Extended Self-Dual Attribute Profiles for the Classification of Hyperspectral Images

Gabriele Cavallaro, Mauro Dalla Mura, Jón Atli Benediktsson*, Lorenzo Bruzzone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


In this letter, we explore the use of self-dual attribute profiles (SDAPs) for the classification of hyperspectral images. The hyperspectral data are reduced into a set of components by nonparametric weighted feature extraction (NWFE), and a morphological processing is then performed by the SDAPs separately on each of the extracted components. Since the spatial information extracted by SDAPs results in a high number of features, the NWFE is applied a second time in order to extract a fixed number of features, which are finally classified. The experiments are carried out on two hyperspectral images, and the support vector machines and random forest are used as classifiers. The effectiveness of SDAPs is assessed by comparing its results against those obtained by an approach based on extended APs.

Original languageEnglish
Article number7103273
Pages (from-to)1690-1694
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Issue number8
Publication statusPublished - 1 Aug 2015

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.

Other keywords

  • Attribute filters (AFs)
  • attribute profiles (APs)
  • extended APs (EAPs)
  • mathematical morphology
  • nonparametric weighted feature extraction (NWFE)
  • remote sensing
  • self-dual APs (SDAPs)


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