Progressive Feature Interleaved Fusion Network for Remote-Sensing Image Salient Object Detection

Salient object detection (SOD) has made significant strides in natural scene images (NSIs) in the span of the past few decades. However, extending these approaches for remote-sensing images (RSIs) faces challenges due to their complex backgrounds, complicated edges, irregular topology, and multiscale object variations, which hinder performance. Existing RSI-SOD techniques are unable to accurately detect salient objects while preserving detailed boundaries, and their computational inefficiency limits their practicality. To overcome these challenges, we entail the development of a progressive feature interleaved framework (PROFILE) in RSI-SOD. In particular, we leverage the interleaved association of the convolutional neural network (CNN) and Transformer (IACTer) to obtain global semantic relations and spatial details. To handle object scale variation, we design a lightweight plug-and-play multiscale hierarchical channel-spatial collaborative feature enhancement module (MHCCF), which can boost the representation of features regarding the relevant region, while identifying the precise location details about the salient region. Finally, a bi-directional consistency constraint module (BCCM) is developed, which can be integrated into the training of arbitrary SOD and segmentation networks to efficiently locate salient regions with refined structures and clear demarcations. Experiments demonstrate that our PROFILE surpasses 20 cutting-edge SOD methods, proving its ability to enhance the accuracy and integrity of SOD in complex backgrounds, such as illumination and shadows.