For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives.

Monitoring the structural integrity of dams in politically sensitive and physically inaccessible regions is critical for disaster risk reduction and national security. This study presents a long-term structural stability assessment of the Hwanggang Dam in North Korea using time-series Sentinel-1 synthetic aperture radar (SAR) data acquired between 2017 and 2025, processed with interferometric SAR (InSAR) techniques. Two key technical challenges associated with long-term dam monitoring in the absence of in situ reference data are addressed: (i) the loss of persistent scatterers (PS) over multi-year observation periods due to temporal decorrelation, and (ii) the need to maximize the density of coherent measurement points within a single, spatially isolated structure. To overcome these limitations, a time-segmented Compressed SAR (TS-ComSAR) approach is employed, in which InSAR processing is performed in annual segments and subsequently stacked to construct a nine-year displacement time series. By constraining the temporal baseline, this strategy enables consistent identification and tracking of stable PS points across multiple years. In addition, a full-stack nine-year time-series dataset is processed using the conventional Compressed SAR (ComSAR) approach to enable a direct comparison with TS-ComSAR results. The analysis indicates that the Hwanggang Dam remains structurally stable over the observation period; however, the concrete dam section exhibits a persistent uplift trend, with cumulative displacement on the order of 20–30 mm. In contrast, the embankment (fill) dam section shows a pronounced long-term subsidence trend, with cumulative displacement reaching approximately 60–80 mm over the monitoring period. These results demonstrate the effectiveness of TS-ComSAR for infrastructure safety monitoring and highlight its capability for remote structural health assessment of strategic dams in restricted regions where ground-based observations are unavailable. The proposed integration of time-segmented and full-stack ComSAR provides a robust framework for long-term stability assessment of critical infrastructure under data-limited conditions.