A novel approach for PolSAR image analysis using support vector machines (SVM) were presented in this paper, with a focus on the impact of target decomposition techniques on classification accuracy. We explore the use of six different target decomposition techniques, including Cloude, Huynen, HAAlpha, Freeman, Vanzyl, and Yamaguchi, to extract feature vectors for training SVM models. Our study evaluates the performance of the classifiers on two standard benchmark datasets (Flevoland and San Francisco Bay) using multiple assessment metrics, including accuracy, sensitivity/recall, specificity, precision, F1-score, and Kappa coefficient. Our contribution is twofold: first, we provide a comprehensive analysis of how the choice of target decomposition technique affects the classification accuracy of PolSAR
images using SVMs, and second, we demonstrate the effectiveness of SVMs for PolSAR image classification, particularly for differentiating between different land cover types. Our results show that certain target decomposition techniques are better suited for specific land cover types, and our approach can achieve high classification accuracy across different datasets. Overall, our study provides important insights into the effective use of SVMs and target decomposition
techniques for PolSAR image analysis.