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Robust automatic target recognition using extra-trees

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4 Author(s)
Pisane, J. ; Dept. Of Electr. Eng. & Comput. Sci., Univ. of Liege, Liège, Belgium ; Marée, R. ; Wehenkel, L. ; Verly, J.

In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT image classifier. It uses randomized sub-windows extraction and extremely randomized trees (extra-trees). This approach requires very little pre-processing of the images, thereby limiting the computational load. It was successfully tested on an extended version of the public standard MSTAR database, that includes targets of interest, false targets, and background clutter. A misclassification rate of about three percent has been achieved. In this paper, we describe a new automatic target recognition algorithm for classifying SAR images based on the PiXiT image classifier. It uses randomized sub-windows extraction and extremely randomized trees (extra-trees). This approach requires very little pre-processing of the images, thereby limiting the computational load. It was successfully tested on an extended version of the public standard MSTAR database, that includes targets of interest, false targets, and background clutter. A misclassification rate of about three percent has been achieved.

Published in:

Radar Conference, 2010 IEEE

Date of Conference:

10-14 May 2010

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