Share Email Print

Proceedings Paper

Rapid high-performance hyperspectral anomaly detection via global support vector data description
Author(s): Reuven Meth; Amit Banerjee; Philippe Burlina; Thomas Strat
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Existing techniques for hyperspectral image (HSI) anomaly detection are computationally intensive precluding real-time implementation. The high dimensionality of the spatial/spectral hypercube with associated correlations between spectral bands present significant impediments to real time full hypercube processing that accurately encapsulates the underlying modeling. Traditional techniques have imposed Gaussian models, but these have suffered from significant computational requirements to compute large inverse covariance matrices as well as modeling inaccuracies. We have developed a novel data-driven, non-parametric HSI anomaly detector that has significantly reduced computational complexity with enhanced HSI modeling, providing the capability for real time performance with detection rates that match or surpass existing approaches. This detector, based on the Support Vector Data Description (SVDD), provides accurate, automated modeling of multi-modal data, facilitating effective application of a global background estimation technique which provides the capability for real time operation on a standard PC platform. We have demonstrated one second processing time on hypercubes of dimension 256×256×145, along with superior detection performance relative to alternate detectors. Computation performance analysis has been quantified via processing runtimes on a PC platform, and detection/false-alarm performance is described via Region Operating Characteristic (ROC) curve analysis for the SVDD anomaly detector vs. alternate anomaly detectors.

Paper Details

Date Published: 2 May 2008
PDF: 11 pages
Proc. SPIE 6967, Automatic Target Recognition XVIII, 69670A (2 May 2008); doi: 10.1117/12.777164
Show Author Affiliations
Reuven Meth, SET Corp. (United States)
Amit Banerjee, Johns Hopkins Univ. (United States)
Philippe Burlina, Johns Hopkins Univ. (United States)
Thomas Strat, SET Corp. (United States)

Published in SPIE Proceedings Vol. 6967:
Automatic Target Recognition XVIII
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

© SPIE. Terms of Use
Back to Top