DocumentCode
484712
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
Volume
4
fYear
2008
fDate
7-11 July 2008
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/IGARSS.2008.4779956
Filename
4779956
Link To Document