Abstract
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 high-sensitivity 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.
Highlights
Each stereo-pair was processed considering the raw images and the one enhanced with the Wallis filter and considering the exterior orientation (EO) of the stereo-pair fixed or computing a new relative orientation solution, for a total of 2688 Digital surface models (DSM)
The structure from motion performances with lowlight conditions, DSM reconstruction accuracy, dense matching failures and reconstruction accuracy correlation with image quality scores obtained for the collected data are presented
The results show that photogrammetry in extreme low-light conditions poses several challenges that should be carefully evaluated during image acquisition
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Digital surface models (DSM) have become one of the main sources of geometrical information for many different applications. Recent substantial improvements in processing hardware and software allow obtaining a detailed three-dimensional (3D) reconstruction of generic objects with millions (and sometimes billions) of 3D point coordinates. The final product of a DSM reconstruction, which can be a point cloud, a raster representation of heights or distances from the observer, a 3D triangular mesh (triangulated irregular network—TIN) or a more complex structured surface (quad- or poly-mesh, non-uniform rational B-spline (NURBS), etc.), can be obtained with several different techniques [1,2,3,4]
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