IEAM: Integrating Edge Enhancement and Attention Mechanism with Multi-Path Complementary Features for Salient Object Detection in Remote Sensing Images

Prominent target detection in optical remote sensing images (RSI-SOD) focuses on segmenting key targets that capture human attention. However, most SOD methods prioritize detection accuracy at the cost of memory. Complex backgrounds, occlusions, and noise distort segmented target boundaries, while large memory demands increase computational cost, and reduced memory impairs segmentation accuracy. To address these challenges, we integrate edge enhancement and attention mechanisms with multi-path complementary features for salient object detection in remote sensing images (IEAM), aiming to improve salient target accuracy, boundary detection, and memory efficiency. The architecture utilizes a structured feature fusion strategy, combining spatial channel attention mechanisms with adaptive merging to enhance multi-scale feature representation and suppress background noise. The Spatially Adaptive Edge Embedded Module (SAEM) refines object boundary perception, the SCAAP module dynamically selects relevant spatial and channel features while balancing adaptive and maximal pooling, and the Spatial Adaptive Guidance (SAG) module enhances feature localization in cluttered environments to mitigate semantic dilution in U-shaped networks. Extensive experiments on the EORSSD and ORSSD benchmark datasets demonstrate that IEAM outperforms 21 state-of-the-art methods, achieving an inference speed of 48 FPS at 103.2 G FLOP, making it suitable for real-time applications. The proposed model is robust and excels in multiple aspects.