SP-KAN: Sparse-sine perception Kolmogorov–Arnold networks for infrared small target detection

Abstract

Infrared small target detection (IRSTD) plays a critical role in diverse complex remote sensing scenarios. However, existing IRSTD methods struggle to discriminate dim targets that are heavily entangled with complex interference due to their fixed activation representations. To tackle this issue, we reformulate IRSTD as a global context modulation problem driven by sparse nonlinear modules and propose a Sparse-sine Perception Kolmogorov–Arnold Network (SP-KAN). It marks a novel attempt to leverage the superior nonlinear capability of the Kolmogorov–Arnold theory for robust IRSTD. Specifically, a compressed vision transformer encoder is first employed to capture long-range spatial dependencie..