ASNet: Adaptive Semantic Network Based on Transformer–CNN for Salient Object Detection in Optical Remote Sensing Images
Ruixiang Yan, Longquan Yan, Guohua Geng, Yufei Cao, Pengbo Zhou, Yongle Meng
Abstract
Salient object detection in optical remote sensing images (RSI-SOD) has recently become a key area of research, driven by the unique challenges posed by the variability in remote sensing imagery. Traditional approaches, largely based on Convolutional Neural Networks (CNNs), are limited in handling the diverse scenarios of remote sensing due to their static network construction and reliance on local feature extraction. To tackle these limitations, we present the Adaptive Semantic Network (ASNet), a novel framework specifically designed for RSI-SOD. ASNet innovatively integrates Transformer and CNN technologies in a Dual Branch Encoder, which captures both global dependencies and local fine-grained image details. The network also features an Adaptive Semantic Matching Module (ASMM) for dynamically harmonizing filter responses to global and local contexts, an Adaptive Feature Enhancement Module (AFEM) that effectively enhances salient region features while restoring image resolution, and a Multi-scale Fine-grained Inference Module (MFIM) which refines high-level semantic features by integrating detailed low-level information, leading to the generation of precise, high-quality saliency maps. These components work in concert to adaptively respond to the complex nature of remote sensing images. Extensive experimental evaluations confirm that ASNet substantially outperforms existing models in the RSI-SOD task.