Despite significant advancements in salient object detection(SOD) in optical remote sensing images(ORSI), challenges persist due to the intricate edge structures of ORSIs and the complexity of their contextual relationships. Current deep learning approaches encounter difficulties in accurately identifying boundary features and lack efficiency in collaboratively modeling the foreground and background by leveraging contextual features. To address these challenges, we propose a stronger multifaceted collaborative salient object detector in ORSIs, termed LBA-MCNet, which incorporates aspects of localization, balance, and affinity. The network focuses on accurately locating targets, balancing detailed features, and modeling image-level global context information. Specifically, we design the Edge Feature Adaptive Balancing and Adjusting(EFABA) module for precise edge localization, using edge features to guide attention to boundaries and preserve spatial details. Moreover, we design the Global Distributed Affinity Learning(GDAL) module to model global context. It captures global context by generating an affinity map from the encoders final layer, ensuring effective modeling of global patterns. Additionally, deep supervision during deconvolution further enhances feature representation. Finally, we compared with 28 state of the art approaches on three publicly available datasets. The results clearly demonstrate the superiority of our method.

翻译：尽管光学遥感图像中的显著目标检测已取得显著进展，但由于遥感图像边缘结构复杂及其上下文关系错综，挑战依然存在。当前深度学习方法在准确识别边界特征方面存在困难，且缺乏利用上下文特征协同建模前景与背景的效率。为应对这些挑战，我们提出一种更强大的光学遥感图像多面协作显著目标检测器，称为LBA-MCNet，其融合了定位、平衡与亲和力三个维度。该网络专注于精确定位目标、平衡细节特征以及建模图像级全局上下文信息。具体而言，我们设计了边缘特征自适应平衡调整模块以实现精确的边缘定位，利用边缘特征引导对边界的关注并保留空间细节。此外，我们设计了全局分布式亲和力学习模块以建模全局上下文，通过从编码器最终层生成亲和力图来捕获全局上下文，确保对全局模式的有效建模。同时，反卷积过程中的深度监督进一步增强了特征表示能力。最后，我们在三个公开数据集上与28种先进方法进行了比较，结果清晰证明了本方法的优越性。