Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

Detecting slope deformation is an important issue in engineering. Timely deformation detection can effectively avoid catastrophic slope failure and ensure the safety of a project and engineering personnel. In this study, deformation detection for a quarry slope is implemented using the integration of subpixel offset tracking (sPOT) and unsupervised change detection algorithms based on unmanned aerial vehicle (UAV) image datasets. The sPOT algorithm is used to give the surface displacement field of the slope with subpixel accuracy, and the unsupervised change detection algorithm yields the ground object reconstruction area of the slope to verify and explain the sPOT result. The integrated analysis method in this paper is highly applicable and only requires a minimum of two UAV datasets as raw data. Combining the advantages of the sPOT and unsupervised change detection algorithms, the proposed method has the ability to detect and analyse slow and rapid slope deformation with good accuracy.