  In this paper we present a novel method for robust hyperspectral image
classification using context and rejection. Hyperspectral image classification
is generally an ill-posed image problem where pixels may belong to unknown
classes, and obtaining representative and complete training sets is costly.
Furthermore, the need for high classification accuracies is frequently greater
than the need to classify the entire image.
  We approach this problem with a robust classification method that combines
classification with context with classification with rejection. A rejection
field that will guide the rejection is derived from the classification with
contextual information obtained by using the SegSALSA algorithm. We validate
our method in real hyperspectral data and show that the performance gains
obtained from the rejection fields are equivalent to an increase the dimension
of the training sets.
