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.
The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by the Advanced Land-Observing Satellite (ALOS) was designed to generate worldwide topographic data with its high-resolution and stereoscopic observation. PRISM performs along-track (AT) triplet stereo observations using independent forward (FWD), nadir (NDR), and backward (BWD) panchromatic optical line sensors of 2.5m ground resolution in swaths 35 km wide. The FWD and BWD sensors are arranged at an inclination of ±23.8° from NDR. In this paper, PRISM images are used under a new perspective, in security domain for sea surveillance, based on the sequence of the triplet which is acquired in a time interval of 90 sec (45 sec between images). An automated motion detection algorithm is developed allowing the combination of encompassed information at each instant and therefore the identification of patterns and trajectories of moving objects on sea; including the extraction of geometric characteristics along with the speed of movement and direction. The developed methodology combines well established image segmentation and morphological operation techniques for the detection of objects. Each object in the scene is represented by dimensionless measure properties and maintained in a database to allow the generation of trajectories as these arise over time, while the location of moving objects is updated based on the result of neighbourhood calculations. Most importantly, the developed methodology can be deployed in any air borne (optionally piloted) sensor system with along the track stereo capability enabling the provision of near real time automatic detection of targets; a task that cannot be achieved with satellite imagery due to the very intermittent coverage.