Robust Surface Matching Research Articles

Rigorous 3D change determination in Antarctic Peninsula glaciers from stereo WorldView-2 and archival aerial imagery

This paper presents detailed elevation and volume analysis of 16 individual glaciers, grouped at four locations, spread across the Antarctic Peninsula (AP). The study makes use of newly available WorldView-2 satellite stereo imagery to exploit the previously untapped value of archival stereo aerial photography. High resolution photogrammetric digital elevation models (DEMs) are derived to determine three-dimensional glacier change over an unprecedented time span of six decades with an unparalleled mean areal coverage of 82% per glacier. The use of an in-house robust surface matching algorithm ensured rigorous alignment of the DEMs to overcome inherent problems associated with processing archival photography, most notably the identification and correction of scale error in some datasets. The analysis provides insight into one of the most challenging and data-scarce areas on the planet by expanding the spatial extent north of the AP to include previously un-studied glaciers located in the South Shetland Islands. 81% of glaciers studied showed considerable loss of volume over the period of record. The mean annual mass loss for all glaciers yielded 0.24±0.08m.w.e.peryear, with a maximum mass loss of up to 62m.w.e. and frontal retreat exceeding 2.2km for Stadium Glacier, located furthest north on Elephant Island. Observed volumetric loss was broadly, though not always, correlated with frontal retreat. The combined mass balance of all 16 glaciers yielded −1.862±0.006Gt, which corresponds to −0.005mm sea level equivalent (SLE) over the 57year observation period.

Robust surface matching by integrating edge segments

Abstract. This paper describes a novel area-based stereo-matching method which aims at reconstructing the shape of objects robustly, correctly, with high precision and with high density. Our goal is to reconstruct correctly the shape of the object by comprising also edges as part of the resulting surface. For this purpose, we need to overcome the problem of how to reconstruct and describe shapes with steep and sharp edges. Area-based matching methods set an image area as a template and search the corresponding match. As a direct consequence of this approach, it becomes not possible to correctly reconstruct the shape around steep edges. Moreover, in the same regions, discontinuities and discrepancies of the shape between the left and right stereo-images increase the difficulties for the matching process. In order to overcome these problems, we propose in this paper the approach of reconstructing the shape of objects by embedding reliable edge line segments into the area-based matching process with parallax estimation. We propose a robust stereo-matching (the extended Edge TIN-LSM) method which integrates edges and which is able to cope with differences in right and left image shape, brightness changes and occlusions. The method consists of the following three steps: (1) parallax estimation, (2) edge-matching, (3) edge-surface matching. In this paper, we describe and explain in detail the process of parallax estimation and the area-based surface-matching with integrated edges; the performance of the proposed method is also validated. The main advantage of this new method is its ability to reconstruct with high precision a 3D model of an object from only two images (for ex. measurement of a tire with 0.14 mm accuracy), thus without the need of a large number of images. For this reason, this approach is intrinsically simple and high-speed.

Assessment of Glacier Volume Change Using ASTER-Based Surface Matching of Historical Photography

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Glaciated regions are known to be particularly sensitive to climate change. Historical archives of glacier volume change are important, as they provide context for present-day changes. Although photogrammetric archives exist for many regions, their usefulness is often limited by a lack of contemporary ground control. High quality digital elevation models (DEMs) underpin a range of change analysis activities. This paper presents a cost-effective solution which utilizes Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs as control for the scaling and orientation of archival data sets. Instead of relying upon ground-control points, a robust surface matching algorithm is employed to automatically determine the transformation required to register two overlapping DEMs. Through application to the Slakbreen glacier system in Svalbard, Norway, the strategy is assessed by first matching an ASTER DEM to a fixed lidar reference surface. This demonstrates that ASTER DEMs are effectively correct in scale, supporting their use as a control surface. The second stage of the research implements this by matching an aerial photogrammetric DEM to an ASTER reference surface. Resultant volumetric and annual elevation change rates are compared to those derived from lidar data, which are considered in this paper as a truth data set. ASTER-based matching produced a mean annual elevation change rate of <formula formulatype="inline"><tex Notation="TeX">$-4.12\ \hbox{ma}^{-1}$ </tex></formula>, compared to a value of <formula formulatype="inline"><tex Notation="TeX">$-4.11\ \hbox{ma}^{-1}$</tex></formula> derived from the lidar data. In volumetric terms, this equates to a difference of 0.6%. A major advantage of this approach is the near-global coverage offered by ASTER data and the opportunity that this presents for remote glacial change analysis over regional extents. </para>

A robust surface matching technique for coastal geohazard assessment and management

Coastal change is a major issue in many regions of the world, and is often driven by geohazard processes such as landslides and rockfalls. Effective assessment of such phenomena is essential for successful management of coastal ecosystems, and is often reliant on GIS-based analysis. However, while it is crucial that multi-temporal datasets can be accurately registered to a common reference system, traditionally, the dynamic nature of the coastal environment has hampered this process. This paper presents a robust surface matching technique which overcomes the requirement for physical control points, and instead derives control directly from the DEM surfaces. Although surface matching procedures are well established, performance can be sub-optimal where the surfaces contain regions of difference, such as those associated with geohazard activity or vegetation effects. The crucial aspect of the least squares matching approach developed here, is the incorporation of a robust estimation function which allows the effects of surface discrepancies to be mitigated through outlier handling. Aerial photogrammetry is an established technique for coastal monitoring, and extensive archival collections exist. However, archival datasets are particularly affected by the difficulties associated with acquisition of ground control. Conversely, the maturing technique of airborne laser scanning is less influenced by such problems, and instead is capable of producing a high quality representation of coastal terrain. This paper describes the application of the robust surface matching technique to test sites located on the east coast of England. Photogrammetric DEMs are approximately oriented, before being matched to control surfaces derived from higher order datasets, including airborne laser scanning DEMs. The robust matching algorithm is shown to produce significantly improved results over ordinary surface matching. Analysis indicates the effectiveness of this technique for exploitation of archival datasets, revealing a signature of extensive geohazard activity over the twenty-five year study period. Robust matching of airborne laser scanning datasets has also enabled the quantification of short-term geohazard activity, demonstrating the flexibility of this strategy.

Curvature Computation on Free-Form 3-D Meshes at Multiple Scales

A novel technique for multiscale curvature computation on a smoothed 3-D surface is presented. This is achieved by iteratively convolving local parameterizations of the surface with 2-D Gaussian filters. In our technique, semigeodesic coordinates are constructed at each vertex of the mesh which becomes the local origin. A geodesic from the origin is first constructed in an arbitrary direction such as the direction of one of the incident edges. The smoothing eliminates surface noise and small surface detail gradually and results in gradual simplification of the object shape. The surface Gaussian and mean curvature values are estimated accurately at multiple scales together with curvature zero-crossing contours. The curvature values are then mapped to colors and displayed directly on the surface. Furthermore, maxima of Gaussian and mean curvatures are also located and displayed on the surface. These features have been utilized by later processes for robust surface matching and object recognition. Our technique is independent of the underlying triangulation and is also more efficient than volumetric diffusion techniques since 2-D rather than 3-D convolutions are employed. Another advantage is that it is applicable to incomplete surfaces which arise during occlusion or to surfaces with holes.

Curvature and torsion feature extraction from free-form 3-D meshes at multiple scales

A novel technique is presented for multi-scale curvature computation on a smoothed 3-D surface. This is achieved by convolving local parameterisations of the surface iteratively with 2-D Gaussian filters. In the technique, each vertex of the mesh becomes a local origin around which semi-geodesic co-ordinates are constructed. A geodesic from the origin is first constructed in an arbitrary direction, typically the direction of one of the incident edges. The smoothing eliminates surface noise and slowly erodes small surface detail, resulting in gradual simplification of the object shape. The surface Gaussian and mean curvature values are estimated accurately at multiple scales, together with curvature zero-crossing contours. For better visualisation, the curvature values are then mapped to colours and displayed directly on the surface. Furthermore local maxima of Gaussian and mean curvatures, as well as the torsion maxima of the zero-crossing contours of Gaussian and mean curvatures are also located and displayed on the surface. These features can be utilised by later processes for robust surface matching and object recognition. The technique is independent of the underlying triangulation and is more efficient than volumetric diffusion techniques since 2-D rather than 3-D convolutions are employed. Another advantage is that it is applicable to incomplete surfaces which arise during occlusion or to surfaces with holes.