基于最大熵准则的多视角SAR目标分类方法

针对合成孔径雷达（Synthetic aperture radar，SAR）目标分类问题，提出基于最大熵准则的多视角方法。采用经典的图像相似度测度构建不同视角SAR图像之间的相关性矩阵，在此基础上分别计算不同视角组合条件下的非线性相关信息熵值。非线性相关信息熵值可分析多个变量之间的统计特性，熵值的大小即可反映不同变量之间的内在关联。根据最大熵的原则选择最优的视角子集，其中SAR图像具有最大的内在相关性。分类过程以联合稀疏表示为基础，对具有最大熵值的多个视角进行联合表示。联合稀疏表示模型同时处理若干稀疏表示问题，在它们具有关联的条件下具有提升重构精度的优势。根据不同视角求解得到的表示系数，按照类别分别计算对于选取多视角的重构误差，并根据误差最小的准则进行最终决策。文中方法可有效对多视角SAR图像样本进行相关性分析，并利用联合稀疏表示利用这种相关性，能够更好提高分类精度。采用MSTAR数据集对方法进行分析测试，通过与几类其他方法在多种测试条件下进行对比，结果显示了最大熵准则在多视角选取中的有效性和文中方法对SAR目标分类性能的优越性。

Multi-view SAR target classification method based on principle of maximum entropy

For the synthetic aperture radar (SAR) target classification method, a multi-view was developed based on the principle of the maximum entropy. The mutual-correlation matrix between multi-view SAR images was established based on the classical image correlation. Afterwards, the nonlinear correlation information entropy (NCIE) of different view sets was calculated. NCIE is capable of analyzing the statical properties of multiple variables and entropy value reflects the inner correlation of different variables. The view set with the highest nonlinear correlation information entropy was chosen, in which the multiple views share the highest correlation. The joint sparse representation was employed to represent the selected multi-view SAR images and the target label was determined based on the total reconstruction errors. The joint sparse representation is capable of handling several sparse representation problems and enhancing the reconstruction precision when these problems share some correlations. The proposed method could effectively analyze the inner correlations of multiple views and employ joint sparse representation to exploit such correlations so the classification accuracy can be improved. Typical experimental setups were designed based on the MSTAR dataset to test the performance of the proposed method while compared with some other methods under different test conditions. The results show the validity of the principle of the maximum entropy and the superior performance of the proposed method for SAR target classification.