Journal
PATTERN RECOGNITION LETTERS
Volume 27, Issue 4, Pages 252-258Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.patrec.2005.08.006
Keywords
SAR image classification; logistic regression; multinomial logistic regression; multi-channel SAR
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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 dernining 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. (c) 2005 Elsevier B.V. All rights reserved.
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