Robust estimation applied to surface matching

Pilgrim, Lloyd J

doi:10.1016/0924-2716(96)00010-x

Cited by 34 publications

(19 citation statements)

References 6 publications

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“…This is true, even where vegetation has been removed through filtering algorithms, as there will always be residual effects due to the differing and inconsistent vegetation penetration properties of these two techniques. The introduction of local surface discrepancies will influence the estimation of the transformation parameters, and where the effects are significant, conventional least squares approaches may fail, or may converge to an erroneous solution (Li et al, 2001;Pilgrim, 1996). Although minor differences between the surfaces can be tolerated, the assumption that the surfaces are overwhelmingly similar is critical for attaining a successful solution.…”

Section: Introductionmentioning

confidence: 99%

“…The main complication relates to the fact that in the context presented here, the surfaces to be matched will never be identical. As Pilgrim (1996) observes, differences can arise for a number of reasons, including as a result of time-induced changes, or where the surfaces have been captured through different techniques. With reference to the former situation, geohazard activity, or vegetation change, has the potential to introduce significant discrepancies to the matching surfaces, while the latter situation is also of great relevance, as the techniques of ALS and photogrammetry are likely to return differing surface representations, particularly with regards to vegetation representation.…”

Section: Introductionmentioning

confidence: 99%

“…However, this approach is only capable of removing a single outlier at a time. Pilgrim (1996) has carried out investigations into overcoming outlier contamination through the development of a robust form of least squares surface matching. This incorporates a weighting function based on a modified maximum likelihood estimator (M-estimator), which identifies, and down-weights high-residual observations which can effectively be considered as outliers in the matching.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A robust surface matching technique for coastal geohazard assessment and management

Miller

Mills

Edwards

et al. 2008

ISPRS Journal of Photogrammetry and Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A robust surface matching technique for coastal geohazard assessment and management

Miller

Mills

Edwards

et al. 2008

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The LS3D method fully considers the 3D information by evaluating the Euclidean distances, while the 2.5D surface matching algorithms (Ebner and Strunz, 1988;Rosenholm and Torlegard, 1988;Karras and Petsa, 1993;Pilgrim, 1996;Mitchell and Chadwick, 1999 …”

Section: Discussionmentioning

confidence: 99%

Co-registration of Surfaces by 3D Least Squares Matching

Akça¹

2010

photogramm eng remote sensing

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A method for the automatic co-registration of 3D IntroductionWith the availability of the various sensors and automated methods, the production of large numbers of point clouds is no longer particularly notable. In many cases, the object of interest is covered by a number of point clouds, which are referenced in different spatial or temporal datums. Therefore, the issue of co-registration of point clouds (or surfaces) is an essential topic in 3D modeling.In terrestrial laser scanning practice, special targets provided by the vendors, e.g., ZollerϩFröhlich, Leica, and Riegl, are mostly used for the co-registration of point clouds. However, such a strategy has several deficiencies with respect to fieldwork time, personnel and equipment costs, and accuracy. In a recent study, Sternberg et al. (2004) reported that registration and geodetic measurements comprise 10 to 20 percent of the total project time. In another study, a collapsed 1,000-car parking garage was documented in order to assess the damage and structural soundness of the building. The laser scanning took three days, while the conventional survey of the control points required two days (Greaves, 2005). In a project conducted by our research group at Pinchango Alto (Lambers et al., 2007), two persons set the targets in the field and measured them using the real-time kinematic GPS technique in one and one-half days.As well as fieldwork time, accuracy is another important concern. The target-based registration methods may not exploit the full accuracy potential of the data. The geodetic measurements naturally introduce some error, which might exceed the internal error of the scanner instrument. In PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSINGM a r c h 2 0 1 0 307

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“…The Least Z-Difference (LZD) [Rosenholm and Torelegård, 1988;Zhang, 1994] is often employed for its relative higher efficiency, and many deformation detection methods are reported. By adopting the M-estimator, Pilgrim [1996] proposed the M-LZD algorithm, which can detect no more than 25% deformation. Li et al [2001] obtained the LMS-LZD algorithm by using the least median of squares (LMS) estimator.…”

Section: Introductionmentioning

confidence: 99%

A novel method for improved DEM deformation detection

Zhang

Cen

Ren

2015

European Journal of Remote Sensing

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The deformation detection method without control points is one of the key techniques for multi-temporal digital elevation model (DEM) analyses, and represents an attractive and difficult research topic. A novel method for improved DEM deformation detection is proposed in this paper, based on the Least Z-Difference algorithm with differential model (DM-LZD). In the new method, an additional parameter is employed to improve the weighting function for the observations in the matching algorithm. Three indexes are designed to give an in-depth and quantitative analysis of the performance, according to the possible two types of errors occurring in the weighting function. The experimental result, based on the simulated dataset, shows that with an appropriate additional parameter it will achieve a better balance between the deformation-detecting ability and the matchingaccuracy, and so generates better performance.

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Robust estimation applied to surface matching

Cited by 34 publications

References 6 publications

A robust surface matching technique for coastal geohazard assessment and management

A robust surface matching technique for coastal geohazard assessment and management

Co-registration of Surfaces by 3D Least Squares Matching

A novel method for improved DEM deformation detection

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