A technique based on adaptive windows for the classification of radar sounder data

The automatic analysis of radargrams acquired by radar sounder instruments is an important task, as it has been often highlighted by the scientific community. In particular, an automatic classification technique enables a fast and objective extraction of ice subsurface target properties on wide areas. The main drawback of the available classification techniques is the use of fixed size windows for feature extraction, which causes a poor classification performance at the transition boundary between adjacent subsurface targets. To address this issue, in this paper, we propose a novel technique for the automatic classification of ice subsurface targets in radargrams. The method provides two key contributions, i.e., i) the use of a window size estimation method to adaptively characterize the subsurface targets, and ii) the use of the estimated windows for extracting novel discriminant features for automatic classification. Quantitative and qualitative results obtained by applying the method to a data set of radargrams acquired by an airborne-mounted radar sounder instrument in central Antarctica show the effectiveness of the proposed technique, which outperforms previously proposed methods.