Title :
Improved hyperspectral anomaly detection in heavy-tailed backgrounds
Author :
Adler-Golden, Steven M.
Author_Institution :
Spectral Sci. Inc., Burlington, MA, USA
Abstract :
A new metric for anomaly detection in hyperspectral imagery is developed to account for anisotropic heavy tails in covariance-whitened data. The anisotropy, consisting of a variation in tail heaviness with principal component number, commonly occurs when the number of linearly independent components representing the data to within the noise level is less than the number of data dimensions. The detection metric is generated by representing the probability density function of the data with an empirical anisotropic super-Gaussian model for the probability density function. Its performance exceeds that of the RX and Subspace RX methods in examples from CAP ARCHER and HyMap imagery.
Keywords :
Gaussian processes; geophysical signal processing; object detection; principal component analysis; CAP ARCHER imagery; HyMap imagery; anisotropic heavy tails; anisotropic superGaussian model; anisotropy; covariance-whitened data; heavy-tailed backgrounds; hyperspectral anomaly detection; hyperspectral imagery; linearly independent components; noise level; principal component number; probability density function; subspace RX methods; Anisotropic magnetoresistance; Hyperspectral imaging; Multidimensional systems; Noise level; Object detection; Personal communication networks; Probability density function; Probability distribution; Statistics; Tail; Hyperspectral; RX; anomaly; detection;
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4686-5
Electronic_ISBN :
978-1-4244-4687-2
DOI :
10.1109/WHISPERS.2009.5289019
