Local–Global Information Perception Network for Salient Object Detection in Optical Remote Sensing Images

In the field of salient object detection (SOD), optical remote sensing images (ORSI) differ significantly from natural sensing images (NSI). Existing research in ORSI-based SOD is constrained by the limitations of convolutional neural networks (CNNs) in feature extraction and by the underutilization of feature information in Transformer-based approaches. To address these challenges, this paper presents a Transformer-based Local-Global Information Perception Network (LGIPNet) for ORSI, which enhances encoder-generated features at multiple levels to highlight salient targets through three specialized feature enhancement modules. The Edge Adaptive Enhancement Module (EAEM) focuses on extracting local edge features to guide precise edge generation. The Multi-scale Grouped Weighted Attention Module (MGWAM) extracts local information from low-level features, scales features, and uses multi-scale channel and learnable weighted spatial attention to locate salient targets. For high-level features, the Dual-Domain Attention Module (DDAM) integrates a Channel Enhancement Attention Block (CEA) and an Adaptive Spatial Attention Block (ASA) to refine both local and global information. Specifically, the EAEM sharpens the edges of salient objects, thereby ensuring the precision of boundary detection. The MGWAM, on the other hand, enriches the feature representation across multiple scales, enhancing the network’s capability to encapsulate both fine-grained details and broader contextual information. The DDAM further strengthens the balance between local and global information, preserving feature integrity across levels. Finally, multi-scale features are cascaded to produce the final saliency map. This holistic strategy empowers LGIPNet to accurately identify and emphasize salient objects in ORSI. Experiments on three datasets demonstrate that LGIPNet outperforms existing state-of-the-art methods, establishing its effectiveness and robustness in ORSI-based SOD. The source code is available at https://github.com/sCauliflower/LGIPNet.git.