Some recent results on hyperspectral image classification

C. A. Shah, P. Watanachaturaporn, P. K. Varshney, M. K. Arora

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

In this paper, we present a summary of our ongoing research on the classification of hyperspectral images. We are experimenting with both supervised and unsupervised algorithms. In particular, we have developed an unsupervised classification algorithm based on Independent Component Analysis (ICA). This algorithm is known as the ICA mixture model (ICAMM) algorithm and has shown promising results. In addition, we are investigating the use of Support Vector Machines (SVMs), a supervised approach for the classification of hyperspectral data. We have employed the Lagrangian optimization method and call our classifier the Lagrangian SVM (LSVM) classifier. Classification accuracy of these classifiers has been assessed using an error matrix based overall accuracy measure.

Original languageEnglish (US)
Title of host publication2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-353
Number of pages8
ISBN (Electronic)0780383508, 9780780383500
DOIs
StatePublished - Jan 1 2004
Event2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data - Greenbelt, United States
Duration: Oct 27 2003Oct 28 2003

Publication series

Name2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data

Other

Other2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data
CountryUnited States
CityGreenbelt
Period10/27/0310/28/03

Keywords

  • ICA mixture model
  • classification
  • hyperspectral images
  • independent componenet analysis
  • support vector machines

ASJC Scopus subject areas

  • Computer Networks and Communications

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    Shah, C. A., Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. (2004). Some recent results on hyperspectral image classification. In 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (pp. 346-353). [1295214] (2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WARSD.2003.1295214