Title :
Automatic Target Recognition by Means of Polarimetric ISAR Images and Neural Networks
Author :
Martorella, M. ; Giusti, E. ; Capria, A. ; Berizzi, F. ; Bates, B.
Author_Institution :
Dept. of "Ing. dell\´\´Inf.", Univ. of Pisa, Pisa
Abstract :
Inverse Synthetic Aperture Radar (ISAR) images are often used for classifying and recognising targets. Moreover the use of a fully polarimentric ISAR image enhances classiication capabilities. In this paper, the authors propose a novel ATR technique based on the use of fully polarimetric ISAR images and Neural Networks. In order to reduce the amount of data processed by the classifier, the brightest scattering centres are first extracted by means of the Pol-CLEAN technique and then their scattering matrices are decomposed using Cameron´s decomposition. The proposed ATR algorithm is finally tested on real data.
Keywords :
feature extraction; geophysical techniques; geophysics computing; neural nets; pattern classification; pattern recognition; radar interferometry; radar polarimetry; remote sensing by radar; synthetic aperture radar; ATC; ATR algorithm; Cameron decomposition; ISAR; Inverse Synthetic Aperture Radar; Pol-CLEAN technique; automatic target classification; automatic target recognition; brightest scattering centre; data classification; data processing; feature extraction; neural network; pattern recognition; polarimetric ISAR image; scattering matrice; Australia; Data mining; Feature extraction; Image databases; Matrix decomposition; Neural networks; Radar scattering; Scattering parameters; Spatial databases; Target recognition;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4779956