Classifier combination and feature selection methods for polarimetric SAR classification

G. Gigli; R. Sabry; G. Lampropoulos

doi:10.1117/12.719407

9 April 2007 Classifier combination and feature selection methods for polarimetric SAR classification

G. Gigli, R. Sabry, G. Lampropoulos

Proceedings Volume 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007; 65710B (2007) https://doi.org/10.1117/12.719407
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

Training classifiers individually, and then fusing their results, has the potential to improve classification accuracy; often, dramatic improvements are realized. In this paper we examine how training classifiers using multiple polarimetric features such as the Cloude-Pottier decomposition, even and odd bounce and the Polarimetric Whitening filter and then fusing their results affects performance of ship classification. We explore and compare two currently competing technologies of classifier bagging and classifier boosting for classifier fusion and introduce a new approach which conducts a search through solution space to configure an optimal classifier given a library of classifiers and features. A related and important facet of this work is feature selection and feature reduction methods. We explore how the selection of different features affects classification performance. We also explore estimates of the classifier error and provide estimates for noise bounds on the data and compare performance of the different methods compared to the noise present in data.

Citation Download Citation

G. Gigli, R. Sabry, and G. Lampropoulos "Classifier combination and feature selection methods for polarimetric SAR classification", Proc. SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007, 65710B (9 April 2007); https://doi.org/10.1117/12.719407

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