Traditional image classification methods are undertaken using the pixel as the research unit. These methods cannot use semantic information, and their classification results may not always be satisfactory. To solve this problem, objected-oriented methods have been widely investigated to classify remote sensing images. In this paper, we propose an innovative objected-oriented technique that combines pixel-based classification and a segmentation approach for the classification of polarimetric synthetic aperture radar (PolSAR) images. In the process of the pixel-based classification, a soft voting strategy is utilized to fuse multiple classifiers, which can, to some extent, overcome the drawback of majority voting. The experimental results are presented for two quad-polarimetric SAR images. The proposed classification scheme improves the classification accuracies after assembling the multiple classifiers, and provides the classification maps with more homogeneous regions by integrating the spatial information, when compared with pixel-based classification. By deploying multi-scale segmentation, we get a series of classification results, which again show that our method is superior to the conventional object-oriented methods.