Multi-scale and Multi-dimensional Weighted Network for Salient Object Detection in Optical Remote Sensing Images

Recent advancements have significantly benefited in salient object detection for optical remote sensing images (ORSI-SOD). Given the varying spatial resolutions and complex scenes characteristic of optical remote sensing images (ORSI), leveraging and integrating features across scales is vital. However, excessive feature integration can introduce significant noise and result in inaccurate saliency mapping. To address this issue, we propose the Multi-scale and Multi-dimensional Weighted Network for ORSI-SOD (WeightNet). The network adopts a two-stage design where the first stage generates multi-scale weighted information, and the second stage conducts indirect multi-scale feature weighted fusion. This design skillfully avoids the lack of scale adaptation and noise interference that may arise from direct multi-scale feature fusion. Furthermore, to enhance feature fusion and target localization precision, we introduce the Multi-Scale Weighted Feature Aggregation Module (MWFAM) and the Multi-Dimensional Feature Guidance Module (MDFGM). MWFAM facilitates multi-scale feature fusion while minimizing noise from cross-layer interactions. MDFGM specializes in precise target localization and enhancement of detail and edge information. Additionally, the introduction of a Multi-Scale Parallel Decoder (MPD) significantly boosts the decoder's capability in identifying targets across various scales. Extensive qualitative and quantitative evaluations on three public ORSI datasets demonstrate the effectiveness and superiority of WeightNet over contemporary state-of-the-art models.