Multiple Infrared Small Targets Detection Based on Hierarchical Maximal Entropy Random Walk

Abstract

The technique of detecting multiple dim and small targets with low signal-to-clutter ratios (SCR) is essential for infrared search and tracking systems. In this letter, we establish a multiple small targets detection method derived from hierarchical maximal entropy random walk (HMERW). The HMERW revolves the limitation of strong bias to the most salient target of the primal maximal entropy random walk (MERW) based on a proposed graph decomposition theory. To enhance the characteristics of small targets and suppress strong clutters, we design a specific weight matrix for HMERW instead of using the conventional weight matrix in MERW. First, a stationary distribution map is obtained by importing the filtered infrared image into the HMERW. Second, a coefficient map is constructed based on the designed weight matrix to fuse the stationary distribution map. Then, an adaptive threshold is used to segment multiple small targets from the fusion map. Extensive experiments on practical datasets demonstrate that the proposed method is superior to the state-of-the-art methods in terms of target enhancement, background suppression, and multiple small targets detection.