Supervised hyperspectral image classification with rejection

Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Hyperspectral image classification is a challenging classification problem: obtaining complete and representative training sets is costly; pixels can belong to unknown classes; and it is generally an ill-posed problem. The need to achieve high classification accuracy surpasses the need to classify the entire image. To achieve this, we use classification with rejection by providing the classifier an option not to classify a pixel and consequently reject it. We propose a method for supervised hyperspectral image classification combining the use of contextual priors with classification with rejection. Rejection is introduced as an extra class that models the probability of classifier failure. We validate the resulting algorithm in the AVIRIS Indian Pines scene and illustrate the performance increase resulting from classification with rejection.

Original languageEnglish (US)
Title of host publication2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781479979295
StatePublished - Nov 10 2015
EventIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy
Duration: Jul 26 2015Jul 31 2015

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


OtherIEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015


  • classification with context
  • classification with rejection
  • hyperspectral image classification

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences


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