Cross-Scale Hybrid Gaussian Attention Network for Object Detection in Remote Sensing Images

Abstract

Accurate object detection in remote sensing images (RSIs) is of great significance for various applications such as environmental monitoring and agricultural production. However, it is a challenging task mainly due to the complex backgrounds and scale diversity of geospatial objects. In this letter, a Cross-Scale Hybrid Gaussian Attention Network (CSHGANet) is proposed for accurate object detection in RSIs, and it consists of two main components as follows. First, hybrid Gaussian attention is designed to learn the interrelationships between channels and spatial locations of features, which can focus on geospatial objects and reduce the interference of complex backgrounds in RSIs. Then, a cross-scale feature aggregation module is developed to adaptively fuse multi-scale attention feature maps to capture more rich and discriminative feature representations, so as to better handle scale variations of remote sensing objects. Extensive experiments on two public datasets (i.e., NWPU VHR-10 and RSOD) show that the proposed CSHGANet outperforms state-of-the-art object detection methods, achieving mean average precision (mAP) scores of 95.53% and 98.61%, respectively.