Infrared Small Target Detection Based on Local Hypergraph Dissimilarity Measure

Abstract

Infrared (IR) small target detection against complex backgrounds is one of the most important tasks in infrared search and tracking systems. Achieving a high detection rate and a low false alarm rate against complex backgrounds remains challenging in practical applications. In this letter, we propose a novel small target detection method based on a local hypergraph dissimilarity measure (LHDM). As an alternative to the unstable dissimilarity in conventional simple graphs, a novel probabilistic hypergraph dissimilarity is presented to capture high-order affinity relationships among neighbors. Then, the corresponding LHDM in a local nested window is constructed based on the hypergraph model to enhance small targets and suppress complex backgrounds. The final saliency map is calculated via max-pooling of the LHDM at multiple scales. Finally, we utilize an adaptive threshold for target segmentation. The results of a series of experiments and evaluations performed on six real IR sequences demonstrate that the LHDM performs favorably compared to several baseline methods.