Infrared point target detection based on multiscale homogeneous feature fusion

Abstract

In view of the difficult detection of infrared dim point target from complex backgrounds, a multiscale homogeneous feature (MHF) based multispectral image fusion detection method is proposed in this paper. Inspired by the local contrast measure (LCM), we extract two local statistical features from the perspective of the homogeneity of gray difference distribution to characterize local structure of the infrared point target. Based on these two local features, we obtain the MHF map that can effectively highlight the potential point targets and suppress the backgrounds simultaneously. For the pixel-size electronic noise (PSEN) and some similar local structures to the point target, the multispectral image fusion detection is a positive way to alleviate these interferences and promote the robustness of the dim point target detection. Experimental results on six real scenarios and synthetic scenarios demonstrate that the proposed method not only works more stably for different target sizes and brightness, but also can achieve superior detection performance compared with the state-of-art detection methods.