Vol. 6, No. 1, 26-37, 2007

A supervised classification of multi-channel high-resolution SAR data
Dirk Borghys and Christiaan Perneel

Many methods have been proposed in literature for the supervised classification of multi-channel (polarimetric and/or multi-frequency) SAR data. Most of them are based on the extraction of a set of features from the original SAR data. In this paper the influence of these features on the results of the classification is examined in a quantitative manner. A set of multi-channel (P, L, C and X band) SAR data was acquired by an airborne system over a site in Southern Europe. A ground-truth mission defined the classes for learning and validation. A feature-based classification method, based on logistic regression, is used for detecting each of the classes. Logistic regression combines the input features into a non-linear function, the logistic function, in order to distinguish that class from all others. For each class a 'detection image', with a well-defined statistical meaning, is obtained. The value at each pixel in the detection image represents the conditional probability that the pixel belongs to that class, given all input features. The logistic regression is performed using a step-wise method in which, at each step, the most discriminating feature is added to the selected feature set, but only if its addition contributes significantly to the detection. The logistic regression thus also performs a feature selection. Moreover, logistic regression allows combining input data with very diverse statistical distributions.

The main aim of the current paper is to investigate the usefulness of each feature for the detection of the different classes.

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Submitted: 21 June 2006
Revised: 26 Feb 2007
Accepted: 28 Feb 2007
Published: 21 Mar 2007
Responsible editor: Mario Caetano

Borghys D & C Perneel, 2007. A supervised classification of multi-channel high-resolution SAR data. EARSeL eProceedings, 6(1): 26-37


EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France


BIS Library and Information System, Carl von Ossietzky University of Oldenburg


ISSN 1729-3782