A decision fusion approach is proposed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support vector machine based supervised classification in class separation and the capability of the unsupervised K-means classifier in reducing spectral variation impact in homogeneous regions. This approach simply adopts the majority voting rule, but can achieve the same objective of object-based classification.