Transcending Pixels: Boosting Saliency Detection via Scene Understanding From Aerial Imagery

Existing remote sensing image salient object detection (RSI-SOD) methods widely perform object-level semantic understanding with pixel-level supervision, but ignore the image-level scene information. As a fundamental attribute of remote sensing images (RSIs), the scene has a complex intrinsic correlation with salient objects, which may bring hints to improve saliency detection performance. However, existing RSI-SOD datasets lack both pixel- and image-level labels, and it is non-trivial to effectively transfer the scene domain knowledge for more accurate saliency localization. To address these challenges, we first annotate the image-level scene labels of three RSI-SOD datasets inspired by remote sensing scene classification. On top of it, we present a novel scene-guided dual-branch network (SDNet), which can perform cross-task knowledge distillation from the scene classification to facilitate accurate saliency detection. Specifically, a scene knowledge transfer module (SKTM) and a conditional dynamic guidance module (CDGM) are designed for extracting saliency key area as spatial attention from the scene subnet and guiding the saliency subnet to generate scene-enhanced saliency features, respectively. Finally, an object contour awareness module (OCAM) is introduced to enable the model to focus more on irregular spatial details of salient objects from the complicated background. Extensive experiments reveal that our SDNet outperforms over 20 state-of-the-art algorithms on three datasets. Moreover, we prove that the proposed framework is model-agnostic, and its extension to six baselines can bring significant performance benefits. Code is available at
https://github.com/lyf0801/SDNet
.