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Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring in vegetated mountainous areas often faces challenges, including temporal decorrelation and atmospheric noise. Extracting true surface deformation trends from time-series signals and validating their accuracy remain critical issues. This study focuses on the Lantai area in Northeast Taiwan, utilizing Sentinel-1 satellite imagery from 2021 to 2025. We employed the Small Baseline Subset (SBAS-InSAR) technique to resolve surface deformation, with a specific focus on optimizing time-series signals and performing validation analysis using in-situ GNSS data.To optimize the time-series deformation signals, this study compared the efficacy of three smoothing methods on the raw SBAS results: Mean Filter, Median Filter, and Gaussian Filter. The results indicate that while the Mean Filter is computationally efficient, it tends to cause boundary blurring and time delays. The Median Filter effectively removes sudden noise spikes but performs less effectively in smoothing subtle continuous changes. In contrast, the Gaussian Filter successfully suppresses noise while preserving waveform continuity, making it the most suitable method for analyzing long-term deformation trends in this study area.Regarding accuracy validation, the study compared the optimized InSAR time-series deformation with data from continuous GNSS monitoring stations. The comparison reveals that, due to the 12-day satellite revisit cycle and dense vegetation, the InSAR results exhibit a noticeable short-term drift effect. However, over the five-year observation period, the overall cumulative deformation trends between InSAR and GNSS show good consistency. This research confirms that with appropriate filter optimization, Sentinel-1 time-series InSAR technology can be effectively applied to broad-area surface deformation screening in mountainous regions, providing reliable long-term trend data for landslide potential zoning.