The main objective of this work is to analyze the performance of different filtering techniques used to remove speckle noise from PolSAR images. The proposed work shall also provide an insight on the use of various filters on PolSAR images with regards to preservation of edges, point target detection and image classification. Quad-Pol, fully polarimetric SAR Data acquired form the ALOS PalSAR 1 Satellite of the San Francisco Region, USA is used in the study. The SAR data which is in the form of a scattering Matrix (S-Matrix) is first converted to a Coherence Matrix (T-Matrix). The T-Matrix has been used since it is the most suitable matrix representation of scattering and includes consideration of the phase of various polarization combinations. Different filters are applied on the T-Matrix. The filters used are Box Car Filter, Gaussian Filter, IDAN Filter, Lee Refined Filter, Lee Sigma Filter, Lopez Filter, Non-Local Means Filter, and Scattering Model Based Filter. The performance of the different filtering techniques is evaluated using the most used Support Vector Machines (SVM) with Radial Basis function as the kernel with cross validations and Wishart classifiers. The SVM and Wishart classifiers are considered in this study since they give highest accuracies and require the least time for training. A short comparative study is also presented by comparing SVM classifier with seven others to justify the use of SVM Classifier. Three different classes, namely Water, Forest and settlements are considered. Scores like individual class accuracies, Overall Accuracy (OA), Kappa Score, Precision, & Recall are used to determine the classification performance after each filtering technique. Box car filter has the highest overall accuracy of 99.5% after applying SVM Classifier. IDAN filter has the highest overall accuracy of 97.68% after applying Wishart Classifier on it. However, classification carried out on the Boxcar filtered image not only gives high overall accuracy but also maintains edges and point targets.