1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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Remotely Sensed Topographic Data for River Channel Research 129 Table 6.3 The correct camera calibration parameters as compared to camera parameters with error simulated under a normal distribution as defined by camera parameter standard errors obtained from the bundle adjustment Image 1 Image 2 Correct Simulated Difference Correct Simulated Difference Xo (m) 283909.26 283909.12 −0.147 283483.46 283483.35 −0.105 Yo (m) 716100.37 716100.33 −0.037 716013.21 716013.13 −0.075 Xo (m) 615.53 615.35 −0.187 615.53 615.53 0.000 omega 0.1970 0.1942 −0.0029 0.2507 0.2464 −0.004 (degrees) kappa 10.5958 10.5985 0.0026 9.7322 9.7287 −0.0035 (degrees) phi (degrees) 0.3520 0.3442 −0.0078 0.3988 0.3985 −0.0003 wherever a grid cell fell across a break of slope. Assuming the stereo-matching process is operating properly, there is an equal probability of the photogrammetric data points being too high (where the grid cell is predominantly dry) or too low (where the grid cell is predominantly wet). The results in Table 6.3 suggest that the errors are systematic. The second hypothesis relates to the operation of the stereo-matching process. Breaks of slope will have an effect upon the viewing angle of the two images. Using imagery of a similar scale and breaks of slope associated with a landform of similar relative relief, Lane et al. (2000) showed that the stereo-correlating algorithm had a tendency to produce erroneous matches due to dead ground problems. In summary, this demonstrates that determination of the spatial distribution of error is effective in helping to understand the causes of that error, and where error is most heavily concentrated. The analysis described in section 6.4.1 could potentially be improved through a more intelligent approach to error correction, in which image information content (e.g. water edges) is used to inform the error search process. However, the finding that errors are greatest at the water edges raises issues of surface representation, as edge data will be required to capture breaks of slope adequately. The selective nature of the point removal in section 6.4.1, rather than usinga1mchannel edge exclusion zone, is clearly preferable. 6.5.2 Causes of the Banding Initial inspection of the banding suggested that this was probably associated with error in the triangulation. To assess the extent to which this could be the case, a simple simulation exercise was undertaken (Figure 6.9(a)). This involved taking a flat DEM, similar in platform extent to an individual DEM. The associated matrix of object space co-ordinates was then applied to the collinearity equations used to generate that DEM. This produced a set of correct image space co-ordinates. The correct image space co-ordinates were then re-applied to the collinearity equations but with camera positions and orientations perturbed under a normal distribution according to the SD of each parameter defined during triangulation. This produces a DEM containing simulated error due to random parameter perturbation. In addition, it is possible to simulate the effects of matching upon the DEM. We do this by adding a random perturbation to the image co-ordinates to reflect the non-perfect matching process. This is defined as having a SD of 1 image pixel.
130 Spatial Modelling of the Terrestrial Environment Figure 6.9 The simulation exercise used to assess the effects of random error in camera parameters upon an idealized planar DEM (a) and the results of simulation of DEM error due to random perturbation of camera positions and orientations and the effects of random error in the stereo-matching process (b)
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Spatial Modelling of the Terrestria
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Copyright C○ 2004 John Wiley & So
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Contents List of Contributors Prefa
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Contributors Jonathan L. Bamber Sch
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List of Contributors xi George Perr
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xiv Preface in theory and applicati
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2 Spatial Modelling of the Terrestr
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Editorial: Spatial Modelling in Hyd
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Editorial: Spatial Modelling in Hyd
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2 Modelling Ice Sheet Dynamics with
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Modelling Ice Sheet Dynamics by Sat
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36 Spatial Modelling of the Terrest
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4 Using Coupled Land Surface and Mi
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Coupled Land Surface and Microwave
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Page 86 and 87: Coupled Land Surface and Microwave
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Page 116 and 117: Editorial: Terrestrial Sediment and
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Page 120 and 121: 6 Remotely Sensed Topographic Data
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Page 166 and 167: Near Real-Time Modelling of Regiona
Page 176 and 177: High Low TSM Concentration Figure 8
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Fireline Intensity and Biomass Cons
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Figure 9.4 The upper row shows EOS
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PART III SPATIAL MODELLING OF URBAN
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200 Spatial Modelling of the Terres
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11 Modelling the Impact of Traffic
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Modelling the Impact of Traffic Emi
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PART IV CURRENT CHALLENGES AND FUTU
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268 Index Bi-spectral InfraRed Dete
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270 Index floodplain maps, UK 92 fl
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272 Index Long-Term Ecological Rese
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274 Index snow depth measurement ne
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276 Index urban land use in remotel
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1 Spatial Modelling of the Terrestrial Environment - Georeferencial

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