Unified piecewise epipolar resampling method for pushbroom satellite images

Jin-Woo Koh,Hyun-Seung Yang

doi:10.1186/s13640-016-0112-y

Abstract

Computational stereo is in the fields of computer vision and photogrammetry. In the computational stereo and surface reconstruction paradigms, it is very important to achieve appropriate epipolar constraints during the camera-modeling step of the stereo image processing. It has been shown that the epipolar geometry of linear pushbroom imagery has a hyperbola-like shape because of the non-coplanarity of the line of sight vectors. Several studies have been conducted to generate resampled epipolar image pairs from linear pushbroom satellites images; however, the currently prevailing methods are limited by their pixel scales, skewed axis angles, or disproportionality between x-parallax disparities and height. In this paper, a practical and unified piecewise epipolar resampling method is proposed to generate stereo image pairs with zero y-parallax, a square pixel scale, and proportionality between x-parallax disparity and height. Furthermore, four criteria are suggested for performance evaluations of the prevailing methods, and experimental results of the method are presented based on the suggested criteria. The proposed method is shown to be equal to or an improvement upon the prevailing methods.

Highlights

The paradigm of computational stereo [1] is that it proceeds following six major steps: image acquisition, camera modeling, feature acquisition, image matching, distance or depth determination, and interpolation
Wang et al [4] suggest a method that implements a new epipolarity model based on the projection reference plane (PRP); this is an object space-based approach, and experimental results showed that the vertical parallaxes all attained sub-pixel levels in along-track and cross-track stereo images from actual equipment, including Earth observation satellites SPOT-5, IKONOS, IRS-P5, and QuickBird
As is apparent from the figure, the slope of the line indicated by the 152 matched points is 1.53544 and negligible differences between actual ground heights and those computed from the xparallax disparity

Summary

Introduction

The paradigm of computational stereo [1] is that it proceeds following six major steps: image acquisition, camera modeling, feature acquisition, image matching, distance or depth determination, and interpolation. We suggest four performance evaluation criteria for epipolar resampling algorithms: (1) geopositioning accuracy of the resampled epipolar images relative to the original sensor model, (2) square pixel scale and perpendicularity of axis in object space, (3) near zero y-parallax in epipolar image space, and (4) proportionality between the disparity in the x-parallax and the ground height Using these criteria, performance evaluations of the previously suggested algorithms and the algorithm proposed in this paper are presented based on experimental data from satellite images, including optical images such as those from IKONOS, Pleiades, and WorldView, as well as synthetic aperture radar (SAR) images as those from KOMPSAT-5 and TerraSAR-X

Previous studies

Determine a starting control point

Extract image control points on piecewise epipolar curve

Relocate the extracted points to epipolar image space

Conclusions