Existing methods for Salient Object Detection in Optical Remote Sensing Images (ORSI-SOD) mainly adopt Convolutional Neural Networks (CNNs) as the backbone, such as VGG and ResNet. Since CNNs can only extract features within certain receptive fields, most ORSI-SOD methods generally follow the local-to-contextual paradigm. In this paper, we propose a novel Global Extraction Local Exploration Network (GeleNet) for ORSI-SOD following the global-to-local paradigm. Specifically, GeleNet first adopts a transformer backbone to generate four-level feature embeddings with global long-range dependencies. Then, GeleNet employs a Direction-aware Shuffle Weighted Spatial Attention Module (D-SWSAM) and its simplified version (SWSAM) to enhance local interactions, and a Knowledge Transfer Module (KTM) to further enhance cross-level contextual interactions. D-SWSAM comprehensively perceives the orientation information in the lowest-level features through directional convolutions to adapt to various orientations of salient objects in ORSIs, and effectively enhances the details of salient objects with an improved attention mechanism. SWSAM discards the direction-aware part of D-SWSAM to focus on localizing salient objects in the highest-level features. KTM models the contextual correlation knowledge of two middle-level features of different scales based on the self-attention mechanism, and transfers the knowledge to the raw features to generate more discriminative features. Finally, a saliency predictor is used to generate the saliency map based on the outputs of the above three modules. Extensive experiments on three public datasets demonstrate that the proposed GeleNet outperforms relevant state-of-the-art methods. The code and results of our method are available at https://github.com/MathLee/GeleNet.

基于全局到局部范式，提出了一种用于光学遥感图像中显著目标检测的全局提取局部探索网络 (GeleNet)。GeleNet首先采用Transformer骨干网络生成四级特征嵌入以捕获全局远程依赖性。然后，GeleNet利用方向感知的混洗加权空间注意力模块 (D-SWSAM) 和其简化版本 (SWSAM) 增强本地交互，并利用知识传输模块 (KTM) 进一步增强跨级上下文交互。最后，根据上述三个模块的输出，使用显著性预测器生成显著性图。对三个公共数据集进行的广泛实验证明所提出的GeleNet优于相关最先进方法。

基于Transformer的光学遥感图像显著目标检测