Commercial Digital Camera Research Articles

High count rate alpha particle spectroscopy by a digital optical camera: A comparison study on edge detection algorithms

Alpha particle spectroscopy is highly significant in technologies related to radioactivity, particularly in radiation safety and the study of natural radioactivity. On the other hand, tools capable of identifying and determining the energies of alpha particles are of great interest. Among these tools, Commercial Off-The-Shelf (COTS) instruments, which can be used for alpha particle detection, have gained significant importance due to their ease of use and low cost. In this study, alpha particle spectroscopy of a Ra-226 source has been performed using a commercial digital camera based-on a CMOS sensor. In order to determine energy spectrum of alpha particles emitted from the source, the performance of various edge detection algorithms was examined. An algorithm based-on local pixel changes was introduced and implemented. By comparing the run times of the algorithms, it was shown that the algorithm proposed in this article exhibited the shortest run-time. Additionally, a method for rejecting pile-up events to correct their destructive effects on the alpha particle energy spectrum was introduced and implemented.

Versatile luminescence thermometry via intense green defect emission from an infrared-pumped fluorosilicate optical fiber

An all-glass optical fiber capable of two distinct methods of optical thermometry is described. Specifically, a silica-clad, barium fluorosilicate glass core fiber, when pumped in the infrared, exhibits visibly intense green defect luminescence whose intensity and upper-state lifetime are strong functions of temperature. Intensity-based optical thermometry over the range from 25°C to 130°C is demonstrated, while a lifetime-based temperature sensitivity is shown from 25°C to 100°C. Time-domain measurements yield a relative sensitivity of 2.85%K−1 at 373 K (100°C). A proof-of-concept distributed sensor system using a commercial digital single-lens reflex camera is presented, resulting in a measured maximum relative sensitivity of 1.13%K−1 at 368 K (95°C). The sensing system described herein stands as a new blueprint for defect-based luminescence thermometry that takes advantage of pre-existing and relatively inexpensive optical components, and allows for the use of standard cameras or simply direct human observation.

Post-midnight purple arc and patches appeared on the high latitude part of the auroral oval: Dawnside counterpart of STEVE?

The phenomenon known as strong thermal emission velocity enhancement (STEVE) is a purple/mauve arc-shaped atmospheric glow observed at lower latitudes of the auroral oval on the duskside. Simultaneous observations using a ground-based camera and a low-altitude satellite have shown that STEVE is accompanied by rapid westward ion flows. Such fast ion flows are termed the subauroral ion drift (SAID) or subauroral polarization stream (SAPS). Similarly, an eastward fast ion flow known as the dawnside auroral polarization stream (DAPS) is observed within the Region 1 current on the dawnside. If the optical phenomenon triggered by SAID/SAPS corresponds to STEVE, a comparable optical phenomenon should be driven by DAPS. Thus far, however, such a phenomenon has not been reported. This study discovers, for the first time, a purple-colored optical phenomenon characterized by the fast eastward ion flows, a possible signature of DAPS, occurring poleward of the bright green arc in the post-midnight sector. We present color all-sky images obtained by a ground-based commercial digital camera, along with wide-coverage optical measurements and in-situ data from low-altitude satellites. The results imply that this glow requires not only a high-speed ion flow but also its sharp latitudinal gradient at the boundary between the Region 1 and 2.Graphical

Vital signs estimation in elderly using camera-based photoplethysmography

In the context of Ambient Assisted Living, the demand for healthcare technologies development has been increased in the last two years by the current global health situation. The contagious nature of COVID-19 warrants inevitability to the thought of easing a continuous measurement of vital signs such as Heart Rate, Breath Rate, and SpO2 in a non-invasive manner, contributing of fundamental importance in the assessment of health status especially among frail and/or elderly individuals. A widely diffused method for contactless vital signs monitoring is remote photoplethysmography from facial video streams that, contrary to traditional contact measurement techniques, allows the measurement of vital parameters without the need for wearable sensors (generally considered uncomfortable, especially by the elderly), even with commercial and low-cost digital cameras. This paper proposes the design and implementation of a new pipeline for estimating Heart Rate, Breath Rate, and SpO2 values, and its integration on Raspberry Pi 4 as an elaboration unit. The pipeline provides specific algorithmic blocks to improve vital signs estimation in elderly subjects as it is made more difficult by the skin tone and the presence on the face of wrinkles, folds, and moles. Quantitative evaluations on our dataset containing the acquisition of only elderly older than 65 years of age demonstrate the validity of the proposed pipeline. For validation against the state of the art, tests were also conducted on three standard benchmark datasets containing video with subjects of varying ages. Again, the pipeline proved to be robust concerning the estimation of vital signs considered in the present work.

Sugarcane nitrogen nutrition estimation with digital images and machine learning methods

The color and texture characteristics of crops can reflect their nitrogen (N) nutrient status and help optimize N fertilizer management. This study conducted a one-year field experiment to collect sugarcane leaf images at tillering and elongation stages using a commercial digital camera and extract leaf image color feature (CF) and texture feature (TF) parameters using digital image processing techniques. By analyzing the correlation between leaf N content and feature parameters, feature dimensionality reduction was performed using principal component analysis (PCA), and three regression methods (multiple linear regression; MLR, random forest regression; RF, stacking fusion model; SFM) were used to construct N content estimation models based on different image feature parameters. All models were built using five-fold cross-validation and grid search to verify the model performance and stability. The results showed that the models based on color-texture integrated principal component features (C-T-PCA) outperformed the single-feature models based on CF or TF. Among them, SFM had the highest accuracy for the validation dataset with the model coefficient of determination (R2) of 0.9264 for the tillering stage and 0.9111 for the elongation stage, with the maximum improvement of 9.85% and 8.91%, respectively, compared with the other tested models. In conclusion, the SFM framework based on C-T-PCA combines the advantages of multiple models to enhance the model performance while enhancing the anti-interference and generalization capabilities. Combining digital image processing techniques and machine learning facilitates fast and nondestructive estimation of crop N-substance nutrition.

Biomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopy

ABSTRACT Above-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.

Carte du Ciel andGaia

Context.The Carte du Ciel archive at the University of Helsinki enables us to see the sky as it was about 120 yr ago. The archive consists of single-exposure and triple-exposure plates between epochs 1896.8–1925.8.Aims.Our main aim is to find binary and multiple stars by combining Carte du Ciel andGaiadata.Methods.The plates were digitised with a commercial digital camera. We usedGaiadata to calculate predicted coordinates of stars at the epoch of each plate. These stars were used as reference stars to fit astrometry for each plate, giving fitted coordinates for stars on the Carte du Ciel plates. If the predicted and fitted coordinates differed at a significant level, we classified the star as a non-single star, for which the proper motion values given in theGaiacatalogue can be unreliable.Results.We find that several astrometric quality indicators ofGaiaindicate that the uncertainties ofGaia’ssingle-star model fit are, in general, larger for our non-single-star candidates. The percentage of our non-single-star candidates, which are in the catalogues of known binary stars, is relatively low, ~10% at maximum.Conclusions.The combination of the Carte du Ciel andGaiadata can be used to identify candidates of non-single stars. We propose that the sources with a significant difference between the predicted and fitted coordinates are long-period binaries, although astrophysical and/or instrumental effects as origin for the coordinate difference cannot be excluded for individual cases.

Response nonuniformity modelling for colour imaging systems

In this paper, a theoretical analysis reveals that system radiation transmission nonuniformity and system radiation conversion nonuniformity have different forms. A colour imaging system response nonuniformity model is then proposed according to the two kind of nonuniformity of three channels. The model includes two models, one for brightness and one for chromaticity. The brightness model is built according to the nonuniformity of single channel. The chromaticity model is built as an error ellipse in the chromaticity diagram. The error ellipse can be also used as a representation parameter of the radiation conversion nonuniformity. The proposed models were verified using three commercial digital cameras. The experimental results show that the proposed nonuniformity model is correct. Moreover, the proposed representation parameter is an effective way to evaluate and characterize the nonuniformity of a colour imaging system. This research has value as a reference for the correction and evaluation of response nonuniformity in colour imaging systems.

Reliability of a human pose tracking algorithm for measuring upper limb joints: comparison with photography-based goniometry

BackgroundRange of motion (ROM) measurements are essential for diagnosing and evaluating upper extremity conditions. Clinical goniometry is the most commonly used methods but it is time-consuming and skill-demanding. Recent advances in human tracking algorithm suggest potential for automatic angle measuring from RGB images. It provides an attractive alternative for at-distance measuring. However, the reliability of this method has not been fully established. The purpose of this study is to evaluate if the results of algorithm are as reliable as human raters in upper limb movements.MethodsThirty healthy young adults (20 males, 10 females) participated in this study. Participants were asked to performed a 6-motion task including movement of shoulder, elbow and wrist. Images of movements were captured by commercial digital cameras. Each movement was measured by a pose tracking algorithm (OpenPose) and compared with the surgeon-measurement results. The mean differences between the two measurements were compared. Pearson correlation coefficients were used to determine the relationship. Reliability was investigated by the intra-class correlation coefficients.ResultsComparing this algorithm-based method with manual measurement, the mean differences were less than 3 degrees in 5 motions (shoulder abduction: 0.51; shoulder elevation: 2.87; elbow flexion:0.38; elbow extension:0.65; wrist extension: 0.78) except wrist flexion. All the intra-class correlation coefficients were larger than 0.60. The Pearson coefficients also showed high correlations between the two measurements (p < 0.001).ConclusionsOur results indicated that pose estimation is a reliable method to measure the shoulder and elbow angles, supporting RGB images for measuring joint ROM. Our results presented the possibility that patients can assess their ROM by photos taken by a digital camera.Trial registrationThis study was registered in the Clinical Trials Center of The First Affiliated Hospital, Sun Yat-sen University (2021–387).

Measurement of volume fraction distribution in a drying film by imaging with a digital camera

We propose a method to measure the evolution of the volume fraction distribution in colloidal films during their drying process, using only the transmitted light intensity recorded with a commercial digital camera. From the Lambert-Beer law, the volume fraction of a dispersion film with a certain film thickness can be measured from the transmitted light intensity. A pseudo-absorption capacity was imparted to the transparent dispersion by adding a red dye, and the ratio of green to blue light absorbance was measured for the liquid film. For any film, the volume fraction increased spatially towards a compaction front, and the volume fraction in this region reached approximately 63.4%. The volume fraction inside the liquid region of the film also increased during drying. These trends are similar to previous results obtained using other methods. The magnification in this method can be easily changed by replacing the imaging equipment, and changes in volume fraction distribution near the drying front were observed at high magnification using different lenses. This method, based on captured photographic images, measure the two-dimensional distribution of the volume fraction, making it easy to observe changes in the volume fraction distribution caused by drying at both macroscopic and microscopic levels. It is likely that the proposed method will be difficult to apply to anisotropic particles or to larger particles which cause significant scattering.

Rapid printing of a Bacterial array for a Solid-Phase Assay (BacSPA) of heavy metal ions

Bacterial whole-cell biosensors have been used to detect numerous pollutants for decades. In this work, a rapid sensing method using patterned bacterial arrays on solidified agar media was reported. A printing system, which quickly patterns 384-spot bacterial arrays via inertial impact, was developed in this study. The droplet formation was observed by a high-speed camera and simulated in COMSOL software to investigate the printing conditions. We found that the droplet volume and speed were influenced by the accelerations of the impact. Following adjustment, the printer could eject discrete droplets and pattern arrays with high accuracy. The printed bacteria were incubated, and images acquired by a commercial digital camera were analyzed by the Python program. The areas of 384 spots in a single array were similar, with a high roundness and low deviations. We found that the printer plate quality influenced the printing results more than the printing conditions. Finally, the printing system was applied to determine heavy metals concentrations in solid growth media by bioluminescent sensor bacteria. The luminescence intensity patterns varied between heavy metals. We propose the printing system as a useful tool for rapid patterning of bacterial whole cell sensor arrays on solid agar.

The use of digital imaging, chlorophyll fluorescence and Vis/NIR spectroscopy in assessing the ripening stage and freshness status of bell pepper fruit

The optimal quality, the marketability and the shelf-life of pepper fruit often depend on the ripening stage at harvest. Moreover, it is necessary to estimate the process of ripening in various steps of the supply chain. The potential of four non-destructive techniques as rapid tools for discriminating the process of ripening was investigated. Chlorophyll fluorescence, Vis/NIR spectroscopy, two commercial digital imaging cameras- one R-G-B dslr and a modified R-G-NIR compact camera were assessed as alternatives to color measurements in classifying pepper fruit according to their ripening stage. Freshly harvested mature bell pepper fruit ‘cv. Denver’ were sorted in six distinct ripening stages (S1-S6), from green to full red (mature green 100% green-S1, green to brown-S2, brown-S3, orange-S4, red-S5 and full red-S6 stage), by visually assessing their color. From the results, it is concluded that the a∗/b∗ color parameter that was determined using a colorimeter was proven to be the most accurate descriptor in ripening stage classification, either at harvest or during storage, as confirmed by chemometrics, such as principal component (PCA), partial least square (PLS) and support vector machine (SVM) analyses and mean comparisons, as well. Therefore, the increase of a∗/b∗ ratio can be used as an indicator to estimate the degree of fruit ripeness. The a∗/b∗ ratio was strongly correlated with fruit spectral reflectance and chlorophyll fluorescence data, as well as with two imaging indices generated by digital R-G-B and R-G-NIR imaging techniques that are reported for the first time on fruit in this study. The above results indicate that it is possible to estimate the a∗/b∗ ratio and simultaneously the ripening stage of pepper fruit with high accuracy and consistency, by using individually several non-destructive techniques. However, freshness status of bell pepper fruit can be reliably assessed, irrespectively of the ripening stage at harvest, only by spectral reflectance data and indeed even by processing only specific wavelengths that were identified after implementing the genetic algorithm.

A Robust Vegetation Index Based on Different UAV RGB Images to Estimate SPAD Values of Naked Barley Leaves

Chlorophyll content in plant leaves is an essential indicator of the growth condition and the fertilization management effect of naked barley crops. The soil plant analysis development (SPAD) values strongly correlate with leaf chlorophyll contents. Unmanned Aerial Vehicles (UAV) can provide an efficient way to retrieve SPAD values on a relatively large scale with a high temporal resolution. But the UAV mounted with high-cost multispectral or hyperspectral sensors may be a tremendous economic burden for smallholder farmers. To overcome this shortcoming, we investigated the potential of UAV mounted with a commercial digital camera for estimating the SPAD values of naked barley leaves. We related 21 color-based vegetation indices (VIs) calculated from UAV images acquired from two flight heights (6.0 m and 50.0 m above ground level) in four different growth stages with SPAD values. Our results indicated that vegetation extraction and naked barley ears mask could improve the correlation between image-calculated vegetation indices and SPAD values. The VIs of ‘L*,’ ‘b*,’ ‘G − B’ and ‘2G − R − B’ showed significant correlations with SPAD values of naked barley leaves at both flight heights. The validation of the regression model showed that the index of ‘G-B’ could be regarded as the most robust vegetation index for predicting the SPAD values of naked barley leaves for different images and different flight heights. Our study demonstrated that the UAV mounted with a commercial camera has great potentiality in retrieving SPAD values of naked barley leaves under unstable photography conditions. It is significant for farmers to take advantage of the cheap measurement system to monitor crops.

Adaptive Interval Type-2 Fuzzy Filter: An AI Agent for Handling Uncertainties to Preserve Image Naturalness

Artificial intelligence (AI) offers fuzzy set theory (FST) as one of the popular AI agents and decision making tools for digital image processing to increase the robustness of vision-based applications. FST is capable to handle uncertainties while enhancing image quality and preserving its naturalness. This article proposes a novel Adaptive Interval Type-2 Fuzzy Filter (AIT2FF) as an AI agent for preserving the naturalness of nonuniformly illuminated images. The proposed AIT2FF estimates coarse illumination of the input image, which is further processed to obtain the reflectance and refined coarse illumination for the composition of enhanced image. The estimated coarse illumination preserves the naturalness by eliminating uncertainties due to grayness ambiguities in homogeneous regions and spatial ambiguities at edges. The effectiveness of the proposed filter has been presented quantitatively in terms of lightness order error (LOE) and naturalness image quality evaluator (NIQE) score on images from the high dynamic range dataset and images captured with commercial digital cameras. The qualitative comparison visualizes that the enhanced images obtained using the proposed filter maintains visual realism and naturalness. The applications of the proposed filter have also been presented for image dehazing, vehicle tracking, and gradients estimation. Moreover, the detection results for dehazed images have been compared with state-of-the-art methods on real-world task driven testing set from REalistic Single Image DEhazing- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> dataset. The comparisons demonstrate that the enhanced images obtained using the proposed AIT2FF approach are better and outperform others in terms of LOE and NIQE measures. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement</i>—The objective of vision-based applications is to automate the human visual perception. This requires a preprocessed input image with fine details. The image details to achieve human vision suffer from uncertainties in homogeneous regions and at edges. This result in an unnatural preprocessed image and degrades the performance. For example: Noisy images are not good to train deep learning models. AI offers one of the best tools, i.e., fuzzy set theory, which is capable to handle uncertainties. This article proposes an adaptive interval Type-2 fuzzy filter for elimination of uncertainties. The proposed filter enhances the image details and maintains its natural appearance. The elimination of uncertainties using proposed filter drops the error value from 2.573 to 1.929 for nonuniformly illuminated images. The proposed approach is able to boost the performance of various vision-based applications like object detection, vehicle tracking, security, medical diagnostics, etc.

Future long-term night sky monitoring projects shall use multispectral imaging sensors when possible

Interest in investigation of the impact of light pollution on ecology leads to a surge of observations and measurements. This article presents the measurement of the night sky radiance in over a period of 4.5 years in the city of Bremen, Germany from two sites situated in suburban and rural areas. It was found that both sites show a decreasing trend in sky radiance. However, since no multispectral images were taken, the exact cause of the decrease cannot be determined. It is therefore recommended that future night sky measurements should be based on multispectral imaging, for example by commercial digital cameras.

Excited State Absorption for Ratiometric Thermal Imaging.

Luminescence thermometry, an alternative to thermal imaging using the thermovision technique, requires the development of new approaches and a thorough understanding of the physical phenomena involved, in order to improve the temperature readout parameters. A phenomenon that has recently been shown to cause an extremely strong increase in the emission intensity for the temperature elevation is the thermally induced excited state absorption. This work demonstrates that taking advantage of the strong thermal dependence of the thermally induced excited state absorption process, the limitation associated with the two thermally coupled excited levels usually involved in the ratiometric temperature readout can be overcome, improving the thermometric properties of the luminescent thermometer. The same excitation wavelength was used to induce the emission resulting from the thermally induced excited state absorption of the Tb3+ ions and ground-state absorption of the other type of co-dopant ions causing the opposite nature of the thermal dependence of their emission intensities. Moreover, thanks to the strong color changes exhibited by the phosphors, it was possible to demonstrate the applicability of the proposed approach for through-object 2D thermal imaging of a microelectronic printed circuit board covered with a glass plate using an ordinary commercial digital camera, where the thermovision camera fails.

Rice nitrogen nutrition estimation with RGB images and machine learning methods

Crop red–green–blue (RGB) images are powerful tools in nitrogen (N) nutrition estimation. Various regression models using crop N nutrition parameters and image indices have been suggested, but their accuracy and generalization performance for N estimation have not been thoroughly evaluated. In this study, a commercial digital camera was used to capture rice canopy RGB images in a 2-year field experiment, and three regression methods (simple nonlinear regression, SNR; backpropagation neural network, BPNN; and random forest regression, RF) were used for rice shoot dry matter (DM), N accumulation (NA), and leaf area index (LAI) estimation. A repeated random subsampling validation method was performed 1000 times on all three regression methods for the evaluation of model performance and stability. The RF regression models had the highest accuracy for the validation dataset, with average testing prediction accuracy (ATPA) of 80.17%, 79.44%, and 81.82% for DM, LAI, and NA estimation, respectively, followed by BPNN and SNR models. According to the distribution of ATPA in 1000-time calculations, the highest standard deviation (SD) and interval range (5%–95%) of ATPA was observed in BPNN models, which indicated that the BPNN model was most susceptible to dataset splitting. The lower SD and interval range of ATPA were followed by RF and SNR models, which indicated that the RF and SNR models were less affected by dataset splitting and were able to produce robust regression models consistently. In conclusion, the ensemble algorithm of the RF model effectively prevents overfitting when dealing with different dataset segmentations; thus, the RF model has strong generalization performance. A combination of digital imagery and appropriate machine learning methods facilitates convenient and reliable estimation of crop N nutrition.

Continuous Monitoring of Fluvial Dike Breaching by a Laser Profilometry Technique

AbstractA nonintrusive, high‐resolution laser profilometry technique (LPT) has been developed for continuous monitoring of the three‐dimensional (3‐D) evolving breach in laboratory models of noncohesive fluvial dikes. This simple and low‐cost setup consists of a commercial digital video camera and a sweeping red diode 30 mW laser projecting a sheet over the dike. The 2‐D image coordinates of each deformed laser profile incident on the dike are transformed into 3‐D object coordinates using the direct linear transformation (DLT) algorithm. All 3‐D object coordinates computed over a laser sweeping cycle are merged to generate a cloud of points describing the instantaneous surface. The DLT‐based image processing algorithm uses control points and reference axes, so that no prior knowledge is needed on the position, orientation, and intrinsic characteristics of the camera, nor on the laser position. Because the dike is partially submerged, ad hoc refraction correction has been developed. Algorithms and instructions for the implementation of the LPT are provided. Reconstructions of a dike geometry with the LPT and with a commercial laser scanner are compared in dry conditions. Using rigid dike geometries, the repeatability of the measurements, the refraction correction, and the dike reconstruction have been evaluated for submerged conditions. Two laboratory studies of evolving fluvial dike breaching due to flow overtopping have been conducted to demonstrate the LPT capabilities and accuracy. The LPT has advantages in terms of flexibility and spatiotemporal resolution, but high turbidity and water surface waves may lead to inaccurate geometry reconstructions.

Convolutional Neural Network Training for RGBN Camera Color Restoration Using Generated Image Pairs

RGBN cameras that can capture visible light and near-infrared (NIR) light simultaneously produce better color image quality in low-light-level conditions. However, these RGBN cameras introduce additional color bias caused by the mixing of visible information and NIR information. The color correction matrix model widely used in current commercial color digital cameras cannot handle the complicated mapping function between biased color and ground truth color. Convolutional neural networks (CNNs) are good at fitting such complicated relationships, but they require a large quantity of training image pairs of different scenes. In order to achieve satisfactory training results, large amounts of data must be captured manually, even when data augmentation techniques are applied, requiring significant time and effort. Hence, a data generation method for training pairs that are consistent with target RGBN camera parameters, based on an open access RGB-NIR dataset, is proposed. The proposed method is verified by training an RGBN camera color restoration CNN model with generated data. The results show that the CNN model trained with the generated data can achieve satisfactory RGBN color restoration performance with different RGBN sensors.

Introducing the dark sky unit for multi-spectral measurement of the night sky quality with commercial digital cameras

Multi-spectral imaging radiometry of the night sky provides essential information on light pollution (skyglow) and sky quality. However, due to the different spectral sensitivity of the devices used for light pollution measurement, the comparison of different surveys is not always trivial. In addition to the differences between measurement approaches, there is a strong variation in natural sky radiance due to the changes of airglow. Thus, especially at dark locations, the classical measurement methods (such as Sky Quality Meters) fail to provide consistent results. In this paper, we show how to make better use of the multi-spectral capabilities of commercial digital cameras and show their application for airglow analysis. We further recommend a novel sky quality metric the “Dark Sky Unit”, based on an easily usable and SI traceable unit. This unit is a natural choice for consistent, digital camera-based measurements. We also present our camera system calibration methodology for use with the introduced metrics.