Improvement of Remote Sensing Target Tracking Method Based on Deep Learnin

Abstract Remote Sensing Object Tracking refers to the process of detecting, recognizing, and tracking targets on the ground or at sea using remote sensing technology, particularly sensors mounted on satellite or aerial platforms to obtain high-resolution remote sensing image sequences. Current methods for remote sensing object tracking face challenges such as low tracking success rates and inefficiencies. This paper proposes a neural network for remote sensing object tracking based on SiamRPN++, which introduces an improved network structure incorporating the C3Minus module and a coordinate attention mechanism within the backbone extraction network. Furthermore, we design a feature extraction module, ResSwinT, that combines ResNet and Swin Transformer architectures to integrate local and global information obtained from feature maps as foundational features. This approach effectively addresses the aforementioned issues, and quantitative experiments demonstrate an increase in accuracy and success rates by 1.9% and 4.7%, respectively, indicating that our method effectively handles object tracking in remote sensing images.