Extended profiles with morphological attribute filters for the analysis of hyperspectral data

  • Authors:

  • Mauro Dalla Mura;Jon Atli Benediktsson;Bjorn Waske;Lorenzo Bruzzone

  • Affiliations:

  • Department of Information Engineering and Computer Science, University of Trento Via Sommarive, Povo, Trento, Italy,Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarha ...;Faculty of Electrical and Computer Engineering, University of Iceland, Hjardarhaga 2-6, Reykjavik, Iceland;Department of Photogrammetry, University of Bonn, Nussallee 15, Institute of Geodesy and Geoinformation, Bonn, Germany;Department of Information Engineering and Computer Science, University of Trento Via Sommarive, Povo, Trento, Italy

  • Venue:
  • International Journal of Remote Sensing - Spatial Information Retrieval, Analysis, Reasoning and Modelling
  • Year:
  • 2010

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Abstract

Extended attribute profiles and extended multi-attribute profiles are presented for the analysis of hyperspectral high-resolution images. These extended profiles are based on morphological attribute filters and, through a multi-level analysis, are capable of extracting spatial features that can better model the spatial information, with respect to conventional extended morphological profiles. The features extracted by the proposed extended profiles were considered for a classification task. Two hyperspectral high-resolution datasets acquired for the city of Pavia, Italy, were considered in the analysis. The effectiveness of the introduced operators in modelling the spatial information was proved by the higher classification accuracies obtained with respect to those achieved by a conventional extended morphological profile.