ORSI Salient Object Detection via Progressive Interaction and Saliency-Guided Enhancement
Yunzuo Zhang, Tong Wang, Liye Xue, Weiqi Lian, Ran Tao
Abstract
Although great progress has been made in Salient Object Detection (SOD) in Optical Remote Sensing Images (ORSIs), it still faces critical challenges, particularly in dealing with irregular topological structures and complex contextual relationships. To address these issues, we propose a Progressive Interaction and Saliency-guided Enhancement Network (PISENet). Specifically, a Progressive Interaction Encoder (PIE) is proposed, which adopts a dual-path heterogeneous fusion architecture and a hierarchical progressive interaction mechanism to capture global irregular topological structures and local fine-grained image details. Meanwhile, it mitigates semantic gaps across multi-scale features and achieves effective cross-level feature fusion. Subsequently, a Global Context Enhancement Module (GCEM) is designed, which incorporates non-local blocks to enhance spatial correlations among features. A parallel multi-branch structure is further utilized to capture multi-level contextual information ranging from local details to long-range semantics, thereby strengthening the modeling of global context. Finally, a Multi-scale Progressive Attention Enhancement Decoder (MPAED) is devised, which adopts a saliency-guided attention mechanism to jointly model spatial and channel-wise dependencies, enhance responses in salient regions and boundaries, and progressively decode and aggregate deep semantic and shallow detailed features. Extensive experiments on three benchmark datasets demonstrate that our method achieves significant superiority over state-of-the-art approaches.