Rapid, high performance hyperspectral anomaly detection via global support vector data description

Reuven Meth, Amit Banerjee, Philippe Burlina, Thomas Strat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationAutomatic Target Recognition XVIII
DOIs
StatePublished - Jun 17 2008
EventAutomatic Target Recognition XVIII - Orlando, FL, United States
Duration: Mar 19 2008Mar 20 2008

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6967
ISSN (Print)0277-786X

Other

OtherAutomatic Target Recognition XVIII
CountryUnited States
CityOrlando, FL
Period3/19/083/20/08

Keywords

  • Anomaly detection
  • HSI
  • Hyperspectral
  • Real-time anomaly detection
  • SVDD
  • SVM

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Rapid, high performance hyperspectral anomaly detection via global support vector data description'. Together they form a unique fingerprint.

  • Cite this

    Meth, R., Banerjee, A., Burlina, P., & Strat, T. (2008). Rapid, high performance hyperspectral anomaly detection via global support vector data description. In Automatic Target Recognition XVIII [69670A] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6967). https://doi.org/10.1117/12.777164