Abstract: This study focuses on the application of Synthetic Aperture Radar Interferometry (InSAR) technology in landslide identification and intensively explores the typical technical challenges encountered during landslide detection via InSAR technology. Firstly, the paper examines the significance of InSAR multi-view processing as a crucial pre-processing step, discussing the balance between noise suppression and spatial resolution in the selection of multi-view factors, eventually obtaining the optimal multi-view parameter set. Furthermore, this paper discusses the influence of interferogram filtering windows on deformation extraction accuracy, finding that the optimal filtering window effectively preserves deformation information while suppressing InSAR interferometric noise, thus facilitating accurate detection of landslide risks. In addition, this research proposes atmospheric correction at the interferogram level to avoid the propagation of InSAR phase unwrapping errors and effectively mitigate atmospheric noise, thereby enhancing the accuracy of InSAR deformation extraction. Finally, the study explores the long and short temporal baselines in InSAR interferometry, discovering that solely relying on short temporal baseline interferograms is insufficient for capturing small-magnitude landslide deformations. Long temporal baselines, although unavoidable, face challenges of interferometric decorrelation. Therefore, a strategy combining a base of short temporal baseline interferograms with a certain number of high-quality long temporal baseline interferograms proves to be more reliable for identifying small-magnitude landslide hazards. Lastly, the study exemplifies its findings through a landslide-prone area in the upper reaches of the Jinsha River, conducting experiments based on the optimal and control parameter sets to validate the effectiveness and applicability of the optimal set. The aforementioned research outcomes significantly deepen the practicality and limitations of InSAR technology in geological hazard identification applications, providing scientific support for the early identification of landslide hazards using InSAR technology, with important theoretical and practical implications.