Infrared Small Target Detection Based on Facet Kernel and Random Walker

Efficient detection of targets immersed in a complex background with a low signal-to-clutter ratio (SCR) is very important in infrared search and tracking (IRST) applications. In this paper, we address the target detection problem in terms of local image segmentation and propose a novel small target detection algorithm derived from facet kernel and random walker (RW) algorithm which includes four main stages. First, since the RW algorithm is suitable for images with less noises, local order-statistic and mean filtering are applied to remove the pixel-sized noises with high brightness (PNHB) and smooth the infrared images. Second, the infrared image is filtered by the facet kernel to enhance the target pixels and candidate target pixels are extracted by an adaptive threshold operation. Third, inspired by the properties of infrared targets, a novel local contrast descriptor (NLCD) based on the RW algorithm is proposed to achieve clutter suppression and target enhancement. Then, the candidate target pixels are selected as central pixels to construct the local regions and the NLCD map of all local regions is computed. The obtained NLCD map is weighted by the filtered map of facet kernel to further enhance target. Finally, the target is detected by a thresholding operation on the weighted map. Experimental results on three data sets show that the proposed method outperforms conventional baseline methods in terms of target detection accuracy.