Hidden Markov model approaches to hyperspectal image classification

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)


In this paper, we present a Hidden Markov Model (HMM) approach to hyperspectral image classification. HMM has been widely used in speech recognition to model a doubly stochastic process with a hidden state process that can be only observed through a sequence of observations. Since the temporal variability of a speech signal is similar to the spectral variability of a remotely sensed image pixel vector, the same idea can be applied to hyperspectral image classification. It makes use of a hidden Markov process to characterize the spectral correlation and band-to-band variability where the model parameters are determined by the spectra of the pixel vectors that form the observation sequences. Experiments demonstrate that the HMM can better describe the unobserved spectral progenies so as to improve classification performance.

Original languageEnglish
Number of pages3
Publication statusPublished - 2001
Event2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001) - Sydney, NSW, Australia
Duration: 2001 Jul 92001 Jul 13


Conference2001 International Geoscience and Remote Sensing Symposium (Igarrs 2001)
CitySydney, NSW

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences


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