Paper Info
Title
Multivariate copula statistical model and weighted sparse classification for radar image target recognition
Abstract
We propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estimated copula parameters are stacked together in a matrix structure. In order to include the locality information of this dictionary for each unknown radar image to recognize, we affect weights for its atoms (columns). That is done by calculating the Kullback–Leibler divergence (KLD) between the multivariate copula parameters of training and test radar images. Finally, the unknown radar image is recognized through the SRC classifier. Several empirical results carried out on the SAR (synthetic aperture radar) and ISAR (inverse synthetic aperture radar) images demonstrate that the proposed method achieves high recognition rates and outperforms the remaining methods.
Year
DOI
Venue
2020
10.1016/j.compeleceng.2020.106633
Computers & Electrical Engineering
Keywords
DocType
Volume
ISAR-ATR,SAR-ATR,DT-CWT,Copula multivariate modeling,WSRC,KLD
Journal
84
ISSN
Citations 
PageRank 
0045-7906
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Ayoub Karine100.34
Abdelmalek Toumi2229.24
ali khenchaf39830.12
Mohammed El Hassouni413529.52