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Tsz-Yam Lau, You Li, Zhongyi Xie, W. Randolph Franklin Rensselaer Polytechnic Institute Troy, NY, USA ACM SIGSPATIAL GIS 2009, Seattle.

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Presentation on theme: "Tsz-Yam Lau, You Li, Zhongyi Xie, W. Randolph Franklin Rensselaer Polytechnic Institute Troy, NY, USA ACM SIGSPATIAL GIS 2009, Seattle."— Presentation transcript:

1 Tsz-Yam Lau, You Li, Zhongyi Xie, W. Randolph Franklin Rensselaer Polytechnic Institute Troy, NY, USA ACM SIGSPATIAL GIS 2009, Seattle

2 SHIP TRACKLINES INVERSE DISTANCE WEIGHTING Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 2

3 SHIP TRACKLINES KRIGING Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 3

4 SHIP TRACKLINES VORONOI Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 4

5 SHIP TRACKLINES LINEAR SPLINE Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 5

6 SHIP TRACKLINES QUADRATIC SPLINE Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 6

7  Sparse overdetermined linear system.  R trades accuracy vs smoothness.  Limited precision data:  Too many resulting surface details not valid, but people like details. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 7 R=1

8  Sparse overdetermined linear system.  R trades accuracy vs smoothness.  Limited precision data:  Too many resulting surface details not valid, but people like details. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 8 R=5

9  Sparse overdetermined linear system.  R trades accuracy vs smoothness.  Limited precision data:  Too many resulting surface details not valid, but people like details. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 9 R=10

10  Sparse overdetermined linear system.  R trades accuracy vs smoothness.  Limited precision data:  Too many resulting surface details not valid, but people like details. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 10 R=20

11  Sparse overdetermined linear system.  R trades accuracy vs smoothness.  Limited precision data:  Too many resulting surface details not valid, but people like details. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 11 R=30

12  Problem of the closest known points being widely varying distance away  Solution: weight equations for different points differently  Surface shows details with fewer artifacts. Lau, Li, Xie, Franklin - Bathymetry Trackline Fitting - ACM SIGSPATIAL GIS 2009 12

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