The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

Landslide is one of the most frequent and destructive geohazards around the world. The accurate identification of potential landslides plays a vital role in the management of landslide risk. The use of unmanned aerial vehicle (UAV) techniques has recently gained much popularity in landslide assessment; however, most of the current UAV-image-based landslide identifications rely upon visual inspections. In this paper, an image-analysis-based landslide identification framework is developed to detect the landslides in UAV images by recognizing the landslide boundaries and ground surface cracks. In this framework, object-oriented image analysis is undertaken to identify the potential landslide boundaries in the input UAV images and the ground surface cracks in the UAV images are recognized by an automatic ground surface crack recognition model, which is trained through a deep transfer learning strategy. With the aid of this transfer learning strategy, the crack recognition model trained can take advantage of the feature of local ground surface cracks in the concerned area and the crack recognition model that has well been developed based on the samples of ground surface cracks collected from different landslide sites. Then, the landslide boundaries and the ground surface cracks obtained are fused based on Boolean operations; the fusion results can allow for informed landslide identification in UAV Images. To illustrate the effectiveness of the proposed image-analysis-based landslide identification framework, the Heifangtai Terrace of Gansu, China, was selected as a study area, and the identification results are further validated through comparisons with the field survey results.