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The technique of detecting multiple dim and small targets with lowsignal-to-clutter ratios (SCR) is very important for infrared search andtracking systems. In this paper, we establish a detection method derived frommaximal entropy random walk (MERW) to robustly detect multiple small targets.Initially, we introduce the primal MERW and analyze the feasibility of applyingit to small target detection. However, the original weight matrix of the MERWis sensitive to interferences. Therefore, a specific weight matrix is designedfor the MERW in principle of enhancing characteristics of small targets andsuppressing strong clutters. Moreover, the primal MERW has a criticallimitation of strong bias to the most salient small target. To achieve multiplesmall targets detection, we develop a hierarchical version of the MERW method.Based on the hierarchical MERW (HMERW), we propose a small target detectionmethod as follows. First, filtering technique is used to smooth the infraredimage. Second, an output map is obtained by importing the filtered image intothe HMERW. Then, a coefficient map is constructed to fuse the stationarydirtribution map of the HMERW. Finally, an adaptive threshold is used tosegment multiple small targets from the fusion map. Extensive experiments onpractical data sets demonstrate that the proposed method is superior to thestate-of-the-art methods in terms of target enhancement, background suppressionand multiple small targets detection.

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