  The technique of detecting multiple dim and small targets with low
signal-to-clutter ratios (SCR) is very important for infrared search and
tracking systems. In this paper, we establish a detection method derived from
maximal entropy random walk (MERW) to robustly detect multiple small targets.
Initially, we introduce the primal MERW and analyze the feasibility of applying
it to small target detection. However, the original weight matrix of the MERW
is sensitive to interferences. Therefore, a specific weight matrix is designed
for the MERW in principle of enhancing characteristics of small targets and
suppressing strong clutters. Moreover, the primal MERW has a critical
limitation of strong bias to the most salient small target. To achieve multiple
small targets detection, we develop a hierarchical version of the MERW method.
Based on the hierarchical MERW (HMERW), we propose a small target detection
method as follows. First, filtering technique is used to smooth the infrared
image. Second, an output map is obtained by importing the filtered image into
the HMERW. Then, a coefficient map is constructed to fuse the stationary
dirtribution map of the HMERW. Finally, an adaptive threshold is used to
segment multiple small targets from the fusion map. Extensive experiments on
practical data sets 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.
