结合多特征融合和极限学习机的红外图像目标分类方法

针对红外图像目标分类问题，提出了结合多特征融合和极限学习机（extreme learning machine，ELM）的方法。采用主成分分析（principal component analysis，PCA）、局部二值模式（local binary pattern，LBP）以及尺度不变特征变换（scale-invariant feature transform，SIFT）三类特征分别描述红外图像中目标的像素分布、局部纹理以及特征点信息。三类特征从不同侧面反映红外图像目标特性，因此具有互为补充的优势。在此基础上，基于多重集典型相关分析（multiset canonical correlations analysis，MCCA）对三类特征进行融合处理，获得统一的特征矢量。融合后的特征不仅继承了原始三类特征的鉴别特性，还有效去除了冗余信息。分类过程中，采用极限学习机作为基础分类器对融合特征矢量进行分类。极限学习机具有参数少、效率高、精度高和稳健性强等显著特点，有利于提高红外目标分类的整体性能。因此，所提出的方法通过结合多特征和极限学习机的优势综合提升了目标识别性能。在实验过程中，采用四类飞机目标的红外图像对所提出方法进行了性能测试。根据与现有几类方法的对比，实验结果证明了提出方法的性能优势。

Target classification method in infrared images via combination of multi-feature fusion and extreme learning machine

For the problem of infrared image target classification, a method combining multi-feature fusion and extreme learning machine (ELM) was proposed. Three types of features, i.e., principal component analysis (PCA), local binary pattern (LBP) and scale-invariant feature transform (SIFT) were used to describe the pixel distribution, local texture and feature point information of the target in the infrared image. The three types of features reflected the characteristics of infrared image targets from different aspects, so they had complementary advantages. Afterwards, the three types of features were fused based on multiset canonical correlations analysis (MCCA) to obtain a unified feature vector. The fused features not only inherited the distinguishing characteristics of the original three types of features, but also effectively removed redundant information. In the classification process, The ELM was used as a basic classifier to classify the fused feature vector. ELM had the obvious characteristics of few parameters, high efficiency, high precision and strong robustness, so it was helpful to improve the overall performance of infrared target classification. Therefore, the proposed method comprehensively improved the target recognition performance by combining the advantages of multiple features and ELM. During the experiment, the infrared images of four types of aircraft targets were used to test the performance of the proposed method. According to the comparison with several existing methods, the experimental results prove the performance advantages of the proposed method.