  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.
