融合背景估计与相对局部对比度的天基短波红外弱小目标检测OA

To address the challenge of detecting dim and small targets in space-based short-wave infrared(SWIR)imagery-where targets are readily obscured by cloud cover and ground clutter under low signal-to-clutter ratio(SCR)conditions-an enhanced detection algorithm is proposed that integrates Anderson-accel-erated Self-Regularized Weighted Sparse(SRWS)modeling with the Relative Local Contrast Measure(RLCM).Computational complexity in background estimation is substantially reduced through the incor-poration of Anderson acceleration,while multi-scale target detection is achieved via background residual maps combined with RLCM.Experiments conducted on 289 SWIR images spanning seven representative scenarios demonstrate consistently strong performance in complex backgrounds,with the AUC reaching 0.950 and remaining no lower than 0.842 under the most challenging conditions.The signal-to-clutter ratio gain(SCRG)is significantly improved relative to conventional methods,including IPI and LCM.Overall,de-tection accuracy and robustness for dim and small targets in space-based SWIR remote sensing are effectively enhanced,providing a reliable solution for target detection in complex background environments.