A support vector data description scheme for hyperspectral target detection using first-order Markov modeling

Wesam A. Sakla, Adel A. Sakla, Andrew Chan, Jim Ji

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

1 Citation (Scopus)

Abstract

Spectral variability remains a challenging problem for target detection in hyperspectral (HS) imagery. In this paper, we have applied the kernel-based support vector data description (SVDD) to perform full-pixel target detection. In target detection scenarios, we do not have a collection of samples characterizing the target class; we are typically given a pure target signature that is obtained from a spectral library. In our work, we use the pure target signature and first-order Markov theory to generate N samples to model the spectral variability of the target class. We vary the value of N and observe its effect to determine a value of N that provides acceptable detection performance. We have inserted target signatures into an urban HS scene with varying levels of spectral variability to explore the performance of the proposed SVDD target detection scheme in these scenarios. The proposed approach makes no assumptions regarding the underlying distribution of the scene data as do traditional stochastic detectors such as the adaptive matched filter (AMF). Detection results in the form of confusion matrices and receiver-operating- characteristic (ROC) curves demonstrate that the proposed SVDD-based scheme is highly accurate and yields higher true positive rates (TPR) and lower false positive rates (FPR) than the AMF.

Original languageEnglish
Title of host publicationAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Volume7696
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI - Orlando, FL, United States
Duration: 5 Apr 20108 Apr 2010

Other

OtherAutomatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
CountryUnited States
CityOrlando, FL
Period5/4/108/4/10

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Keywords

  • Automatic target recognition
  • Hyperspectral imagery
  • Markov modeling
  • Support vector data description
  • Target detection

ASJC Scopus subject areas

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

Cite this

Sakla, W. A., Sakla, A. A., Chan, A., & Ji, J. (2010). A support vector data description scheme for hyperspectral target detection using first-order Markov modeling. In Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI (Vol. 7696). [76960X] https://doi.org/10.1117/12.849934