Phase Congruency-Guided Cross-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images

In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced (PCE) Module to solve the problem of low contrast between targets and backgrounds. It acquires multi-scale phase features via Fourier decomposition, fuses them with Transformer shallow features and uses a tailored loss weighting mechanism to weight phase congruency learning for better PCE module adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention and residual connections to refine multi-scale features and enables the model to accurately capture effective target features under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks show that PCFNet outperforms 24 state-of the-art methods in core metrics, and ablation studies further confirm the effectiveness of each module.