A new framework for hyperspectral image classification using multiple spectral and spatial features

Mahdi Khodadadzadeh, Jun Li, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jose M. Bioucas-Dias

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

This paper presents a new multiple feature learning approach for accurate spectral-spatial classification of hyperspec-tral images. The proposed method integrates multiple features based on the logarithmic opinion pool. We consider subspace multinomial logistic regression for classification as it exhibits a flexible structure for the combination of multiple features through the posterior probability. At the same time, it is able to cope with highly mixed hyperspectral data and with the presence of limited training samples. In this work, we considered lowpass filtering and morphological attribute profiles for spatial feature extraction. Our experimental results with a real hyperspectral images collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the proposed method exhibits state-of-the-art classification performance.

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4628-4631
Number of pages4
ISBN (Electronic)9781479957750
DOIs
Publication statusPublished - 4 Nov 2014
EventJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014 - Quebec City, Canada
Duration: 13 Jul 201418 Jul 2014

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

ConferenceJoint 2014 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014 and the 35th Canadian Symposium on Remote Sensing, CSRS 2014
Country/TerritoryCanada
CityQuebec City
Period13/07/1418/07/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Other keywords

  • Hyperspectral images
  • multiple features learning
  • spectral-spatial classification
  • subspace multinomial logistic regression (MLRsub)

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