Infrared Small Target Detection by Modified Density Peaks Searching and Local Gray Difference

Abstract

Infrared small target detection is a challenging task with important applications in the field of remote sensing. The idea of density peaks searching for infrared small target detection has been proved to be effective. However, if high-brightness clutter is close to the target, the distance from the target pixel to the surrounding density peak will be very small, which easily leads to missing detection. In this paper, a new detection method, named modified density peaks searching and local gray difference (MDPS-LGD), is proposed. First, a local heterogeneity indicator is used as the density to suppress high-brightness clutter, and an iterative search is adopted to improve the efficiency in the process of searching for density peaks. Following this, a local feature descriptor named the local gray difference indicator (LGD) is proposed according to the local features of the target. In order to highlight the target, we extract the core area of the density peak by a random walker (RW) algorithm, and take the maximum response of the minimum gray difference element in the core region as the LGD of the density peak. Finally, targets are extracted using an adaptive threshold. Extensive experimental evaluation results in various real datasets demonstrate that our method outperforms state-of-the-art algorithms in both background suppression and target detection.