Unsupervised constrained linear fisher's discriminant analysis for hyperspectral image classification

Baohong Ji, Chein I. Chang, Janet L. Jensen, James O. Jensen

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)


Fisher's linear discriminant analysis (FLDA) has been widely used in pattern classification due to its criterion, called Fisher's ratio, based on the ratio of between-class variance to within-class variance. Recently, a linear constrained discriminant analysis (LCDA) was developed for hyperspectral image classification where Fisher's ratio was replaced with the ratio of inter-distance to intra-distance and the target signatures were constrained to orthogonal directions. This paper directly extends the FLDA to constrained Fisher's linear discriminant analysis (CFLDA), which uses Fisher's ratio as a classification criterion. Since CFLDA is supervised which requires a set of training samples, this paper further extends the CFLDA to an unsupervised CFLDA (UCFLDA) by including a new unsupervised training sample generation algorithm to automatically produce a sample pool of training data to be used for CFLDA. In order to determine the number of classes, p, to be classified, a newly developed concept, called virtual dimensionality (VD) is used to estimate the p where a Neyman-Pearson-based eigen-analysis approach developed by Harsanyi, Farrand and Chang, called noise-whitened HFC (NWHFC)'s method, is implemented to find the VD. The experimental results have shown that the proposed UCFLDA perform effectively for HYDICE data and provides a promising unsupervised classification technique for hyperspectral imagery.

Original languageEnglish
Article number45
Pages (from-to)344-353
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 2004
EventImaging Spectrometry X - Denver, CO, United States
Duration: 2004 Aug 22004 Aug 4

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Unsupervised constrained linear fisher's discriminant analysis for hyperspectral image classification'. Together they form a unique fingerprint.

Cite this