Spectral information divergence for hyperspectral image analysis

Research output: Contribution to conferencePaperpeer-review

250 Citations (Scopus)

Abstract

In this paper, we propose an information theoretic criterion, called Spectral Information Divergence (SID) for spectral similarity and discriminability. It is derived from the concept of divergence arising in information theory and can be used to describe the statistics of a spectrum. Unlike Spectral Angle Mapper (SAM) which extracts geometric features between two spectra, SID views each pixel spectrum as a random variable and then measures the discrepancy of probabilistic behaviors between two spectra. In order to evaluate SID, SAM is used for comparison via hyperspectral data. Experimental results show that SID can characterize spectral similarity and variability more effectively than SAM.

Original languageEnglish
Pages509-511
Number of pages3
Publication statusPublished - 1999
EventProceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century' - Hamburg, Ger
Duration: 1999 Jun 281999 Jul 2

Conference

ConferenceProceedings of the 1999 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'99) 'Remote Sensing of the Systems Earth - A Challenge for the 21st Century'
CityHamburg, Ger
Period99-06-2899-07-02

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • General Earth and Planetary Sciences

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