ariel Schlamm, Digital Imaging and Remote Sensing Laboratory
Ronald G. Resmini, George Mason University, The MITRE Corporation
David Messinger, Digital Imaging and Remote Sensing Laboratory
William Basener, Rochester Institute of Technology
ABSTRACT
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 hyperspectral data, including scene content, complexity, and clutter. There are many ways to estimate the inherent dimension of data, each measuring the data in a different way. This paper compares a group of dimension estimation metrics on a variety of data, both full scene and individual material regions, to determine the relationship between the different estimates and what features each method is measuring when applied to complex data.
Additional Search Keywords
hyperspectral, inherent dimension, fractal dimension, correlation dimension, box counting
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