一种基于动态字典学习的SAR图像目标识别算法

A Target Recognition Method in SAR Images Based on Dynamic Dictionary Learning

本文提出了一种应用于SAR图像目标识别的动态字典学习算法,该算法通过在字典学习过程中自动删除和增加字典条目来调整字典表示性能与尺寸。删除操作是在删除代价的约束下针对相关度高或利用率低的字典条目进行,而增加操作是在增加代价的约束下针对信号表示的残留误差的主分量进行,通过交替执行删除和增加操作来不断优化字典,使其表示能力达到最佳。在MSTAR数据集上的实验验证了算法性能,并给出了相应的参数调整建议。从实验结果和分析可看出,该算法具有识别率高、算法稳定等特点。

A dynamic dictionary learning method was proposed for automatic target recognition in Synthetic Aperture Radar （SAR） images.The new method decreased the size of a dictionary by erasing the useless or high-correlation atoms in the dictionary under a deleting cost.It added an atom by decomposing the residue data matrix under an adding cost.The performance of signal representation of a dictionary could be improved in the dynamic learning procedure.Experiments based on MSTAR database show that the proposed method can converge to a proper-size dictionary no matter how large the original size is.The proposed method was demonstrated to have good and robust performance of SAR target recognition in the trials.