Land cover classification is a vital application of polarimetric synthetic aperture radar (PolSAR) images in various fields, such as agriculture monitoring and urban assessment. We introduce a modified and enhanced PolSAR image classification method, combining six decomposition techniques, a support vector machine (SVM) based classifier, and a probabilistic voting ensemble (PVE) model. Our method addresses the challenges posed by the complexity of PolSAR data and the limited availability of labeled samples. The core of our approach lies in integrating multiple decomposition techniques as feature extractors, aiming to capture diverse scattering behaviors and uncover valuable information related to land cover characteristics. These techniques include the Huynen, Cloude, Freeman and Durden, HAAlpha, Yamaguchi, and Vanzyl decomposition methods. The extracted features are then utilized as inputs for training the SVM base classifier. To enhance classification performance, a PVE model is used to combine predictions from each decomposition technique, considering the individual prediction confidence and the characteristics of the decomposition methods. The decision fusion process is applied to integrate diverse predictions based on the majority voting and estimated class probability, providing a more robust and reliable final label prediction and thereby improving the overall accuracy of the classification process. Experimental analyses are conducted on airborne and spaceborne PolSAR images, covering various bands and land cover types, to evaluate the effectiveness and robustness of our proposed method. The experimental results demonstrate that our approach yields more confident class predictions than alternative methods.