Error detection and DEM fusion using self-consistency

Schultz, Howard; Riseman, Edward M.; Stolle, Frank; Woo, Dong-Min

doi:10.1109/iccv.1999.790413

Cited by 36 publications

(17 citation statements)

References 8 publications

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“…Area-based stereo is performed on synthetic images with Terrest system [12], which is a correlation-based 3D reconstruction system developed at University of Massachusetts and Myongji University. We employed multi-resolution scheme [5], referred to as hierarchical, or pyramid processing, to our Terrest system.…”

Section: Resultsmentioning

confidence: 99%

3D Line Segment Detection Based on Disparity Data of Area-Based Stereo

Woo

Park

2009

2009 WRI Global Congress on Intelligent Systems

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This paper presents a new 2D line matching technique in terms of disparity data generated by area-based stereo to build 3D site model from urban images. The core of our technique is that feature matching is carried out by the reference of the disparity evaluated by area-based stereo. Since the reference of the disparity can significantly reduce the number of feature matching combinations, feature matching error can be drastically minimized. One requirement of the disparity to be referenced is that it should be reliable to be used in feature matching. To measure the reliability of the disparity, in this paper, we employ the selfconsistency of the disparity. Our suggested technique is applied to the detection of 3D line segments by 2D line matching using our hybrid stereo matching, which can be efficiently utilized in the generation of the rooftop model from urban images. The suggested method is evaluated on Avenches data set of Ascona aerial images. Experimental results indicate that the extracted 3D line segments have an average error of 0.5m and can be efficiently used to the construction of 3D site models.

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Section: Resultsmentioning

confidence: 99%

3D Line Segment Detection Based on Disparity Data of Area-Based Stereo

Woo

Park

2009

2009 WRI Global Congress on Intelligent Systems

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“…These layover and shadow effects in SAR image cause severe impact on the quality of the reconstructed elevation values [5], [6]. There have been several studies on fusion of DEMs generated using different data sources [7]- [14]. Most of these studies have used the weighted averaging approach for DEM fusion with weight calculated from different methods.…”

Section: Introductionmentioning

confidence: 99%

Framework for Fusion of Ascending and Descending Pass TanDEM-X Raw DEMs

Deo

Rossi

Eineder

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A novel method for calculating optimum incidence angle for the TanDEM-X system using any available digital elevation model (DEM) for the given area is proposed in this study. This method includes the plotting of slopes and aspect of the test area in a statistical way and applying mathematical approach using acquisition geometry in ascending and descending pass TanDEM-X data to optimize the incidence angle for obtaining precise DEM. Furthermore, the TanDEM-X raw DEMs in ascending and descending pass over Mumbai, India are combined using a simple weighted fusion algorithm and the quality of fused DEM thus generated is enhanced. The method adopted for fusion is just an experimental study. The problem of optimum weight selection for fusion has been addressed using height error map and a robust layover shadow mask calculated in "Integrated TanDEM-X Processor" (ITP) during TanDEM-X DEM generation. The height error map is calculated from the interferometric coherence with geometrical considerations and the robust layover and shadow map is calculated using TanDEM-X DEM and the corresponding slant range. Results show a significant reduction in the number of invalid pixels after fusion. In the fused DEM, invalids are only 2.14%, while ascending and descending pass DEMs have 5.02% and 6.34%, respectively. Statistical analysis shows a slight improvement in standard deviation of the error in fused DEM by 8% in urban area and about 5% for the whole scene. Only slight improvement in accuracy of fused DEM can be attributed to the coarse resolution of the SRTM-X DEM used as reference.Index Terms-Fusion, layover, optimum incidence angle, TanDEM-X digital elevation model (DEM), weightage.

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“…DSM fusion has been considered by various authors previously. The simplest method is based on weighted averaging of two or more height maps (Schultz et al, 1999), (Reinartz et al, 2005). As weighted averaging cannot deal with outliers or blunders in the DSMs, a median fusion is often used for multi-DSM fusion, sometimes followed by weighted averaging of the inliers (Hirschmueller, 2008).…”

Section: Introductionmentioning

confidence: 99%

Fusion of Multi-Resolution Digital Surface Models

Kuschk

d’Angelo

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper proposes an algorithm for fusing digital surface models (DSM) obtained by heterogenous sensors. Based upon prior confidence knowledge, each DSM can be weighted locally adaptively and therefore strengthen or lessen its influence on the fused result. The proposed algorithm is based on variational methods of first and second order, minimizing a global energy functional comprising of a data term forcing the resulting DSM being similar to all of the input height information and incorporating additional local smoothness constraints. By applying these additional constraints in the form of favoring low gradients in the spatial direction, the surface model is forced to be locally smooth and in contrast to simple mean or median based fusion of the height information, this global formulation of context-awareness reduced the noise level of the result significantly. Minimization of the global energy functional is done with respect to the L1 norm and therefore is robust to large height differences in the data, which preserves sharp edges and fine details in the fused surface model, which again simple mean-and median-based methods are not able to do in comparable quality. Due to the convexity of the framed energy functional, the solution furthermore is guaranteed to converge towards the global energy minimum. The accuracy of the algorithms and the quality of the resulting fused surface models is evaluated using synthetic datasets and real world spaceborne datasets from different optical satellite sensors.

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Error detection and DEM fusion using self-consistency

Cited by 36 publications

References 8 publications

3D Line Segment Detection Based on Disparity Data of Area-Based Stereo

3D Line Segment Detection Based on Disparity Data of Area-Based Stereo

Framework for Fusion of Ascending and Descending Pass TanDEM-X Raw DEMs

Fusion of Multi-Resolution Digital Surface Models

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