Direction-aware Attention and Semantic Guidance Network for Salient Object Detection in Optical Remote Sensing Images

Yifei Teng, Zhaoru Guo, Yaqian Wang, Liejun Wang, Panpan Zheng

Abstract: Salient object detection in optical remote sensing images (ORSI-SOD) aims to automatically identify the most visually prominent objects or regions in remote sensing images. However, due to the diverse orientations and varying scales of salient objects, as well as cluttered backgrounds, it remains a challenging task. To tackle these issues, we propose a direction-aware attention and semantic guidance network (DASGNet), a novel framework designed to enhance sensitivity to both orientation and multi-scale information while improving the depiction of boundaries in complex scenes. DASGNet integrates two key modules: a multi-scale direction-aware attention module (MDAM) and a semantic-guided edge reconstruction module (SERM). MDAM combines the attention mechanism with orientation information, effectively suppressing redundant information while capturing multi-scale orientation features. SERM employs 3D convolution to construct a stereoscopic receptive field, facilitating the integration of high-level semantic information across scales to guide the reconstruction of low-level texture information and thereby achieving precise edge delineation, particularly in complex scenes. Extensive experiments on three benchmark datasets demonstrate that DASGNet outperforms 14 state-of-the-art methods, achieving significant improvements in both accuracy and precision.

External IDs:dblp:conf/mir/TengGWWZ25