COMPARATIVE EVALUATION OF VECTOR MACHINE BASED HYPERSPECTRAL CLASSIFICATION METHODS


KARACA A. C. , ERTÜRK A., Gullu M. K. , ERTÜRK S.

IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22 - 27 July 2012, pp.4970-4973 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/igarss.2012.6352496
  • City: Munich
  • Country: Germany
  • Page Numbers: pp.4970-4973
  • Keywords: Import Vector Machines, Support Vector Machines, Relevance Vector Machines, Hyperspectral Classification

Abstract

This paper presents a comparison of the classification performance of some vector machine based classification methods, namely, Import Vector Machines (IVM), Support Vector Machines (SVM) and Relevance Vector Machines (RVM), for hyperspectral images. Evaluation is carried out in terms of the number of vectors and classification accuracies. Furthermore, novel to this paper, Discriminative Random Field method with Graph Cut algorithm is applied to the probabilistic classification output of IVM based hyperspectral classification results, and it is shown that this approach significantly increases classification accuracies.