Robust automatic target recognition using extra-trees

Abstract : 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.
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https://hal-supelec.archives-ouvertes.fr/hal-00556229
Contributor : Anne-Hélène Picot <>
Submitted on : Saturday, January 15, 2011 - 10:53:56 PM
Last modification on : Tuesday, March 26, 2019 - 3:10:28 PM

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Jonathan Pisane, Raphaël Marée, Louis Wehenkel, Jacques Verly. Robust automatic target recognition using extra-trees. 2010 IEEE Radar Conference, May 2010, Washington United States. ⟨10.1109/RADAR.2010.5494683⟩. ⟨hal-00556229⟩

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