CSFFNet: Lightweight cross-scale feature fusion network for salient object detection in remote sensing images

Salient object detection (SOD), one of the most important applications in the field of computer vision, aims to extract the most visually appealing regions of scenes. However, the improvement of the accuracy of existing salient object detection in optical remote sensing images (ORSI-SOD) is usually accompanied by an increase of network complexity, which affects the application of these models. Motivated by this, a novel lightweight edge-supervised neural network for ORSI-SOD is proposed, named CSFFNet. Specifically, the backbone (ResNet34) is first lightened by feature encoding module (FEM), building a lightweight subnet for feature extraction. Then, in the transformer-based feature pyramid enhancement module (FPEM), the convolutional features obtained in the FEM are enhanced by long-distance dependence to obtain multi-scale features containing rich saliency cues. Based on this, the feature fusion module (FFM) is designed to capture cross-scale long-range dependencies and effectively fuse high-level semantic information with low-level detail information. Thus, the increase in network complexity due to multi-level decoding is avoided. Finally, the segmentation results are optimized by using salient edges as auxiliary information, which effectively improves the contrast and completeness of the results. Experimental results on two public datasets demonstrate that the lightweight CSFFNet achieves competitive or even better performance compared with state-of-the-art methods. The proposed CSFFNet feeds the local feature representation of the CNN-based encoder into a feature pyramid module based on transformer to obtain global attention features. Meanwhile, we built a feature enhancement module to fuse features at different levels in a more refined way to achieve high-accuracy ORSI-SOD.image