Image analysis – Pattern recognition – Classification
Reexamination Certificate
2005-10-04
2005-10-04
Hellner, Mark (Department: 3662)
Image analysis
Pattern recognition
Classification
Reexamination Certificate
active
06952499
ABSTRACT:
An automatic endmember classification algorithm for hyperspectral data cubes. This algorithm is an improved version of a pattern recognition technology which was developed at Massachusetts Institute of Technology (MIT). The MIT algorithm is called the Extended Cross Correlation (XCC) technique, and it was designed to separate patterns from time resolved spectroscopic data. ASPIRE uses XCC as one of its core algorithms, but it features many improvements. These include: the use of Principle Components Analysis (PCA) to preprocess the data, and automatic endmember searching algorithm, and a Bayesian algorithm which is used to unmix the end-members. This invention also represents a new use of the XCC technology, because it had never before been used to identify spatial targets and patterns in hyperspectral data.
REFERENCES:
Wu et al, Adaptive Tree-Structured Subspace Classification of Hyperspectral Images, IEEE, pp 570-593, 1998.
Jimenez et al, Supervised Classification in High Dimensional Space: Geometrical, Statistical, and Asymptotical Properties of Multivariate Data, IEEE pp 39-54, Feb. 1, 1998.
Auton William G.
Hellner Mark
The United States of America as represented by the Secretary of
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