# Remote Sensing Target Tracking Method Based on Super-Resolution Reconstruction and Hybrid Networks

## TL;DR

This paper introduces a new method for tracking targets in remote sensing images using super-resolution and hybrid networks to improve accuracy.

## Contribution

The novel contribution is a hybrid network method combining super-resolution reconstruction and motion estimation for improved remote sensing target tracking.

## Key findings

- The proposed method achieves a tracking accuracy of 67.8%.
- The recognition identification F-measure (IDF1) reaches 0.636.
- The method outperforms traditional tracking algorithms in remote sensing.

## Abstract

Remote sensing images have the characteristics of high complexity, being easily distorted, and having large-scale variations. Moreover, the motion of remote sensing targets usually has nonlinear features, and existing target tracking methods based on remote sensing data cannot accurately track remote sensing targets. And obtaining high-resolution images by optimizing algorithms will save a lot of costs. Aiming at the problem of large tracking errors in remote sensing target tracking by current tracking algorithms, this paper proposes a target tracking method combined with a super-resolution hybrid network. Firstly, this method utilizes the super-resolution reconstruction network to improve the resolution of remote sensing images. Then, the hybrid neural network is used to estimate the target motion after target detection. Finally, identity matching is completed through the Hungarian algorithm. The experimental results show that the tracking accuracy of this method is 67.8%, and the recognition identification F-measure (IDF1) value is 0.636. Its performance indicators are better than those of traditional target tracking algorithms, and it can meet the requirements for accurate tracking of remote sensing targets.