In this paper the idea is to combine classifiers with different error types based on Fuzzy Majority Voting (FMV). Four study areas
with different sensors and scene characteristics were used to assess the performance of the model. First, the lidar point clouds were
filtered to generate a Digital Terrain Model (DTM), and then a Digital Surface Model (DSM) and the Normalized Digital Surface
Model (nDSM) were generated. A total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar
intensity image, DSM and nDSM. Three different classification algorithms were used to classify buildings, trees, roads and ground
from aerial images, lidar data and the generated attributes. The used classifiers include: Self-Organizing Map (SOM); Classification
Trees (CTs); and Support Vector Machines (SVMs) with average classification accuracies of 96.8%, 95.9% and 93.7% obtained for
SVMs, SOM, and CTs respectively. FMV was then applied for combining the class memberships from the three classifiers. The
main aim is to reduce overlapping regions of different classes for minimizing misclassification errors. The outcomes demonstrate
that the overall accuracy as well as commission and omission errors have been improved compared to the best single classifier.