Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network

Zhiwei Ye,Chuan Xu,Wei Yang,Hongli Li,Haigang Sui,Chang Liu

doi:10.3390/rs14040838

Abstract

Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions.

Highlights

Accepted: 7 February 2022Aviation and space-based remote sensing technology has been applied in many fields due to its advantages of macroscopic, rapid, and accurate object recognition [1]
This paper proposes a joint description neuraldesigned networkto match multiview satellite and Compared with some traditional methods, the designed to match multiview satellite and unmanned aerial vehicle (UAV) images
Compared with other image matching algorithms based on the grayscale, the normalized cross-correlation (NCC) algorithm has been proven to be the best approach for similarity evaluation, so the NCC algorithm has been widely used

Summary

Introduction

Aviation and space-based remote sensing technology has been applied in many fields due to its advantages of macroscopic, rapid, and accurate object recognition [1]. Image matching technology refers to mapping an image to other images obtained under different conditions, such as different time phases, angles, and levels of illumination, through spatial transformation and the establishment of spatial correspondence relations among these images. It is the key technology of image processing and analysis and provides technical support for medical image analysis, industrial image detection, remote sensing image processing, and other fields [6].

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