Global Perception Network for Salient Object Detection in Remote Sensing Images
Yu Liu, Shanwen Zhang, Zhen Wang, Baoping Zhao, Lincheng Zou
Abstract
Despite recent works that have achieved remarkable progress on salient object detection for natural scene images, to detect various types and scales of objects, complex backgrounds in remote sensing images are still challenging. In this study, a novel global perception network (GPNet) is constructed for the salient object detection of remote sensing images. The proposed GPNet includes a global perception module (GPM), an axial attention block (AAB), and a feature distillation structure (FDS). The GPM is used to preserve the relationships of the entire dataset, the AAB is designed to capture the dependencies between the space and channel, the FDS is introduced to enable the helpful multilevel information flow into deep layers to enhance feature generation, and the global and the local attention information are mutually fused to enhance the network mode. Extensive experiments on three public datasets demonstrate that the proposed method outperforms other compared state-of-the-art methods both qualitatively and quantitatively (<uri>https://github.com/liuyu1002/GPnet</uri>).