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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 4; p. 838
• Main Authors: Xu, Chuan, Liu, Chang, Li, Hongli, Ye, Zhiwei, Sui, Haigang, Yang, Wei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Algorithms; area; Aviation; Deep learning; Descriptions; exhibitions; image analysis; image matching; Immersion plating; joint description; Matching; Methods; monitoring; multiview; neural network; Neural networks; Pixels; problem solving; Radiation; Remote sensing; satellite and UAV image; Satellite imagery; Satellites; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14040838

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.