Hyper spectral image classification using random forests and neural networks

B. T. Abe, O. O. Olugbara, T. Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)


Spectral unmixing of hyperspectral images are based on the knowledge of a set of unknown endmembers. Unique characteristics of hyperspectral dataset enable different processing problems to be resolved using robust mathematical logic such as image classification. Consequently, pixel purity index is used to find endmembers from Washington DC mall hyperspectral image dataset. The generalized reduced gradient algorithm is used to estimate fractional abundances in the hyperspectral image dataset. The WEKA data mining tool is selected to construct random forests and neural networks classifiers from the set of fractional abundances. The performances of these classifiers are experimentally compared for hyperspectral data land cover classification. Results show that random forests give better classification accuracy when compared to neural networks. The study proffers solution to the problem associated with land cover classification by exploring generalized reduced gradient approach with learning classifiers to improve overall classification accuracy. The classification accuracy comparison of classifiers is important for decision maker to consider tradeoffs in accuracy and complexity of methods.

Original languageEnglish
Title of host publicationInternational MultiConference of Engineers and Computer Scientists, IMECS 2012
EditorsJon Burgstone, S. I. Ao, Craig Douglas, W. S. Grundfest
PublisherNewswood Limited
Number of pages6
ISBN (Electronic)9789881925169
ISBN (Print)9789881925114
Publication statusPublished - 2012
Event2012 World Congress on Engineering and Computer Science, WCECS 2012 - San Francisco, United States
Duration: 24 Oct 201226 Oct 2012

Publication series

NameLecture Notes in Engineering and Computer Science
ISSN (Print)2078-0958


Conference2012 World Congress on Engineering and Computer Science, WCECS 2012
Country/TerritoryUnited States
CitySan Francisco


  • Classifiers
  • Generalized reduced gradient
  • Hyperspectral image
  • Land cover classification

ASJC Scopus subject areas

  • Computer Science (miscellaneous)


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