Photogrammetric Digital Surface Model Reconstruction in Extreme Low-Light Environments

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.