An open source object-based framework to extract landform classes

  • Authors:

  • F. F. Camargo;C. M. Almeida;G. A. O. P. Costa;R. Q. Feitosa;D. A. B. Oliveira;C. Heipke;R. S. Ferreira

  • Affiliations:

  • National Institute for Space Research (INPE), Remote Sensing Division, PO Box 515, São José dos Campos, SP, Brazil;National Institute for Space Research (INPE), Remote Sensing Division, PO Box 515, São José dos Campos, SP, Brazil;Catholic University of Rio de Janeiro (PUC-Rio), Electrical Engineering Department, PO Box 38097, Rio de Janeiro, RJ, Brazil;Catholic University of Rio de Janeiro (PUC-Rio), Electrical Engineering Department, PO Box 38097, Rio de Janeiro, RJ, Brazil;Catholic University of Rio de Janeiro (PUC-Rio), Electrical Engineering Department, PO Box 38097, Rio de Janeiro, RJ, Brazil;Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, Nienburger Str. 1, D-30167 Hannover, Germany;Catholic University of Rio de Janeiro (PUC-Rio), Electrical Engineering Department, PO Box 38097, Rio de Janeiro, RJ, Brazil

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.