Comparing CART with backpropagation neural networks in vegetation greenness classification

  • Authors:

  • Jiang Li

  • Affiliations:

  • Austin Peay State University, Clarksville, TN

  • Venue:
  • Proceedings of the 44th annual Southeast regional conference
  • Year:
  • 2006

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Abstract

This paper provides a comparison of CART with BNNs for vegetation greenness classification in terms of classification error rate, computational time, and predicator importance. The experiments were conducted on climatic data and NDVI data. The results show the training time of CART is much less than that of BNNs which achieves better prediction accuracy.