Supervised feature-based classification of multi-channel SAR images

  • Authors:

  • D. Borghys;Y. Yvinec;C. Perneel;A. Pizurica;W. Philips

  • Affiliations:

  • Signal and Image Centre, Royal Military Academy, Renaissancelaan 30, B-1000 Brussels, Belgium;Signal and Image Centre, Royal Military Academy, Renaissancelaan 30, B-1000 Brussels, Belgium;Signal and Image Centre, Royal Military Academy, Renaissancelaan 30, B-1000 Brussels, Belgium;Department of Telecommunication and Information Processing, University of Ghent, St-Pietersnieuwsstraat 41, B-9000 Gent, Belgium;Department of Telecommunication and Information Processing, University of Ghent, St-Pietersnieuwsstraat 41, B-9000 Gent, Belgium

  • Venue:
  • Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
  • Year:
  • 2006

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Abstract

This paper describes a new method for a feature-based supervised classification of multi-channel SAR data. Classic feature selection and classification methods are inadequate due to the diverse statistical distributions of the input features. A method based on logistic regression (LR) and multinomial logistic regression (MNLR) for separating different classes is therefore proposed. Both methods, LR and MNLR, are less dependent on the statistical distribution of the input data. A new spatial regularization method is also introduced to increase consistency of the classification result. The classification method was applied to a project on humanitarian demining in which the relevant classes were defined by experts of a mine action center. A ground survey mission collected learning and validation samples for each class. Results of the proposed classification methods are shown and compared to a maximum likelihood classifier.