The Epipolarity Constraint in Stereo-Radargrammetric DEM Generation

Abstract

For stereometric processing of optical image pairs, the concept of epipolar geometry is widely used. It helps to reduce the complexity of image matching, which can be seen to be the most crucial step within a workflow to generate digital elevation models. In this paper, it is shown that this concept is also applicable to the cocircular geometry of synthetic aperture radar (SAR) image pairs. First, it is proven that, for any feasible SAR acquisition, the deviation from true epipolar geometry is within subpixel range and therefore acceptably small. Based on this, we propose a method to create "epipolar" geometry for arbitrary stereo configurations of any SAR sensor through appropriate geometric image transformations. Consequently, the semiglobal matching (SGM) algorithm can be applied, which is restricted to epipolar geometry and is thus known to be highly efficient. This innovative approach, integrating both epipolar transformation and SGM, has been applied to a TerraSAR-X stereo data set. Its benefit has been demonstrated in a comparative assessment with respect to results, which have been previously achieved on the same test data using state-of-the-art stereometric methods.

Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

“…Geometric pre-processing is usually performed by fully automated image co-registration using sub-pixel image matching algorithms [56]. For radiometric pre-processing of time series data, there are two options: absolute and relative calibration.…”

A Tie Point Matching Strategy for Very High Resolution Sar-Optical Stereogrammety Over Urban Areas

“…Unfortunately, a rigorous epipolar-line does not exist for SAR stereo pairs (Gutjahr et al, 2014), and even less so for SAR-optical stereo pairs. Therefore, in this paper, we propose a similar search strategy, called Imaging-Model-based-Line-Shape (IMBLS) search window for SAR-optical image matching.…”

Assessment of the mapping potential of Pléiades stereo and triplet data

“…In the respective workflow (see chapter 5) accurate sensor models are a demand to generate strict epipolar image pairs (cf. Gutjahr et al, 2014) with mutually corresponding image lines. This is a pre-requisite to apply 1D matching algorithms, like the semiglobal matching approach (Hirschmüller et al, 2008).…”

“…While the concept of epipolar geometry was first realized for perspective images, appropriate implementations were further made for Pléiades-like pushbroom geometries (Wang et al, 2011) and for SAR geometries (Gutjahr et al, 2014). In either case the generation of epipolar image pairs is based on the underlying sensor geometries and relies on accurate sensor models in order to achieve pairs with strictly corresponding image lines.…”