A General Framework of Remote Sensing Epipolar Image Generation

Xuanqi Wang,Yuming Xiang,Hongjian You,Feng Wang

doi:10.3390/rs13224539

Abstract

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.

Highlights

With the rapid development of high-resolution remote sensing sensors, digital stereo image data with large coverage, high precision, and timeliness can be obtained.Each pair of overlapping images forms a stereoscopic observation of the ground target, allowing the estimation of its 3D position
It can be found that the vertical disparity of the generated epipolar image pair is approximately negligible, which means that satisfactory results can be obtained for areas with large terrain elevation changes that do not satisfy the affine transformation model
We proposed a framework for generating epipolar images based on digital elevation model (DEM)

Summary

Introduction

With the rapid development of high-resolution remote sensing sensors, digital stereo image data with large coverage, high precision, and timeliness can be obtained.Each pair of overlapping images forms a stereoscopic observation of the ground target, allowing the estimation of its 3D position. With the rapid development of high-resolution remote sensing sensors, digital stereo image data with large coverage, high precision, and timeliness can be obtained. An important application of 3D information processing is the extraction of the digital surface model (DSM). In addition to terrain elevation, DSM contains the elevation information of ground objects such as buildings, bridges, and trees, and is widely used in various fields. Dense matching (stereo matching) is a key technology in DSM generation, and it has an important constraint, the epipolar geometry constraint. Using epipolar lines to retrieve conjugate points in image pairs can improve the efficiency and accuracy of stereo matching and it is a development trend to generate high-quality epipolar images from original satellite stereo image pairs [1]. Optical satellite images are mostly obtained by linear array pushbroom imaging technology.

Methods

Results

Discussion

Conclusion