Comparison of local transfer function classifier and radial basis function neural network with and without an exhaustively defined set of classes

  • Authors:

  • L. Li;J. Ma;Q. Wen

  • Affiliations:

  • State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing, PR China,Gra ...;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing, PR China;State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing, PR China,Gra ...

  • Venue:
  • International Journal of Remote Sensing
  • Year:
  • 2009

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Abstract

The local transfer function classifier (LTF-C) is a new radial basis function (RBF)-like neural network, but it uses an entirely different learning algorithm, so as to achieve the novel ability of locally partitioning the feature space. This paper investigates LTF-C and the RBF neural network with reference to land cover classification with and without an exhaustively defined set of classes using Landsat-5 TM data. Results indicate that LTF-C achieves higher accuracy, usually with fewer hidden units, than the RBF neural network with an exhaustively defined set of classes. LTF-C is more stable than the RBF neural network during classifications of the testing set, including the untrained class. Through the setting of post-classification thresholds on the network's outputs, a well-trained RBF neural network sometimes gives abnormally high output value for an input pattern which represents the untrained class. Meanwhile, a well-trained LTF-C outputs extremely low values all the time under the same circumstances. Therefore, LTF-C may outperform the RBF neural network in detecting or removing the atypical classes that are excluded from the training set, which maybe useful in situations where only interesting types of land cover are selected in the training set, due to high labour costs or difficulties in defining all classes represented in a study area.