Infrared moving small target detection based on spatial-temporal local contrast under slow-moving cloud background

Abstract

It is intractable to detect infrared moving small targets under slow-moving clouds, because small targets are weak so that they may be submerged in clouds, in addition to cloud edges are difficult to distinguish from them. To overcome these obstacles, a robust spatial–temporal local contrast method for infrared moving small targets is proposed. Firstly, the spatial–temporal local energy change saliency (STLECS) map is designed to discriminate whether interframe local energy changes are caused by cloud slow-motion or target motion. In addition, a multi-directional interval minimum gradient (MDIMG) map is also constructed to measure the difference between the target and the background edge on the gradient distribution. To further enhance the saliency of moving targets in space–time domain, a fused spatial -temporal saliency (FSTS) map is obtained by combining the STLECS map and the MDIMG map, and an adaptive threshold is applied to this fused map to segment real targets. The performance experiment results based on real images show that the proposed method has the better performance compared to other state-of-the-art contrast-based methods.