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Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This study presents a Bayesian approach to decomposing and characterising the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time‐series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parameters in a fully probabilistic model, propagating all sources of uncertainty in inference, makes the Bayesian modelling framework more attractive and powerful than classical methods for approaching the image sensing model. A feasible correspondence of noise parameters to their expected theoretical behaviours and well‐calibrated posterior predictive distributions with a small root mean square error for model predictions have been achieved in this study, thus showing that the proposed model accurately approximates the image sensing model. The Bayesian approach could be extended to formulate further components aimed at identifying even more specific parameters of the imaging process. [ABSTRACT FROM AUTHOR]

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Abstract: Soil erosion is a big concern in bare soils from burnt areas and agricultural lands that lack a vegetation cover. In those unprotected soils, intense rain episodes, typical in Mediterranean climate, cause severe soil erosion processes that have been well studied previously using a number of procedures, such as the geomorphic change detection (GCD) method. This method uses digital elevation models (DEMs) of the soil surface and determines the morphological changes in terms of both erosion and deposition by DEMs of difference (DoDs). However, some types of soil erosion, such as diffuse and sheet erosion, may have a small magnitude, at a millimetre scale, and their determination requires methods adapted to that scale. In this paper, we analyse the suitability of the GCD method to account for small magnitude soil erosion. We present a laboratory procedure and setup to represent and quantify sediment budget in small experimental soil plots through differences of DEMs obtained from images using photogrammetric structure from motion. This study explores several key aspects of the technique: establishment of a common reference system for DEMs; determination of errors in the generation of DEMs; selection of appropriate criteria to obtain reliable changes in DoDs; error propagation using Monte Carlo simulation; and validation of the procedure by comparing the results with actual sediments collected during the experiment. Results showed an overestimation of 13% in accumulated soil loss and confirmed that GCD approach with structure from motion‐based DoDs is a suitable method to quantify small‐magnitude erosion events. [ABSTRACT FROM AUTHOR]

Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective of this study is to establish a rigorous procedure, from the colorimetric point of view, for the characterization of cameras, following different polynomial models. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. In this article we report on pyColorimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l'Éclairage (CIE). This software allows users to control the entire digital image processing and the colorimetric data workflow, including the rigorous processing of raw data. We applied the methodology on a picture targeting Levantine rock art motifs in Remigia Cave (Spain) that is considered part of a UNESCO World Heritage Site. Three polynomial models were tested for the transformation between color spaces. The outcomes obtained were satisfactory and promising, especially with RAW files. The best results were obtained with a second-order polynomial model, achieving residuals below three CIELAB units. We highlight several factors that must be taken into account, such as the geometry of the shot and the light conditions, which are determining factors for the correct characterization of a digital camera. [ABSTRACT FROM AUTHOR]