Demonstration of an automatic target recognition algorithm simulation and evaluation testbed for mine detection algorithms

1995 ◽  
Author(s):  
Bryan S. Ware ◽  
George B. Maksymonko ◽  
David E. Poole
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Mine Detection ◽  
Detection Algorithms ◽  
Simulation And Evaluation

scholarly journals Robust Automatic Target Recognition Algorithm for Large-Scene SAR Images and Its Adaptability Analysis on Speckle

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Hongqiao Wang ◽  
Yanning Cai ◽  
Guangyuan Fu ◽  
Shicheng Wang
Keyword(s):  
Target Recognition ◽  
Low Frequency ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Scale Transformation ◽  
Sar Image ◽  
Sar Images ◽  
Stationary Target ◽  
Multilevel Sampling ◽  
Process Method

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.

An Automatic Target Recognition Algorithm Based on Support Vector Machine

2014 ◽  
Vol 496-500 ◽  
pp. 1873-1876
Author(s):  
He Zhang ◽  
Jie Li ◽  
Bei Bei Xu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector Machine Model ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Machine Model ◽  
Fast Recognition ◽  
High False Alarm Rate

To improve the performance of automatic target recognition technology and solve the problems of traditional methods, such as high false alarm rate and poor adaptability to environment changes, a new algorithm based on support vector machine is proposed. We have realized the feature extraction of the target and the parameter optimization of the support vector machine to get the support vector machine model applied to the target recognition of unknown images. Experiment results show that the algorithm has a good recognition effect, a fast recognition speed and certain anti-interference abilities based on sufficient samples training.

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