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.
['Dirk Borghys', 'Yann Yvinec', 'Christiaan Perneel', 'Aleksandra Pizurica', 'Wilfried Philips']
Supervised feature-based classification of multi-channel SAR images