LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching

Local features extraction is a crucial technology for image matching navigation of an unmanned aerial vehicle (UAV), where it aims to accurately and robustly match a real-time image and a geo-referenced image to obtain the position update information of the UAV. However, it is a challenging task due...

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Autores principales:	Xupei Zhang, Zhanzhuang He, Zhong Ma, Zhongxi Wang, Li Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	image feature extraction scene matching visible light and infrared image UAV and satellite imagery UAV vision-based navigation Science Q
Acceso en línea:	https://doaj.org/article/a770f329f4e646028c6698ea8a5d237d
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id	oai:doaj.org-article:a770f329f4e646028c6698ea8a5d237d
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spelling	oai:doaj.org-article:a770f329f4e646028c6698ea8a5d237d2021-11-25T18:54:47ZLLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching10.3390/rs132246182072-4292https://doaj.org/article/a770f329f4e646028c6698ea8a5d237d2021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4618https://doaj.org/toc/2072-4292Local features extraction is a crucial technology for image matching navigation of an unmanned aerial vehicle (UAV), where it aims to accurately and robustly match a real-time image and a geo-referenced image to obtain the position update information of the UAV. However, it is a challenging task due to the inconsistent image capture conditions, which will lead to extreme appearance changes, especially the different imaging principle between an infrared image and RGB image. In addition, the sparsity and labeling complexity of existing public datasets hinder the development of learning-based methods in this research area. This paper proposes a novel learning local features extraction method, which uses local features extracted by deep neural network to find the correspondence features on the satellite RGB reference image and real-time infrared image. First, we propose a single convolution neural network that simultaneously extracts dense local features and their corresponding descriptors. This network combines the advantages of a high repeatability local feature detector and high reliability local feature descriptors to match the reference image and real-time image with extreme appearance changes. Second, to make full use of the sparse dataset, an iterative training scheme is proposed to automatically generate the high-quality corresponding features for algorithm training. During the scheme, the dense correspondences are automatically extracted, and the geometric constraints are added to continuously improve the quality of them. With these improvements, the proposed method achieves state-of-the-art performance for infrared aerial (UAV captured) image and satellite reference image, which shows 4–6% performance improvements in precision, recall, and F1-score, compared to the other methods. Moreover, the applied experiment results show its potential and effectiveness on localization for UAVs navigation and trajectory reconstruction application.Xupei ZhangZhanzhuang HeZhong MaZhongxi WangLi WangMDPI AGarticleimage feature extractionscene matchingvisible light and infrared imageUAV and satellite imageryUAV vision-based navigationScienceQENRemote Sensing, Vol 13, Iss 4618, p 4618 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image feature extraction scene matching visible light and infrared image UAV and satellite imagery UAV vision-based navigation Science Q
spellingShingle	image feature extraction scene matching visible light and infrared image UAV and satellite imagery UAV vision-based navigation Science Q Xupei Zhang Zhanzhuang He Zhong Ma Zhongxi Wang Li Wang LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
description	Local features extraction is a crucial technology for image matching navigation of an unmanned aerial vehicle (UAV), where it aims to accurately and robustly match a real-time image and a geo-referenced image to obtain the position update information of the UAV. However, it is a challenging task due to the inconsistent image capture conditions, which will lead to extreme appearance changes, especially the different imaging principle between an infrared image and RGB image. In addition, the sparsity and labeling complexity of existing public datasets hinder the development of learning-based methods in this research area. This paper proposes a novel learning local features extraction method, which uses local features extracted by deep neural network to find the correspondence features on the satellite RGB reference image and real-time infrared image. First, we propose a single convolution neural network that simultaneously extracts dense local features and their corresponding descriptors. This network combines the advantages of a high repeatability local feature detector and high reliability local feature descriptors to match the reference image and real-time image with extreme appearance changes. Second, to make full use of the sparse dataset, an iterative training scheme is proposed to automatically generate the high-quality corresponding features for algorithm training. During the scheme, the dense correspondences are automatically extracted, and the geometric constraints are added to continuously improve the quality of them. With these improvements, the proposed method achieves state-of-the-art performance for infrared aerial (UAV captured) image and satellite reference image, which shows 4–6% performance improvements in precision, recall, and F1-score, compared to the other methods. Moreover, the applied experiment results show its potential and effectiveness on localization for UAVs navigation and trajectory reconstruction application.
format	article
author	Xupei Zhang Zhanzhuang He Zhong Ma Zhongxi Wang Li Wang
author_facet	Xupei Zhang Zhanzhuang He Zhong Ma Zhongxi Wang Li Wang
author_sort	Xupei Zhang
title	LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
title_short	LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
title_full	LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
title_fullStr	LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
title_full_unstemmed	LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching
title_sort	llfe: a novel learning local features extraction for uav navigation based on infrared aerial image and satellite reference image matching
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a770f329f4e646028c6698ea8a5d237d
work_keys_str_mv	AT xupeizhang llfeanovellearninglocalfeaturesextractionforuavnavigationbasedoninfraredaerialimageandsatellitereferenceimagematching AT zhanzhuanghe llfeanovellearninglocalfeaturesextractionforuavnavigationbasedoninfraredaerialimageandsatellitereferenceimagematching AT zhongma llfeanovellearninglocalfeaturesextractionforuavnavigationbasedoninfraredaerialimageandsatellitereferenceimagematching AT zhongxiwang llfeanovellearninglocalfeaturesextractionforuavnavigationbasedoninfraredaerialimageandsatellitereferenceimagematching AT liwang llfeanovellearninglocalfeaturesextractionforuavnavigationbasedoninfraredaerialimageandsatellitereferenceimagematching
_version_	1718410545345855488

LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching

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