A higher order statistical approach to spectral unmixing of remote sensing imagery

Chintan A. Shah, Pramod K. Varshney

Research output: Contribution to conferencePaper

5 Scopus citations

Abstract

In this paper, a novel approach for unsupervised spectral unmixing in remote sensing imagery is presented. This approach is derived from Independent Component Analysis (ICA). First, we present the limitations of Gaussian Mixture Model (GMM) and ICA for spectral unmixing. To overcome these limitations we have developed an approach that employs the ICA model to characterize the data generation process and have proposed an ICA mixture model (ICAMM) based approach for unsupervised spectral unmixing. This approach estimates the endmember probability density function by modeling it with a non-Gaussian probability distribution. Thus, the ability to model higher order statistical properties of remote sensing imagery increases the practical applicability of ICAMM for spectral unmixing. The results from our experimental study have demonstrated the efficacy of the proposed algorithm for unsupervised spectral unmixing.

Original languageEnglish (US)
Pages1065-1068
Number of pages4
StatePublished - Dec 1 2004
Event2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004 - Anchorage, AK, United States
Duration: Sep 20 2004Sep 24 2004

Other

Other2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004
CountryUnited States
CityAnchorage, AK
Period9/20/049/24/04

Keywords

  • Blind source separation
  • Gaussian mixture model
  • Higher order statistics
  • Independent componant analysis mixture model
  • Independent component analysis
  • Linear mixture model
  • Spectral unmixing

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Shah, C. A., & Varshney, P. K. (2004). A higher order statistical approach to spectral unmixing of remote sensing imagery. 1065-1068. Paper presented at 2004 IEEE International Geoscience and Remote Sensing Symposium Proceedings: Science for Society: Exploring and Managing a Changing Planet. IGARSS 2004, Anchorage, AK, United States.