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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
SK Cokriging 
Class Variogram Variogr am of Marlogram of Covariogram 
primary variable secondary variable 
Open water 0.0007 Sph(30) 0.001 Sph(30) 0.0003 Sph(30) 0.0003 Sph(30) 
Forest 0.046 Exp(21) 0.044 Exp(58) 0.011 Exp(60) 0.005 Gau(250) 
Grassland/Shrub 0.012 Gau(37) 0.122 Exp(44) 0.018 Sph(50) 0.008 Exp(100) 
Barren/Sand 0.130 Gau(53) 0.130 Exp(80) 0.011 Gau(65) 0.005 Gau(180) 
Cropland 0.056 Gau(90) 0.056 Exp(430) 0.0009 Exp(75) 0.0002 Exp(100) 
Wetland 0.019 Exp(92) 0.019 Gau(100) 0.001 Gau(200) 0.0007 Gau(100) 
Table 1. Variograms and covariograms of SK and cokriging 
Method SVM Classifie Cokriging Simple Kriging with Local Mean 
Class Producer’s User’s Producer’s User’s Producer’s User's 
Accuray Accuray Accuracy Accuracy Accuracy Accuracy 
Open water 37.29 53.14 24.41 48.65 31.86 50.81 
Forest 78.95 84.17 76.52 81.59 71.95 83.12 
Grassland/Shrub 57.36 66.33 58.86 64.91 61.29 64.47 
Barren/Sand 91.96 74.51 86.25 82.76 87.53 81.82 
Cropland 15.92 59.75 73.28 76.11 72.80 73.93 
Wetland 10.32 41.80 35.98 20.51 27.16 54.83 
Overall Accuracy 73.79 75.77 77.02 
Kappa Coefficient 0.58 0.63 0.65 
  
Table 2. Accuracy Assessment Indexes of SVM Classification and Two Kriging Methods (Accuracy Unit: %) 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
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(c) Classified as farmland; revised as farmland 
Figure 3. The Probabilities of the SVM Classifier and the SK Method (e.g., Farmland) 
On the whole, Figures 3(a)-(c) reflect the effect of the SK 
method, i.e., it utilizes the information of spatial distribution 
provided by training samples to improve the posterior 
probabilities pertaining to the target land cover type and 
accordingly reduce those pertaining to the confusing types. 
Take Figure 3(a) for example. Given the testing samples with 
(d) Classified as farmland; revised as non-farmland 
ground truth as farmland, the predicted posterior probabilities of 
farmland are less than those of other confusing types, which 
results in omission. However, after the residual corrections, the 
probabilities of farmland are improved with a relative 
probability decrease of other types, which may also be reflected 
by the producer’s accuracy in Table 2. In addition, as is shown