Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide
AbstractLandslides are one of the most destructive geo-hazards that can bring about great threats to both human lives and infrastructures. Landslide monitoring has been always a research hotspot. In particular, landslide simulation experimentation is an effective tool in landslide research to obtain critical parameters that help understand the mechanism and evaluate the triggering and controlling factors of slope failure. Compared with other traditional geotechnical monitoring approaches, the close-range photogrammetry technique shows potential in tracking and recording the 3D surface deformation and failure processes. In such cases, image matching usually plays a critical role in stereo image processing for the 3D geometric reconstruction. However, the complex imaging conditions such as rainfall, mass movement, illumination, and ponding will reduce the texture quality of the stereo images, bringing about difficulties in the image matching process and resulting in very sparse matches. To address this problem, this paper presents a multiple-constraints based robust image matching approach for poor-texture close-range images particularly useful in monitoring a simulated landslide. The Scale Invariant Feature Transform (SIFT) algorithm was first applied to the stereo images for generation of scale-invariate feature points, followed by a two-step matching process: feature-based image matching and area-based image matching. In the first feature-based matching step, the triangulation process was performed based on the SIFT matches filtered by the Fundamental Matrix (FM) and a robust checking procedure, to serve as the basic constraints for feature-based iterated matching of all the non-matched SIFT-derived feature points inside each triangle. In the following area-based image-matching step, the corresponding points of the non-matched features in each triangle of the master image were predicted in the homologous triangle of the searching image by using geometric constraints, followed by a refinement course with similarity constraint and robust checking. A series of temporal Single-Lens Reflex (SLR) and High-Speed Camera (HSC) stereo images captured during the simulated landslide experiment performed on the campus of Tongji University, Shanghai, were employed to illustrate the proposed method, and the dense and reliable image matching results were obtained. Finally, a series of temporal Digital Surface Models (DSM) in the landslide process were constructed using the close-range photogrammetry technique, followed by the discussion of the landslide volume changes and surface elevation changes during the simulation experiment. View Full-Text
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Qiao, G.; Mi, H.; Feng, T.; Lu, P.; Hong, Y. Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide. Remote Sens. 2016, 8, 396.
Qiao G, Mi H, Feng T, Lu P, Hong Y. Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide. Remote Sensing. 2016; 8(5):396.Chicago/Turabian Style
Qiao, Gang; Mi, Huan; Feng, Tiantian; Lu, Ping; Hong, Yang. 2016. "Multiple Constraints Based Robust Matching of Poor-Texture Close-Range Images for Monitoring a Simulated Landslide." Remote Sens. 8, no. 5: 396.
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