Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas

Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Büyükalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (2015–2020) is performed using LiCSAR–LiCSBAS, followed by two novel approaches: multi-dimensional InSAR research and analysis (MIRA) and Crosta’s InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of −1.1 cm/year to 1.0 cm/year for descending tracks and −3.8 cm/year to 3.8 cm/year for ascending tracks. Vertical displacements range from −1.9 cm/year to 2.3 cm/year and east–west components from −2.8 cm/year to 2.9 cm/year. MIRA uses an n-Dimensional Visualizer and SVM classifier to identify deformation clusters, and InCROSS applies PCA to enhance deformation features. MIRA increases the deformation detection capacity compared to conventional InSAR products, and InCROSS integrates these products. A comparison of the results reveals 80.48% consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide-prone areas.