Low-Rank Approximation and Multiple Sparse Constraint Modeling for Infrared Low-Flying Fixed-Wing UAV Detection

Infrared dim small target detection is one of the important contents in the research of military applications such as remote sensing intelligence reconnaissance, long-range precision strike, aerospace offense–defense confrontation, etc. In this article, we focus on the detection of low-flying fixed-wing unmanned aerial vehicle target based on infrared imaging. To this end, we propose a simple and effective detection model, which can be viewed as a combination of low-rank approximation and multiple sparse constraints. We first model the infrared image that to be detected as a sum of three patch matrices called background, target, and noise. Then, we put a nonconvex low-rank approximation on the background patch matrix to suppress the background edges and put a reweighted
$l_{1,1}$
-norm constraint on the target matrix to better preserve the dim small target. Moreover, in order to eliminate the strong residual edges left in the target image under complex background, both the
$l_{1,1}$
matrix norm and the
$l_{2,1}$
matrix norm are used to constrain the noise patch. Finally, we develop an alternating optimization algorithm to solve the associated minimization problem. Extensive experiments carried out on a recently released real low-flying UAV database show that the proposed approach works well in detecting infrared dim small target measured by qualitative analysis and quantitative analysis.