Method and system for detecting anomalies in multispectral...

Details Method and system for detecting anomalies in multispectral... Method and system for detecting anomalies in multispectral...

2007-03-20

2007-03-20

Tran, Phuoc (Department: 2624)

Image analysis

Pattern recognition

Feature extraction

C382S191000, C382S224000

Reexamination Certificate

active

10454116

ABSTRACT:
A method for detecting anomalies from multidimensional data comprises: a) receiving multidimensional data; b) estimating background parameters of a normal compositional model from the multidimensional data; c) estimating abundance values of the normal compositional model from the background parameters and the multidimensional data; d) determining an anomaly detection statistic from the multidimensional data, the background parameters, and abundance values; and e) classifying an observation from the multidimensional data as either a background reference or an anomaly from the anomaly detection statistic.

REFERENCES:
patent: 6353673 (2002-03-01), Shnitser et al.
patent: 6587575 (2003-07-01), Windham et al.
Stein, David W. J. ; Modeling Variability In Hyperspectral Imagery Using A Stochastic Compositional Approach; Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS '01, Sydney Australia, Jul. 2001.
Stein, Dr. David W. ; Hyperspectral Detection Processing Development; ILIR '01 SSC San Diego In-House Laboratory Independent Research 2001 Annual Report; pp. 63-66; SSC-San Diego Technical Document, May 2002, USA.
Stein, David W. J. ; Stochastic Compositional Models Applied To Subpixel Analysis Of Hyperspectral Imagery; Proceedings of SPIE vol. 4480, Imaging Spectrometry VII, M. Descour and S. Shen eds.; San Diego CA 2001; USA.

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