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ERS-2 images. TMEC is sharper than TMEB. However, the differences in contrast, saturation and sharpness between 
TMEC and TMEB are less pronounced than between TMJC and TMJB. 
The previously described effects of topography on the SAR radiometry, layover and shadow are transmitted to the fused 
images since the Landsat TM intensity is substituted by the SAR intensity. For the layover effect, this is clear if Figures 
1c (ERS-2), 1e (TMEB) and 1g (TMEC) are compared. The very bright areas (related to layover) on the ERS-2 image 
are also very bright on the fused images, independently on which type of transformation is used. 
Visual comparison of the areas that corresponds to the Landsat TM cloud gaps on the fused images, indicated a clear 
superiority of the IHS cylindrical transformation over the Brovey transformation on the replacement of the information 
lost due to cloud cover. The cloud shadow areas (dark on the Landsat TM) appear with a dark purple tone on the Brovey 
transformed images. However, on the IHS cilindrical transformed images the same areas have a tonality that is similar 
to the areas where the cloud shadow is not present. 
The classification using one forest class was performed to determine whether the data fusion can improve land cover 
classification accuracy for the study area if compared with the single Landsat TM. Table 3 summarizes the results of the 
Gaussian maximum-likelihood classification using one forest class. The user's, producer's and overall accuracies for 
each image (single and fused) were obtained from the error matrices. Moreover, the calculated Kappa coefficients and 
Kappa variances are indicated. 
The classified Landsat TM 95 (TM951) presented an overall accuracy of 82.1%, with a Kappa coefficient of 0.618. The 
producer’s accuracy achieved for the forest class is considerably higher (84.6% of the pixels were correctly classified) 
that the one achieved by the Eucalyptus plantation class (76.9%). The user’s accuracy of the forest class is high, 
indicating that 91.7% of the area classified as forest is actually forest on the ground. 
The two single SAR images achieved poor classification results. The JERS-1 achieved an overall accuracy of 50% a 
Kappa value of 0.087.In particular for the ERS-2, the classification resulted in an extremely low overall accuracy 
(5.7%) and a negative Kappa coefficient. The result reflects what was previously mentioned: the ERS-2 image not only 
had a lower contrast between forest and non-forest (due to its sensor characteristics) but also was more affected by the 
topographic variations (i.e. it has more layover and shadow areas than the JERS-1). 
Table 3: Accuracy assessment summary for the classification with one forest class. 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Classified Single Images Classified fused images Classified fused images 
IHS cylindrical transf. Brovey transf. 
Cover Class TM951 JERSI ERSI TMJC1 TMECI TMJB1 TMEBI 
User Prod. | User Prod. | User Prod.| User Prod. | User Prod. | User Prod. User Prod. 
Forest 91.7 84.6 | 65.0 56.5 0 0 88.9 92.3 90.9 76.9 760 730 | 800 30.8 
Eucalyptus Plantation | 714 76.9 | 364 364 286 182 | 818 69.2 | 625 769 500 538 | 370 769 
Overall Accuracy 82.1 50.0 57 84.6 76.9 66.7 46.2 
Kappa Coefficient 0.61818 0.08723 -0.05 0.65385 0.52632 0.26415 0.1 
Kappa Variance 0.01576 0.01292 0.05625 0.01533 0.01799 0.02575 0.04436 
  
  
Despite the JERS-1 low classification accuracy, the TMJC1 (JERS-1 and IHS cylindrical transformation) fused image 
performed the highest overall classification accuracy (84.6%) and Kappa coefficient (0.654) among all the classified 
images. Moreover, its forest class producer’s accuracy (92.3%) presented an increase of near 8% in relation to the same 
class accuracy for the TM951 (84.6%), for a similar value of forest user’s accuracy in both images. 
The second classification approach used two different sets of native forest subdivided from the original forest set to 
evaluate if the fused images could better differentiate forest classes structurally (the average stem volume was used as a 
parameter) different than the Landsat TM 95. Table 4 below summarizes the overall mapping accuracy and Kappa 
coefficients for each image classified using two forest classes. The user’s, producer’s and overall accuracies were 
obtained from error matrices. 
The classification accuracies obtained are much lower if comparison is made with the classification carried out with one 
forest class. The best classification accuracy was achieved by the TMEC?2 classified fused image. The result suggests 
that the distribution of the two forest strata might the related to the geomorphology of the area, given the ERS-2 greater 
sensitivity to terrain variations. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part BI. Amsterdam 2000. 101