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isprsannals II 3 W5 187 2015

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Figure 1. Training areas and randomly selected checkpoints (crosses). The colours represent different classes
Figure 2. Principle of decision tree classification (T=test using  thresholds of attributes; a, b, c=classes)
Table 2. Error matrix of the land cover map derived by a DT (b=’building’, h=’hedge&bush’, g=’grass’, r=’road&parking lot’, t=’tree’, w=’wall&car port’)
Table 6. User’s accuracy of the individual classes (SVM- classification)
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