Claudio Dupas
Table 4: Accuracy assessment summary for the classification using two forest classes.
Classified Single Images Dries | Cori
Cover Class TM952 JERS2 ERS2 TMJC2 TMEC2 TMJB2 TMEB2
User Prod. | User Prod. | User Prod.| User Prod. | User Prod. | User Prod. | User Prod.
Forest A 46.1 46.1 | 333 8.3 0 0 1333 23.1 1538 5381429 4621300 231
Forest B 50.0 53.8 | 28.6 364 0 O | 50.0 69.2 | 60.0 60.0 | 45.5 238.5 | 25.0 73
Eucalyptus Plantation | 72.7 615 | 300. 273 | 286 18.2 ]81.8 69.2 | 66.7 76.9 | 615 0615|417 769
Overall Accuracy 53.9 23.6 5.7 53.8 59.0 48.7 35.9
Kappa Coefficient 0.31646 -0.04938 -0.02758 0.31646 0.39241 0.30769 0.05063
Kappa Variance 0.01394 0.01680 0.03273 0.01391 0.01318 0.01440 0.01819
The classification accuracies for the areas that correspond to cloud cover were assessed throughout matrices. Table 5
summarises the results and ads the calculated Kappa coefficient and variance.
The results indicate similar classification accuracy for the two fused images. Although the test data sets used to assess
the classification accuracy were based on the Landsat TM 96 classified image, an expressive classification accuracy was
achieved for the forest class (around 60%). Attention is paid to the fact that the classification accuracy for the
Eucalyptus class is null for both images. That is probably a result of extremely low signature separability between the
forest class and the Eucalyptus class. As the number of sampling points used for the Forest training set was the half of
the number for the training set, the Gaussian Maximum Likelihood classifier should had given ‘preference’ to the Forest
signature instead of the Eucalyptus signature for classifying ‘doubt’ points.
Table 5: Summary of the accuracy assessment for the cloud gap areas classification.
Fused Images Classification for the Areas Correspondent to Cloud cover
Cover Class TMJCIM TMECIM
User Prod. User Prod.
Forest 55.4 65.7 90.9 67.1
Eucalyptus Plantation 0 0 0 0
Non-forest 56.7 54.3 54.8 48.6
Overall Accuracy 56.0 54.0
Kappa Coefficient 0.175 0.13752
Kappa Variance 0.00552 0.00582
5 CONCLUSIONS AND RECOMMENDATIONS
The main problems encountered were related to the effects of topography on the SAR imagery (shadow and layover).
These effects induced differences in the backscattered radar signals, which leaded to confusion of these differences with
tonal differences from the different cover types. The consequence was the poor classification accuracy achieved by the
SAR images.
Since the Landsat TM intensity is substituted by the SAR intensity during the data fusion process, the shadow and
layover effects are transmitted to the fused images, generating the same confusion when the data is classified. However,
even being affected by the shadow and layover, the TMJC image (fusion with JERS-1, using IHS cylindrical
transformation) out-performed the Landsat TM classification accuracy. This is an indication that if some changes are
made in the methodology, future research might lead to a significant improve in the classification results.
A 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. Although the test data sets used to assess the accuracy of the classification for the gap areas
were based on the Landsat TM 96 classified image, a classification accuracy of about 60% was achieved for the forest
class. However, the overall classification accuracy of zero percent for the Eucalyptus class indicates a low separability
of this class from the forest class when the accuracy for the shadow areas is assessed.
102 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B1. Amsterdam 2000.
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