Salient object detection in optical remote sensing image (ORSI-SOD) has gradually attracted attention thanks to the development of deep learning (DL) and salient object detection in natural scene image (NSI-SOD). However, NSI and ORSI are different in many aspects, such as large coverage, complex background, and large differences in target types and scales. Therefore, a new dedicated method is needed for ORSI-SOD. In addition, existing methods do not pay sufficient attention to the boundary of the object, and the completeness of the final saliency map still needs improvement. To address these issues, we propose a novel method called Dual Feedback Attention Framework via Boundary-Aware Auxiliary and Progressive Semantic Optimization (DFA-BASO). First, Boundary Protection Calibration (BPC) module is proposed to reduce the loss of edge position information during forward propagation and suppress noise in low-level features. Second, a Dual Feature Feedback Complementary (DFFC) module is proposed based on BPC module. It aggregates boundary-semantic dual features and provides effective feedback to coordinate features across different layers. Finally, a Strong Semantic Feedback Refinement (SSFR) module is proposed to obtain more complete saliency maps. This module further refines feature representation and eliminates feature differences through a unique feedback mechanism. Extensive experiments on two public datasets show that DFA-BASO outperforms 15 state-of-the-art methods. Furthermore, this paper strongly demonstrates the true contribution of DFA-BASO to ORSI-SOD by in-depth analysis of the visualization figure. All codes can be found at https://github.com/YUHsss/DFA-BASO.

翻译：光学遥感图像显著目标检测（ORSI-SOD）借助深度学习（DL）和自然场景图像显著目标检测（NSI-SOD）的发展逐渐受到关注。然而，自然场景图像（NSI）与光学遥感图像（ORSI）在诸多方面存在差异，例如覆盖范围广、背景复杂以及目标类型和尺度差异大等。因此，ORSI-SOD需要一种新的专用方法。此外，现有方法对目标边界的关注不足，最终显著性图的完整性仍有待提升。为解决这些问题，我们提出了一种名为基于边界感知辅助与渐进语义优化的双反馈注意力框架（DFA-BASO）的新方法。首先，提出边界保护校准（BPC）模块以减少前向传播过程中边缘位置信息的丢失，并抑制低层特征中的噪声。其次，基于BPC模块提出双特征反馈互补（DFFC）模块，该模块聚合边界-语义双特征，并通过有效反馈协调不同层间的特征。最后，提出强语义反馈细化（SSFR）模块以获取更完整的显著性图。该模块通过独特的反馈机制进一步细化特征表示，消除特征差异。在两个公开数据集上的大量实验表明，DFA-BASO优于15种最先进方法。此外，本文通过可视化图像的深入分析有力证明了DFA-BASO对ORSI-SOD的真实贡献。所有代码见https://github.com/YUHsss/DFA-BASO。