Recognition of SAR occluded targets using SVM

Authors:
Yi Gao;Rui Hu;Licheng Jiao;Weida Zhou;Xiangrong Zhang
Affiliations:
Institute of Intelligence Information Processing, and National Key Lab for Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligence Information Processing, and National Key Lab for Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligence Information Processing, and National Key Lab for Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligence Information Processing, and National Key Lab for Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligence Information Processing, and National Key Lab for Radar Signal Processing, Xidian University, Xi’an, China
Venue:
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Year:
2007

Citing 8
Cited 1

The nature of statistical learning theory

The nature of statistical learning theory
Target indexing in SAR images using scattering centers and the Hausdorff distance

Pattern Recognition Letters
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
Recognition of Articulated and Occluded Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition Results Using MSTAR Synthetic Aperture Radar Data

CVBVS '00 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (CVBVS 2000)
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Automatic Target Classification " Experiments on the MSTAR SAR Images

SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Guest Editorial Introduction To The Special Issue On Automatic Target Detection And Recognition

IEEE Transactions on Image Processing

Template matching of occluded object under low PSNR

Digital Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel method for automatic occluded targets recognition in SAR images is proposed in this paper. Different SAR occluded targets are simulated based on actual vehicles from the MSTAR database, and are recognized using SVM classifier by grouping recognition based on the targets azimuth angles. It is shown that the proposed method outperforms the typical methods in accuracy at high occlusion, and robustness to occlusion with experiments considering accuracy and confusion matrix.