  With the increasing application of deep learning in various domains, salient
object detection in optical remote sensing images (ORSI-SOD) has attracted
significant attention. However, most existing ORSI-SOD methods predominantly
rely on local information from low-level features to infer salient boundary
cues and supervise them using boundary ground truth, but fail to sufficiently
optimize and protect the local information, and almost all approaches ignore
the potential advantages offered by the last layer of the decoder to maintain
the integrity of saliency maps. To address these issues, we propose a novel
method named boundary-semantic collaborative guidance network (BSCGNet) with
dual-stream feedback mechanism. First, we propose a boundary protection
calibration (BPC) module, which effectively reduces the loss of edge position
information during forward propagation and suppresses noise in low-level
features without relying on boundary ground truth. Second, based on the BPC
module, a dual feature feedback complementary (DFFC) module is proposed, which
aggregates boundary-semantic dual features and provides effective feedback to
coordinate features across different layers, thereby enhancing cross-scale
knowledge communication. Finally, to obtain more complete saliency maps, we
consider the uniqueness of the last layer of the decoder for the first time and
propose the adaptive feedback refinement (AFR) module, which further refines
feature representation and eliminates differences between features through a
unique feedback mechanism. Extensive experiments on three benchmark datasets
demonstrate that BSCGNet exhibits distinct advantages in challenging scenarios
and outperforms the 17 state-of-the-art (SOTA) approaches proposed in recent
years. Codes and results have been released on GitHub:
https://github.com/YUHsss/BSCGNet.
