Critical infrastructure monitoring using UAV imagery

Evangelos Maltezos; Michael Skitsas; Elisavet Charalambous; Nikolaos Koutras; Dimitris Bliziotis; Kyriacos Themistocleous

doi:10.1117/12.2240478

12 August 2016 Critical infrastructure monitoring using UAV imagery

Evangelos Maltezos, Michael Skitsas, Elisavet Charalambous, Nikolaos Koutras, Dimitris Bliziotis, Kyriacos Themistocleous

Author Affiliations +

Proceedings Volume 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016); 96880P (2016) https://doi.org/10.1117/12.2240478
Event: Fourth International Conference on Remote Sensing and Geoinformation of the Environment, 2016, Paphos, Cyprus

Abstract

The constant technological evolution in Computer Vision enabled the development of new techniques which in conjunction with the use of Unmanned Aerial Vehicles (UAVs) may extract high quality photogrammetric products for several applications. Dense Image Matching (DIM) is a Computer Vision technique that can generate a dense 3D point cloud of an area or object. The use of UAV systems and DIM techniques is not only a flexible and attractive solution to produce accurate and high qualitative photogrammetric results but also is a major contribution to cost effectiveness. In this context, this study aims to highlight the benefits of the use of the UAVs in critical infrastructure monitoring applying DIM. A Multi-View Stereo (MVS) approach using multiple images (RGB digital aerial and oblique images), to fully cover the area of interest, is implemented. The application area is an Olympic venue in Attica, Greece, at an area of 400 acres. The results of our study indicate that the UAV+DIM approach respond very well to the increasingly greater demands for accurate and cost effective applications when provided with, a 3D point cloud and orthomosaic.

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Evangelos Maltezos, Michael Skitsas, Elisavet Charalambous, Nikolaos Koutras, Dimitris Bliziotis, and Kyriacos Themistocleous "Critical infrastructure monitoring using UAV imagery ", Proc. SPIE 9688, Fourth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2016), 96880P (12 August 2016); https://doi.org/10.1117/12.2240478

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