Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles

Giorgio Licciardi*, Prashanth Reddy Marpu, Jocelyn Chanussot, Jon Atli Benediktsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

143 Citations (Scopus)

Abstract

Morphological profiles (MPs) have been proposed in recent literature as aiding tools to achieve better results for classification of remotely sensed data. MPs are in general built using features containing most of the information content of the data, such as the components derived from principal component analysis (PCA). Recently, nonlinear PCA (NLPCA), performed by autoassociative neural network, has emerged as a good unsupervised technique to fit the information content of hyperspectral data into few components. The aim of this letter is to investigate the classification accuracies obtained using extended MPs built from the features of NPCA. A comparison of the two approaches has been validated on two different data sets having different spatial and spectral resolutions/coverages, over the same ground truth, and also using two different classification algorithms. The results show that NLPCA permits one to obtain better classification accuracies than using linear PCA.

Original languageEnglish
Article number6084704
Pages (from-to)447-451
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume9
Issue number3
DOIs
Publication statusPublished - 2012

Other keywords

  • Classification
  • extended morphological profiles (EMPs)
  • neural networks (NNs)
  • nonlinear principal component analysis (NLPCA)

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