TSFANet: Trans-Mamba Hybrid Network with Semantic Feature Alignment for Remote Sensing Salient Object Detection

Recent advances in deep learning have witnessed the wide application of convolutional neural networks (CNNs), Transformer models, and Mamba models in optical remote sensing image (ORSI) analysis, particularly for salient object detection (SOD) tasks in disaster warning, urban planning, and military surveillance. Although existing methods improve detection accuracy by optimizing feature extraction and attention mechanisms, they still face limitations when dealing with the inherent challenges of ORSI. These challenges mainly manifest as complex backgrounds, extreme scale variations, and topological irregularities, which severely affect detection performance. However, the deeper underlying issue lies in how to effectively align and integrate local detail features with global semantic information. To tackle these issues, we propose the Trans-Mamba Hybrid Network with Semantic Feature Alignment (TSFANet), a novel architecture that exploits intrinsic correlations between semantic information and detail features. Our network comprises three key components: (1) a Trans-Mamba Semantic-Detail Dual-Stream Collaborative Module (TSDSM) that combines CNNs-Transformer and CNNs-Mamba in a hybrid dual-branch encoder to capture both global context and multi-scale local features; (2) an Adaptive Semantic Correlation Refinement Module (ASCRM) that leverages semantic-detail feature correlations for guided feature optimization; and 3) a Semantic-Guided Adjacent Feature Fusion Module (SGAFF) that aligns and refines multi-scale semantic features. Extensive experiments on three public RSI-SOD datasets demonstrate that our method consistently outperforms 30 state-of-the-art approaches, effectively accomplishing the task of salient object detection in remote sensing imagery.