To reduce the noise in remote-sensing images, seven typical filtering operators arc selected to separately process the remote-sensing images. Combined with the classification method of support vector machine (SVM), we analyze the variation of images' brightness values after filtering and compare their accuracy with that of unfiltered remote-sensing images. The results show that the filtered remote-sensing images have a higher classification accuracy for the extraction of soil salinization compared with untreated remote-sensing images. Of the several selected filtering operators, the soil-salinity extraction model that uses Gaussian low-pass filtering and SVM can improve the classification accuracy and the Kappa coefficient from 86. 7285% and 82. 21% to 89. 6950% and 86.20%, respectively, which is the best classification accuracy to date. To summarize, the filtering operation suppresses noise, improves image quality, effectively improves the monitoring ability of salinization. Grasping the spatial distribution characteristics and temporal and spatial variation principle of soil salinization is of practical significance for preventing and mitigating soil salinization to protect fragile ecological environments in arid and semiarid regions.