Kernel-Based Anomaly Detection in Hyperspectral Imagery

Abstract

In this paper we present a nonlinear version of the wellknown anomaly detection method referred to as the RXalgorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the non-linear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing it in terms of kernels which implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA431620

Entities

People

  • Heesung Kwon
  • Nasser M. Nasrabadi

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Anomaly Detection
  • Change Detection
  • Covariance
  • Detection
  • Detectors
  • Eigenvalues
  • Eigenvectors
  • Equations
  • False Alarms
  • Hyperspectral Imagery
  • Kernel Functions
  • Pattern Recognition
  • Recognition
  • Signal Processing
  • Three Dimensional
  • Warning Systems

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Computer Vision.
  • Neurotrauma and Rehabilitation Medicine.

Technology Areas

  • Space
  • Space - Space Objects