- Title
- Photogrammetric digital surface model reconstruction in extreme low-light environments
- Creator
- Roncella, Riccardo; Bruno, Nazarena; Diotri, Fabrizio; Thoeni, Klaus; Giacomini, Anna
- Relation
- ARC.LP160100370 http://purl.org/au-research/grants/arc/LP160100370
- Relation
- Remote Sensing Vol. 13, Issue 7, no. 1261
- Publisher Link
- http://dx.doi.org/10.3390/rs13071261
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2021
- Description
- Digital surface models (DSM) have become one of the main sources of geometrical information for a broad range of applications. Image-based systems typically rely on passive sensors which can represent a strong limitation in several survey activities (e.g., night-time monitoring, underground survey and night surveillance). However, recent progresses in sensor technology allow very high sensitivity which drastically improves low-light image quality by applying innovative noise reduction techniques. This work focuses on the performances of night-time photogrammetric systems devoted to the monitoring of rock slopes. The study investigates the application of different camera settings and their reliability to produce accurate DSM. A total of 672 stereo-pairs acquired with high-sensitivity cameras (Nikon D800 and D810) at three different testing sites were considered. The dataset includes different camera configurations (ISO speed, shutter speed, aperture and image under-/over-exposure). The use of image quality assessment (IQA) methods to evaluate the quality of the images prior to the 3D reconstruction is investigated. The results show that modern highsensitivity cameras allow the reconstruction of accurate DSM in an extreme low-light environment and, exploiting the correct camera setup, achieving comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.
- Subject
- digial surface model; low-light photogrammetry; slope monitoring; imaging sensor; stereo vision; accuracy; image quality assessment; ISO
- Identifier
- http://hdl.handle.net/1959.13/1434618
- Identifier
- uon:39476
- Identifier
- ISSN:2072-4292
- Rights
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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