Analysis of low-light and night-time stereo-pair images for photogrammetric reconstruction

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Abstract

Rockfalls and rockslides represent a significant risk to human lives and infrastructures because of the high levels of energy involved in the phenomena. Generally, these events occur in accordance to specific environmental conditions, such as temperature variations between day and night, that can contribute to the triggering of structural instabilities in the rock-wall and the detachment of blocks and debris. The monitoring and the geostructural characterization of the wall are required for reducing the potential hazard and to improve the management of the risk at the bottom of the slopes affected by such phenomena. In this context, close range photogrammetry is largely used for the monitoring of high-mountain terrains and rock walls in mine sites allowing for periodic survey of rockfalls and wall movements. This work focuses on the analysis of low-light and night-time images of a fixed-base stereo pair photogrammetry system. The aim is to study the reliability of the images acquired over the night to produce digital surface models (DSMs) for change detection. The images are captured by a high-sensitivity DLSR camera using various settings accounting for different values of ISO, aperture and time of exposure. For each acquisition, the DSM is compared to a photogrammetric reference model produced by images captured in optimal illumination conditions. Results show that, with high level of ISO and maintaining the same grade of aperture, extending the exposure time improves the quality of the point clouds in terms of completeness and accuracy of the photogrammetric models.

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APA

Santise, M., Thoeni, K., Roncella, R., Diotri, F., & Giacomini, A. (2018). Analysis of low-light and night-time stereo-pair images for photogrammetric reconstruction. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 42, pp. 1015–1022). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprs-archives-XLII-2-1015-2018

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