International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 | Interna 
  
  
  
  
  
  
  
  
  
  
    
  
Data Metrics 00:18:08 -138.04 154.02 -1.069 20.638 | 
Local Polynomial 00:25:24 -161.73 149.95 -0.113 20.133 | 
From statistics that above the form can get, all the aspect of mean error, are impracticable margin | 
methods use the time most longer is Radial Basis for error of Polynomial Regression biger outside, the | 
Function, and the shortest Polynomial Regression rest of worth and all very small. | 
discrepancy very big, especially at the standard error With the scope of error margin worth for horizontal | 
aspect, every kind of method have the obvious sit the mark, and the quantity is for vertical sit to | 
margin as well, its inside than is interesting of is, at mark as follows of histogram: | 
| | | | | 
| | | 
| | | | 
| > | 
| | 
ee re rer etre creme i ca 5 x = dE EON ns 3 
Local Polynomial Inverse Distance to a Power Kriging 
  
fem EST Fall 
man 11 7 Bm MÀ 
  
  
Natural Neighbor 
  
Ss p 
| 
  
nn 
pe 
140.661 
  
  
Nearest Neighbor Polynomial Regression Radial Basis Function | 
  
histogram 
  
  
hetogeam. 
  
  
  
  
  
  
  
  
tn ii -77 6953 113162 ^ | 
Triangulation with Linear Moving Average Data Metrics 
Interpolation 
Top each statistical chart of form in, put the Regression, can then obviously find its distributing 
method due to each interpolation horizontal sit to the appearance not good, the rest sketch all make the 
mark the scope all different, therefore can't directly rule symmetry to distribute. 
judge mutually the density of its distribute the 
difference, but in the statistical chart of Polynomial 
784