SMILE: Spatial–Spectral Mamba Interactive Learning for Infrared Small Target Detection

Infrared (IR) search and track systems are widely applied in aerospace and defense fields. Infrared small target detection (IRSTD) in heavy clouds and chaotic terrestrial environments remains a challenging task. The semantic features of IR small targets are highly prone to vanishing with the addition of network layers. Transformer with quadratic computational complexity struggles for local feature refinement. To tackle this issue, we introduce a Mamba-driven approach dubbed Spatial- Spectral Mamba Interactive Learning (SMILE) network. Specifically, the perspective transformation structures heterogeneous backgrounds. The reconstructed data couples Mamba’s flattened multidirectional scanning mechanism. Given that small targets possess sparse and high-frequency properties, spatial Mamba and spectral Mamba collaboratively enrich the semantic features of small targets. The Dual-Path Aggregation (DPA) network is engineered to integrate the saliency and high-frequency attributes of small targets, effectively balancing detailed and contextual features without overwhelming the network. The Hybrid Representation Learning Module (HRLM) refines the local features to inscribe the intact edge structure. Both qualitative and quantitative experiments demonstrate that our proposed SMILE outperforms 14 recent benchmark algorithms on two public datasets.