The use of decision trees for image classification has grown rapidly in recent years, since the reported developments are promising. The current work uses the C4.5 and Random Forest methods for selecting statistical and customized attributes derived from a WorldView-2 (WV-2) image, meant to effectively separate the classes of interest. The attributes comprising the bands extracted from transforms such as Principal Component Analysis and Minimum Noise Fraction in addition to vegetation indices and attributes based on band ratios were used in the stage of feature selection. Two datasets have been employed in the herein described experiments: one consisting of a WV-2 scene with 42 image-derived attributes, and the second one containing the same WV-2 scene and 28 pre-selected meaningful attributes. Comparisons between the two datasets showed that for both methods (C4.5 and RF) the use of statistical attributes plus those derived from image transforms and arithmetical operations increases the agreement indices accuracy. Both classifiers work as data miners, identifying among a large set of attributes those able to discriminate the concerned classes. The results of this article comply with the peer- reviewed literature, for they demonstrate that the classification integrating a greater number of input attributes (without feature selection) attain a significantly superior accuracy. In sum, decision tree classifiers have the capacity to deal with weak explanatory variables, and hence, it is not possible to assess their importance based on individual factors alone.