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.