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.

翻译：随着深度学习在各个领域的应用日益广泛，光学遥感图像中的显著目标检测（ORSI-SOD）引起了广泛关注。然而，现有大多数ORSI-SOD方法主要依赖低层特征的局部信息来推断显著性边界线索，并使用边界真值进行监督，但未能充分优化和保护局部信息，且几乎所有方法都忽略了解码器最后一层在维护显著图完整性方面潜在的优势。为解决这些问题，我们提出了一种名为边界-语义协同引导网络（BSCGNet）的新方法，该方法采用双流反馈机制。首先，我们提出了一种边界保护校准（BPC）模块，该模块在不依赖边界真值的情况下有效减少了前向传播过程中边缘位置信息的损失，并抑制了低层特征中的噪声。其次，基于BPC模块，我们提出了一个双特征反馈互补（DFFC）模块，该模块聚合了边界-语义双流特征，并通过有效反馈协调不同层之间的特征，从而增强跨尺度知识交流。最后，为获得更完整的显著图，我们首次考虑了解码器最后一层的特殊性，并提出了自适应反馈细化（AFR）模块，该模块通过独特的反馈机制进一步细化特征表示并消除特征间的差异。在三个基准数据集上的大量实验表明，BSCGNet在具有挑战性的场景中表现出明显优势，并优于近年来提出的17种最先进（SOTA）方法。代码和结果已在GitHub上发布：https://github.com/YUHsss/BSCGNet。