单演信号在红外图像目标分类中的应用研究

红外成像是夜间观测的重要手段，在军事民用领域都有着广泛运用。针对红外图像目标分类问题，将单演信号引入用于特征提取，用于对目标特性的分析。经过单演信号处理后的红外图像可用幅度、相位和方位三个成分描述。对于每一个成分的多尺度结果，采用矢量串接以及降采样结合的方式构建单一特征矢量。最终构造得到的三个特征矢量能够反映目标的多层次特性。采用联合稀疏表示作为三种单演信号特征矢量的表征模型。在重构过程中，充分利用三类特征之间的关联性从而提高整体重构精度。在不同类别上按照联合稀疏表示的求解结果计算对于测试样本的重构误差，进而决定测试样本的类别信息。该方法通过单演信号获取红外图像中目标的多层次特性，基于联合稀疏表示模型对这些特征进行充分分析和挖掘，从而提高目标分类的精度和稳健性。实验基于公开的中波红外（Medium wave infrared，MWIR）图像数据集开展，分别对原始样本、模拟噪声样本以及模拟遮挡样本进行分类。根据实验结果，并与几类现有算法对比，反映了所提方法对于红外图像目标分类问题能够取得更高的有效性和稳健性。

Research on monogenic signal of application in infrared imagery target classification

Infrared imaging is an important measure for night observation, which is widely used in both military and civil fields. For infrared imagery target classification problem, the monogenic signal was introduced for feature extraction, which was used to analyze the target characteristics. The infrared image after single signal processing can be described by the amplitude, phase and orientation components. For each component, its multi-scale decompositions were processed by connection and downsampling to achieve one single feature vector. Finally, three feature vectors were generated to describe the multi-layer properties of the target. The joint sparse representation was employed as the representation model for the three feature vectors, which used their correlation to improve the overall reconstruction precision. The reconstruction errors of different classes were calculated based on the results from joint sparse representation and the target label could be further decided. The experiments were conducted on the medium wave infrared (MWIR) image dataset to classify the original, noisy, and occluded samples. By comparison with several existed algorithms, the validity and robustness of the proposed method for infrared imagery target classification could be confirmed.