17 May 2006 Assessment of a novel decision and reject method for multi-class problems in a target classification framework for SAR scenarios
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Abstract
The enhancement and improvement of classifiers for SAR signatures are a permanent challenge. The focus of this paper is the development of an integrated decision-and-reject method suitable for a kernel-machine-based target classification framework for SAR scenarios. The proposed processing chain consists of a screening process identifying ROIs with target cues, a pre-processing, and a high-performance classifier. A feasible screening method has to provide a maximum of detections namely object hypotheses while the false alarm rate is of lower interest. Therefore the quality of the following classification step significantly depends on the capability of reducing the false alarms. In complex scenarios standard approaches may classify clutter objects incorrectly as targets. To overcome this problem a novel classification scheme was developed. Class discriminating information is computed in a pre-classification step by a family of two-class kernel machines. Thus, a feature vector for an additional classification stage is provided. A comparative assessment was done using a SAR data set provided by QinetiQ. First results are given in terms of ROC curves.
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Wolfgang Middelmann, Wolfgang Middelmann, Alfons Ebert, Alfons Ebert, Ulrich Thoennessen, Ulrich Thoennessen, } "Assessment of a novel decision and reject method for multi-class problems in a target classification framework for SAR scenarios", Proc. SPIE 6237, Algorithms for Synthetic Aperture Radar Imagery XIII, 62370P (17 May 2006); doi: 10.1117/12.664919; https://doi.org/10.1117/12.664919
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