Frequency Meets Semantics: Text-Visual Fusion with Directional Spectral Enhancement for Salient Object Detection in Optical Remote Sensing Images

Salient object detection in optical remote sensing images (ORSI-SOD) faces unique challenges due to complex backgrounds, diverse scales, and multi-directional objects. Existing methods primarily rely on visual features, often struggling to distinguish salient objects from visually similar backgrounds. To address this limitation, we leverage large language models (LLMs) to expend existing ORSI-SOD datasets with detailed textual annotations, creating a more comprehensive benchmark for image-text ORSI-SOD. Building upon this foundation, we propose the Frequency Meets Semantics Network (FMS-Net), a novel framework that integrates text-visual fusion with directional spectral enhancement for ORSI-SOD. FMS-Net consists of two key innovations: the Hierarchical Multi-Modal Dual-Channel Fusion (HMDF) module and the Adaptive Directional Spectral Enhancement (ADSE) module. The HMDF module enables bidirectional interactions between visual and textual features via parallel global-local attention mechanisms, progressively enriching visual representations with semantic context. Meanwhile, the ADSE module enhances feature representations in the frequency domain, capturing directional patterns and boundary details critical for accurate saliency detection. Extensive experiments on two public datasets, ORSSD and EORSSD, demonstrate that FMS-Net outperforms state-of-the-art methods, particularly in complex scenes with ambiguous boundaries. Our work paves the way for integrating multi-modal and frequency-based approaches in the interpretation of optical remote sensing images (ORSI).