Real-time constrained discriminant analysis to target detection and classification in hyperspectral imagery

Qian Du, Hsuan Ren

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a pre-assigned color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalPattern Recognition
Volume36
Issue number1
DOIs
StatePublished - Jan 2003

Keywords

  • Classification
  • Constrained linear discriminant analysis (CLDA)
  • Detection
  • Hyperspectral imagery
  • Real-time processing

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