Infrared Small Target Detection Based on Adaptive Size Estimation by Multidirectional Gradient Filter

Abstract

The strong edge of bright clutter would produce a large number of false alarms, and it seriously debases the detection performance of infrared (IR) small targets. Existing IR small target detection algorithms often lack adaptability in complex scenes, and their performance heavily relies on initial parameter configurations, including target size, which is closely related to the characteristics of the background and target. This article proposes an IR small target detection algorithm based on adaptive size estimation by a multidirectional gradient filter to address these limitations. First, based on the Gaussian-like distribution of a small target, the multidirectional gradient filter is constructed to enhance the target in order to get the target characteristic map (TCM). Second, the size of the small target is estimated from the enhanced TCMs by the criteria of sinusoidal curve best fitting. Subsequently, a multidirectional morphological filter with the optimal estimated size is proposed to suppress the strong background clutter to obtain the local strength difference map (LSDM). Finally, the corresponding enhanced TCM with the optimal estimated target size is fused with the LSDM for threshold segmentation to further suppress the background and detect the small targets. A large number of experimental results show that the proposed method can not only accurately estimate target size but also effectively detect small targets in diverse, complex scenes.