Salient Object Detection in Optical Remote Sensing Images Based on Global Context Mixed Attention

Abstract

Optical remote sensing images exhibit complex characteristics such as high density, multiscale, and multi-angle features, posing significant challenges in the field of salient object detection. This academic exposition introduces an integrated model customized for the precise detection of salient objects in optical remote sensing images, presenting a comprehensive solution. At the core of this model lies a feature aggregation module based on the concept of hybrid attention. This module orchestrates the gradual fusion of multi-layer feature maps, thereby reducing information loss encountered during traversal of the inherent skip connections in the U-shaped architecture. Notably, this framework integrates a dual-channel attention mechanism, cleverly leveraging the spatial contours of salient regions within optical remote sensing images to enhance the efficiency of the proposed module. By implementing a hybrid loss function, the overall approach is further strengthened, facilitating multifaceted supervision during the network training phase, covering considerations at the pixel-level, region-level, and statistical levels. Through a series of comprehensive experiments, the effectiveness and robustness of the proposed method are validated, undergoing rigorous evaluation on two widely accessed benchmark datasets, meticulously catering to optical remote sensing scenarios. It is evident that our method exhibits certain advantages relative to other methods.