A General Framework of Remote Sensing Epipolar Image Generation

Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xuanqi Wang, Feng Wang, Yuming Xiang, Hongjian You
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	DEM epipolar constraint RPCs epipolar image DSM stereo matching Science Q
Acceso en línea:	https://doaj.org/article/12d427201c994be697c696905bb92eb6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:12d427201c994be697c696905bb92eb6
record_format	dspace
spelling	oai:doaj.org-article:12d427201c994be697c696905bb92eb62021-11-25T18:54:04ZA General Framework of Remote Sensing Epipolar Image Generation10.3390/rs132245392072-4292https://doaj.org/article/12d427201c994be697c696905bb92eb62021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4539https://doaj.org/toc/2072-4292Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM.Xuanqi WangFeng WangYuming XiangHongjian YouMDPI AGarticleDEMepipolar constraintRPCsepipolar imageDSMstereo matchingScienceQENRemote Sensing, Vol 13, Iss 4539, p 4539 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	DEM epipolar constraint RPCs epipolar image DSM stereo matching Science Q
spellingShingle	DEM epipolar constraint RPCs epipolar image DSM stereo matching Science Q Xuanqi Wang Feng Wang Yuming Xiang Hongjian You A General Framework of Remote Sensing Epipolar Image Generation
description	Epipolar images can improve the efficiency and accuracy of dense matching by restricting the search range of correspondences from 2-D to 1-D, which play an important role in 3-D reconstruction. As most of the satellite images in archives are incidental collections, which do not have rigorous stereo properties, in this paper, we propose a general framework to generate epipolar images for both in-track and cross-track stereo images. We first investigate the theoretical epipolar constraints of single-sensor and multi-sensor images and then introduce the proposed framework in detail. Considering large elevation changes in mountain areas, the publicly available digital elevation model (DEM) is applied to reduce the initial offsets of two stereo images. The left image is projected into the image coordinate system of the right image using the rational polynomial coefficients (RPCs). By dividing the raw images into several blocks, the epipolar images of each block are parallel generated through a robust feature matching method and fundamental matrix estimation, in which way, the horizontal disparity can be drastically reduced while maintaining negligible vertical disparity for epipolar blocks. Then, stereo matching using the epipolar blocks can be easily implemented and the forward intersection method is used to generate the digital surface model (DSM). Experimental results on several in-track and cross-track images, including optical-optical, SAR-SAR, and SAR-optical pairs, demonstrate the effectiveness of the proposed framework, which not only has obvious advantages in mountain areas with large elevation changes but also can generate high-quality epipolar images for flat areas. The generated epipolar images of a ZiYuan-3 pair in Songshan are further utilized to produce a high-precision DSM.
format	article
author	Xuanqi Wang Feng Wang Yuming Xiang Hongjian You
author_facet	Xuanqi Wang Feng Wang Yuming Xiang Hongjian You
author_sort	Xuanqi Wang
title	A General Framework of Remote Sensing Epipolar Image Generation
title_short	A General Framework of Remote Sensing Epipolar Image Generation
title_full	A General Framework of Remote Sensing Epipolar Image Generation
title_fullStr	A General Framework of Remote Sensing Epipolar Image Generation
title_full_unstemmed	A General Framework of Remote Sensing Epipolar Image Generation
title_sort	general framework of remote sensing epipolar image generation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/12d427201c994be697c696905bb92eb6
work_keys_str_mv	AT xuanqiwang ageneralframeworkofremotesensingepipolarimagegeneration AT fengwang ageneralframeworkofremotesensingepipolarimagegeneration AT yumingxiang ageneralframeworkofremotesensingepipolarimagegeneration AT hongjianyou ageneralframeworkofremotesensingepipolarimagegeneration AT xuanqiwang generalframeworkofremotesensingepipolarimagegeneration AT fengwang generalframeworkofremotesensingepipolarimagegeneration AT yumingxiang generalframeworkofremotesensingepipolarimagegeneration AT hongjianyou generalframeworkofremotesensingepipolarimagegeneration
_version_	1718410571580178432

A General Framework of Remote Sensing Epipolar Image Generation

Ejemplares similares