Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on SpeckleReport as inadecuate

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Scientific Programming - Volume 2016 2016, Article ID 3801053, 11 pages -

Research ArticleXi’an Research Institute of Hi-Tech, Xi’an 710025, China

Received 17 July 2016; Revised 24 September 2016; Accepted 19 October 2016

Academic Editor: Xiong Luo

Copyright © 2016 Hongqiao Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aiming at the multiple target recognition problems in large-scene SAR image with strong speckle, a robust full-process method from target detection, feature extraction to target recognition is studied in this paper. By introducing a simple 8-neighborhood orthogonal basis, a local multiscale decomposition method from the center of gravity of the target is presented. Using this method, an image can be processed with a multilevel sampling filter and the target’s multiscale features in eight directions and one low frequency filtering feature can be derived directly by the key pixels sampling. At the same time, a recognition algorithm organically integrating the local multiscale features and the multiscale wavelet kernel classifier is studied, which realizes the quick classification with robustness and high accuracy for multiclass image targets. The results of classification and adaptability analysis on speckle show that the robust algorithm is effective not only for the MSTAR Moving and Stationary Target Automatic Recognition target chips but also for the automatic target recognition of multiclass-multitarget in large-scene SAR image with strong speckle; meanwhile, the method has good robustness to target’s rotation and scale transformation.

Author: Hongqiao Wang, Yanning Cai, Guangyuan Fu, and Shicheng Wang



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