Independent Component Analysis-Based Infrared Dim and Small Target Detection

Infrared (IR) dim and small target detection is crucial in various military and civilian fields but faces challenges in complex environments and low signal-to-noise ratio. Considering the limitations of the existing methods in capturing the characteristics of different components, this article proposes an IR small target detection method based on frequency data sphering (FDS) and independent component analysis (FICA-TD). Specifically, for the yet-to-be-detected IR frame, we construct a corresponding spatial-temporal tensor with its adjacent frames. Then, we suppress the low-frequency information with a designed frequency high-pass filtering operator and a FDS operator. Subsequently, we develop independent component analysis (ICA) theory for determining the number of signal sources and the basis of independent component space $\Omega _{\mathbf {IC}}$ , followed by projecting the frequency data-sphered tensor ${\mathcal {D}}^{\text {sp}}$ into the constructed independent component space. Furthermore, as for the achieved independent component tensor $\mathcal {I}$ , a sparsity information filter (SIF) is developed to filter out the residual low-response information of the middle frontal slice. Finally, the target detection result sequence can be obtained. Comprehensive experiments and analysis on five real IR sequences demonstrate that FICA-TD is superior to seven state-of-the-art competitive methods in terms of target detectability (TD), background suppressibility, and overall performance.