Paper
19 June 2015 Multi-temporal change image inference towards false alarms reduction for an operational photogrammetric rockfall detection system
Panagiotis Partsinevelos, Christina Kallimani, Achilleas Tripolitsiotis
Author Affiliations +
Proceedings Volume 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015); 95351R (2015) https://doi.org/10.1117/12.2199736
Event: Third International Conference on Remote Sensing and Geoinformation of the Environment, 2015, Paphos, Cyprus
Abstract
Rockfall incidents affect civil security and hamper the sustainable growth of hard to access mountainous areas due to casualties, injuries and infrastructure loss. Rockfall occurrences cannot be easily prevented, whereas previous studies for rockfall multiple sensor early detection systems have focused on large scale incidents. However, even a single rock may cause the loss of a human life along transportation routes thus, it is highly important to establish methods for the early detection of small-scale rockfall incidents. Terrestrial photogrammetric techniques are prone to a series of errors leading to false alarm incidents, including vegetation, wind, and non relevant change in the scene under consideration. In this study, photogrammetric monitoring of rockfall prone slopes is established and the resulting multi-temporal change imagery is processed in order to minimize false alarm incidents. Integration of remote sensing imagery analysis techniques is hereby applied to enhance early detection of a rockfall. Experimental data demonstrated that an operational system able to identify a 10-cm rock movement within a 10% false alarm rate is technically feasible.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Panagiotis Partsinevelos, Christina Kallimani, and Achilleas Tripolitsiotis "Multi-temporal change image inference towards false alarms reduction for an operational photogrammetric rockfall detection system", Proc. SPIE 9535, Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015), 95351R (19 June 2015); https://doi.org/10.1117/12.2199736
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Cited by 2 scholarly publications.
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KEYWORDS
Image processing

Remote sensing

Cameras

Denoising

Vegetation

Image analysis

Injuries

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