Synthetic Aperture Radar (SAR) serves as a fundamental system for Earth observation. Specifically, Polarimetric SAR (PolSAR) sensors capture images of diverse polarization scenes, enhancing the retrievable information. Due to their coherent nature, SAR images represent complex data affected by multiplicative noise known as speckle. The presence of this noise impedes image interpretation, making speckle removal a crucial pre-processing step for further applications. Recently, various Deep Learning (DL)-based methods have been proposed for speckle removal in PolSAR data, relying on different strategies for constructing training datasets. This complicates practical comparisons due to the absence of ground truth. In this paper, a comparison of the impact of different PolSAR despeckling methods on classification applications is studied. Some different PolSAR despeckling filters shows the different ability on improving the classification. In general, DL-based methods have greater improvement ability and potential.