Geographic Object-Based Image Analysis (GEOBIA) makes it possible to exploit a number of new features in the remote sensing image classification process in comparison to the traditional pixel-based alternatives. Such possibility arises from the introduction of a segmentation step in the analysis process. The new features refer to aggregated spectral pixel values, textural, morphological and topological properties computed for the different image segments. The usual segmentation approach found in most GEOBIA works relies on a hierarchy of segmentations, each hierarchy level associated to a number of classes of objects characterized by similar sizes, i.e., which are detectable at a particular scale. The usual practice, therefore, does not consider specific, independent segmentations for each class of interest in the interpretation problem, grouping objects at the same scale through a single segmentation procedure, for instance, using the same algorithm and parameters. The thesis investigated in this work lied on the assumption that if segmentations are not specialized for each object class, then many object features cannot be properly exploited in the classification process. The proposed approach relies on a specific rule to solve eventual spatial conflicts among different segmentations. The experimental results have showed that the proposed approach performed better, i.e., produced better classification results, than the usual one in most of the investigated interpretation problems.