Color Distribution Research Articles

The impact of laser speckle on color spatial uniformity in laser-excited phosphor-converted white light systems

This study delved into the impact mechanisms of laser speckles on the quality of phosphor-converted white light, employing both simulation and experimental approaches. We comprehensively analyzed and assessed the interactions between laser speckle contrast, phosphor concentration, and color spatial uniformity in laser-excited phosphor-converted white light. Our findings indicate that while laser speckle does not shift the overall average color temperature, it significantly affects the spatial distribution of color within the emitted white light. Consequently, setting appropriate upper limits for laser speckle contrast, tailored to specific phosphor concentrations and lighting scenarios, is essential for engineering high-quality laser-excited phosphor white light. This research provides valuable data and practical guidelines for designing laser-excited phosphor illumination systems to optimize color spatial uniformity.

Physical Unclonable Functions with Hyperspectral Imaging System for Ultrafast Storage and Authentication Enabled by Random Structural Color Domains.

Physical unclonable function (PUF) is attractive in modern encryption technologies. Addressing the disadvantage of slow data storage/authentication in optical PUF is paramount for practical applications but remains an on-going challenge. Here, a highly efficient PUF strategy based on random structural color domains (SCDs) of cellulose nanocrystal (CNC) is proposed for the first time, combing with hyperspectral imaging system (HIS) for ultrafast storage and authentication. By controlling the growth and fusion behavior of the tactoids of CNC, the SCDs display an irregular and random distribution of colors, shapes, sizes, and reflectance spectra, which grant unique and inherent fingerprint-like characteristics that are non-duplicated. Based on images and spectra, these fingerprint features are used to develop two sets of PUF key generation methods, which can be respectively authenticated at the user-end and the manufacturer-front-end that achieving a high coding capacity of at least 22304. Notably, the use of HIS greatly shortens the time of key reading and generation (≈5s for recording, 0.5-0.7s for authentication). This new optical PUF labels can not only solve slow data storage and complicated authentication in optical PUF, but also impulse the development of CNC in industrial applications by reducing color uniformity requirement.

A national-scale assessment on the spatial and temporal variations in water color for urban lakes in China

Monitoring the variations of lake water quality is essential for urban water security and sustainable eco-environment health. However, it is challenging to investigate the water quality of urban lakes at large scales due to the need for large-amount in situ data with diverse optical properties for developing the remote sensing inversion algorithms. Forel-Ule Index (FUI), a proxy of quantifying water color, whose calculation does not require in situ data of specific properties, can comprehensively reflect water quality conditions. However, the spatial and temporal distribution of water color in Chinese urban lakes is still poorly understood. To fill this research gap, this study investigated the spatial distribution of water color in 523 urban lakes (area > 0.5 km2) in China using the FUI derived from the high-quality Multi-Spectral Instrument (MSI) data onboard Sentinel-2 during the ice-free period (April–October) from 2019 to 2022. The monthly and seasonal variation patterns of water color in urban lakes were also analyzed. Our results show that green domain is the most common color of urban lakes, with about 86 % of urban lakes in China being green, and non-green lakes accounting for only 14 % of the total number of lakes. The monthly variation of FUI in urban lakes across the country and multiple geographic regions is basically the same. The monthly average FUI first increases, then decreases, and then rebounds. We also found that the seasonal variation of water color in most urban lakes in southern and northern China is opposite. This study helps to comprehensively understand the spatial and temporal variation of water color and quality of urban lakes in China, providing key basic information for the protection and governance of urban lakes.

Effect of different thawing methods on the quality of yellowtail (Seriola quinqueradiata) fillets

SummaryThe effects of hydrostatic thawing (HT), flowing water thawing (FT), ultrasonic thawing (UT), microwave thawing (MT), and microwave‐combined ultrasonic thawing (MUT) on the quality of yellowtail fillets were investigated via thawing time, physicochemical quality, water retention, microstructure and water migration distribution. The results showed that MUT had a shorter thawing time, second only to microwave thawing (MT) and significantly shorter than other thawing methods. Moreover, MUT could retain a better performance of the samples, such as colour, texture, water retention and moisture distribution, and prevent damage to the microstructure. In addition, freshness analysis showed that pH, total volatile saline nitrogen and K values were optimal for MUT. Therefore, MUT could be a viable thawing method that increases the thawing rate of yellowtail fillets while maintaining their quality characteristics.

Analysis of microscopic images using image processing to measure homogeneity of metal powder mixture samples

This paper presents automated image analysis frameworks that leverage Python's computer vision libraries, including OpenCV and Matplotlib, to analyze and characterize color-shade distributions in microscopic images of metal powder mixtures. The proposed framework segments the images into multiple regions of interest (ROIs), calculates and visualizes color histograms, and extracts quantitative information about the color distribution within each ROI. The developed image processing framework measures the homogeneity of the powder mixture by surface analysis of the image of samples. This article demonstrates the efficacy of these frameworks on a set of microscopy images, highlighting their potential to measure the homogeneity of the metal powder mixture. Homogeneity is a crucial factor in material science, as it directly impacts the performance and properties of the resulting material. The developed technique is economical compared to available methods to predict the homogeneity of metal powder mixtures, such as Tomographic and Spectroscopic analyses. It is an effective technique to measure the homogeneity of metal powder mixtures exhibiting different colors or shades of color in computer vision. In this work, the homogeneity is analyzed for the powder mixtures consisting of 80% aluminum (Al) and 20% copper (Cu), 90% aluminum (Al) and 10% copper (Cu) and 90% silicon carbide (SiC) and 10% aluminum (Al) using processing of microscopic color images.

Universal scale laws for colors and patterns in imagery

Distribution of colors and patterns in images is observed through cascades that adjust spatial resolution and dynamics. Cascades of colors reveal the emergent universal property that Fully Colored Images (FCIs) of natural scenes adhere to the debated continuous linear log-scale law (slope −2.00±0.01) (L1). Cascades of discrete 2×2 patterns are derived from pixel square reductions onto the seven unlabeled rotation-free textures (0000, 0001, 0011, 0012, 0101, 0102, 0123). They exhibit an unparalleled universal entropy maximum of 1.74±0.013 at some dynamics regardless of spatial scale (L2). Patterns also adhere to the Integral Fluctuation Theorem (1.00±0.01) (L3), pivotal in studies of chaotic systems. Images with fewer colors exhibit quadratic shift and bias from L1 and L3 but adhere to L2. Randomized Hilbert fractal FCIs better match the laws than basic-to-AI-based simulations. Those results are of interest in Neural Networks, out-of-equilibrium physics, and spectral imagery.

Type of pectin affects the functionality of potato protein-pectin conjugates in emulsions

This study investigated the effect of the molecular structure of pectin on conjugate formation and its functionality in plant protein-based emulsions. For this purpose, high-methoxylated, low-methoxylated, amidated citrus pectin and their mixtures with potato protein were vacuum-dried at defined process conditions. The change in free amino groups, colour and molecular weight distribution assessed conjugation. The side reaction, such as pectin degradation, was monitored by the degree of methoxylation, galacturonic acid content and intrinsic viscosity. In addition, FT-IR and hydrophobicity measurements were performed to determine the changes in chemical structure due to the occurring reactions. The ζ-potential, solubility and oil droplet size reflected the functionality of the conjugated samples.The results confirm that, apart from the conjugation of protein and pectin, where the most significant decrease in free amino groups was obtained with an increasing degree of methoxylation or presence of amide groups, undesirable degradation of pectin occurring, resulting in a broad molecular weight distribution of the conjugate samples. The conjugate formation was the highest with amidated pectin. The FT-IR measurements confirmed the formation of Maillard conjugates. The FT-IR spectra showed a decrease in the intensity signals of amide I and amide II and the formation of new C–N bond maxima. In addition, the conjugation caused the shift of the protein's isoelectric point to the acidic environment, resulting in higher solubility and improved emulsion stability at the isoelectric point of the protein.Regarding emulsion stability, the conjugates with amidated pectin performed best and achieved the narrowest oil droplet size distribution over a wider pH range. Emulsion-stabilising properties of conjugates with high- and low-methoxylated pectin differed only slightly. To better understand the underlying mechanisms for emulsion stabilisation, future research will focus on the interfacial properties of the conjugates as determined by interfacial shear rheology and pendant drop analysis.

Effects of Pr3+ -activated BaZrGe3O9@TiO2 phosphor compound on light emitting diodes validated by computer simulation

The Pr<sup>3+</sup> -doped BaZrGe<sub>39</sub> gallogermanate phosphors are reported to have a well-defined successive deep defect structure that effectively mitigates thermal carrier fading. This phosphor also presents a red emission with a peak at 615 nm, originating from the Pr<sup>3+</sup> transtition from <sup>1</sup>D<sub>2</sub> to <sup>3</sup>H<sub>4</sub>. We investigated the impact of Pr<sup>3+</sup> -activated BaZrGe3O9 (referred to as BZG:Pr) on the lighting characteristics of light emitting diodes (LED) packages in this paper. By combining BZG:Pr with TiO<sub>2</sub> particles and silicone, we produced a phosphor layer (designated as BZG:Pr@TiO<sub>2</sub>). The optical performance of the resulting LED was systematically examined by varying the TiO<sub>2</sub> doping percentage. Our findings reveal that the incorporation of the BZG:Pr phosphor enhances the red spectral component, thereby contributing to improved homogeneity in color distribution. However, a progressive increase in TiO<sub>2</sub> content within the phosphor layer corresponds to diminishing luminous output and decreased chromatic rendering efficiency of the LED. Employing a lower concentration of TiO<sub>2</sub> proves advantageous, as it capitalizes on the scattering-enhancing attributes while leveraging the red emission of the BZG:Pr phosphor. This synergistic approach yields a favorable balance between luminosity and color quality, enhancing the LED’s overall performance.

10‐1: Optical Simulation and Improvement of the Reflection Pattern of Polarizer‐Free OLED Panel

The reflection color dispersion phenomenon is more visible on polarizer‐free OLED panel compared to the panel with polarizer. However, the mechanism and detailed analysis of this effect is very limited in previous reports. The panel reflection patterns are usually very sophisticated, which are composed of rings, dots and color distribution (R G B reflection located at different direction of the panel, respectively). In this paper, the causes of the reflection pattern are analyzed in detail and the influential factors of the shape, distribution, color and other characteristics of the reflection pattern are clarified and verified through the comparison of simulation and actual results. Finally, ideas for improving reflection pattern are proposed.

78‐1: Invited Paper: Gamut Rings Color Scope for Use with Wide Gamut Display Systems

Traditional chromaticity scopes and vectorscopes have limited ability to visualize color appearance attributes, which limits color grading and color correction. A new tool based on gamut rings can monitor the lightness, chroma, and hue distributions of the pixel colors of input images in a single 2D diagram. The perceptually intuitive visualization is expected to facilitate more efficient and effective color grading, thereby broadening the market's understanding of the benefits of wide gamut displays.

Image Colorization

Abstract: Image colorization is a complex and challenging task in computer vision, with numerous applications in art, entertainment, restoration, and more. This project aims to develop an automated image colorization system leveraging the power of deep learning techniques. The primary objective is to train a deep neural network model capable of accurately and semantically colorizing grayscale images, reproducing natural and visually appealing colour distributions. Our approach utilizes Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to learn the intricate relationships between grayscale images and their corresponding colour versions. The project involves the following key steps: Data Collection and Preprocessing, Model architecture, Training, Evaluation, Application. The project's outcome is expected to provide a powerful and versatile tool for automating image colorization tasks, offering high-quality results while preserving the artistic intent of the original images. The fusion of deep learning and computer vision techniques in this project exemplifies the potential for artificial intelligence to revolutionize image processing and creative industries.

Visualized Sensing‐Integrated Multi‐Responsive Soft Actuator for On‐Demand Robotic Manipulation

AbstractResearch on small‐scale soft robots has emerged in recent years, and various soft actuators have been developed to implement various actions. Nevertheless, realizing self‐sensing alongside environmental sensing capabilities remains a challenge, largely due to the constraints imposed by compact dimensions and the limited load‐bearing capacity intrinsic to these robots. In this study, an innovative approach is introduced through the development of a visualized sensing integrated soft actuator, which harnesses the actuator's color as a straightforward and real‐time sensing medium. Additionally, a multi‐responsive actuation mechanism is adopted in which the actuator responds concurrently to both electric and magnetic fields. To exemplify the efficacy of this concept, the actuators are engineered as flexible soft grippers, thereby accommodating functions encompassing grasping and transporting. Relying on the color distribution manifested by the actuator, the actuator's self‐sensing ability alongside its capacity to discern object temperatures is demonstrated. Such a soft actuator provides new perspectives in the realm of soft robotics, showing great potential in diverse robotic applications.

Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity.

The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.

Research on High Performance Apple Recognition Model Construction Strategy Based on Deep Learning

In order to improve the working efficiency and environmental adaptability of apple picking robots, which can realize accurate recognition and localization, ripening assessment, and quality assessment under complex situations such as "leaf occlusion", "branch occlusion", "fruit occlusion", "mixed occlusion", etc., this paper proposes a Faster R-CNN-based apple recognition method. "mixed occlusion" and other complex situations to achieve accurate recognition and localization, maturity assessment, quality assessment, the paper proposes an apple recognition method based on Faster R-CNN. The method combines the cv2.inRange and cv2.fitEllipse functions in OpenCV to optimize the model, and performs color segmentation and shape fitting by setting up color and shape thresholds to recognize apples. The trained model was tested to have a recall of 88.44% under the validation set, an accuracy of 90.35%, an F1 value of 89.38%, and a recognition time of about 0.3 s per image.The experimental results showed that the number of apples in the dataset showed a normal distribution, with the number distributed more in the range of 0 to 10. Candidate frames are screened by RPN, and apple coordinates are derived based on the coordinate transformation formula, and the experimental results show that the apples are more densely distributed near the range of x=100, y=100. The image color distribution was extracted through color histogram, and the entropy value of GLCM, contrast as the texture feature of apples, and the classification prediction of apple ripeness was carried out by using Random Forest Model, and the accuracy of the model prediction was 83.2%, the precision rate was 84.1%, and the recall rate was 83.5%, and the experimental results showed that 88.7% of the apples were ripe. Image edges were extracted by the Canny edge detection algorithm and the area values of pixel points within the edges were calculated. The ratio of pixel area converted to apple mass was estimated to be 0.007 based on the image resolution and apple density, and the test results indicated that the mass of apples was mostly concentrated in the range of 0~100g.

Rapid Fabrication of Large-Grain Opal Films and Photonic Crystal Hydrogel Sensors by a Filter Paper-Enhanced Evaporation Chip.

Developing a rapid fabrication method for crack-free opal films is a significant challenge with broad applications. We developed a microfluidic platform known as the "filter paper-enhanced evaporation microfluidic chip" (FPEE-chip) for the fabrication of photonic crystal and inverse opal hydrogel (IOPH) films. The chip featured a thin channel formed by bonding double-sided adhesive poly(ethylene terephthalate) with a polymethyl methacrylate cover and a glass substrate. This channel was then filled with nanosphere colloids. The water was guided to evaporate rapidly at the surface of the filter paper, allowing the nanospheres to self-assemble and accumulate within the channel under capillary forces. Experimental results confirmed that the self-assembly method based on the FPEE-chip was a rapid platform for producing high-quality opal, with centimeter-sized opal films achievable in less than an hour. Furthermore, the filter paper altered the stress release mechanism of the opal films during drying, resulting in fewer cracks. This platform was proven capable of producing large-grain, crack-free opal films of up to 30 mm2 in size. We also fabricated crack-free IOPH pH sensors that exhibited color and size responsiveness to pH changes. The coefficient of variation of the gray color distribution for crack-free IOPH ranged from 0.03 to 0.07, which was lower than that of cracked IOPH (ranging from 0.07 to 0.14). Additionally, the grayscale peak value in 1 mm2 of the crack-free IOPH was more than twice that of the cracked IOPH at the same pH. The FPEE-chip demonstrated potential as a candidate for developing vision sensors.

The original composition of the gas forming first-generation stars in clusters: Insights from HST and JWST

Globular cluster (GC) stars composed of pristine material, also known as first-generation (1G) stars, are not chemically homogeneous as they exhibit extended sequences in the chromosome map (ChM). Recent studies characterized 1G stars within the center of 55 Galactic GCs, revealing metallicity variations. Despite this progress, several unanswered questions persist, particularly concerning the link between the 1G metallicity spread and factors such as the radial distance from the cluster center or the host GC parameters. Additionally, it remains unclear whether the extended 1G sequence phenomenon is exclusive to old Galactic GCs with multiple populations. This work addresses these open issues, examining 1G stars in different environments. First, we combine Hubble Space Telescope (HST) and James Webb Space Telescope photometry of the GC 47\,Tucanae to study 1G stars at increasing distances from the cluster center. We find that metal-rich 1G stars are more centrally concentrated than metal-poor ones, suggesting a metallicity radial gradient. Additionally, the two groups of 1G stars share similar kinematics. Since our analysis focuses on giant stars in the cluster center and M dwarfs in external fields, we discuss the possibility that the metallicity distribution depends on stellar mass. Subsequently, we analyze HST multi-band photometry of two simple-population clusters, NGC\,6791 and NGC\,1783, revealing elongated sequences in the ChM associated with metallicity variations. Finally, we investigate the 1G color distribution in 51 GCs, finding no connections with the host cluster parameters. These results shed light on the complex nature of 1G stars, providing insights into the GC formation environment.

Microplastics pollution in the Surma River, Bangladesh: A rising hazard to upstream water quality and aquatic life

The ecological health of freshwater rivers is deteriorating globally due to careless human activities, for instance, the emission of plastic garbage into the river. The current research was the first assessment of microplastics (MPs) pollution in water, sediment, and representative organisms (fish, crustacean, and bivalve) from the Surma River. Water, sediment, and organisms were sampled from six river sites (Site 1: Charkhai; Site 2: Golapganj; Site 3: Alampur; Site 4: Kazir Bazar; Site 5: Kanishail and Site 6: Lamakazi), and major water quality parameters were recorded during sampling. Thereafter, MPs in water, sediment, and organism samples were extracted, and then microscopically examined to categorize selected MPs types. The abundance of MPs, as well as size, and color distribution, were estimated. Polymer types were analyzed by ATR-FTIR, the color loss of MPs was recorded, the Pollution Load Index (PLI) was calculated, and the relationship between MPs and water quality parameters was analyzed. Sites 4 and 5 had comparatively poorer water quality than other sites. Microplastic fibers, fragments, and microbeads were consistently observed in water, sediment, and organisms. A substantial range of MPs in water, sediment, and organisms (37.33–686.67 items/L, 0.89–15.12 items/g, and 0.66–48.93 items/g, respectively) was recorded. There was a diverse color range, and MPs of <200 μm were prevalent in sampling areas. Six polymer types were identified by ATR-FTIR, namely Polyethylene (PE), Polyamide (PA), Polypropylene (PP), Cellulose acetate (CA), Polyethylene terephthalate (PET), and Polystyrene (PS), where PE (41%) was recognized as highly abundant. The highest PLI was documented in Site 4 followed by Site 5 both in water and sediment. Likewise, Sites 4 and 5 were substantially different from other study areas according to PCA. Overall, the pervasiveness of MPs was evident in the Surma River, which requires further attention and prompt actions.

Simulation-based inference of deep fields: galaxy population model and redshift distributions

Accurate redshift calibration is required to obtain unbiased cosmological information from large-scale galaxy surveys. In a forward modelling approach, the redshift distribution n(z) of a galaxy sample is measured using a parametric galaxy population model constrained by observations. We use a model that captures the redshift evolution of the galaxy luminosity functions, colours, and morphology, for red and blue samples. We constrain this model via simulation-based inference, using factorized Approximate Bayesian Computation (ABC) at the image level. We apply this framework to HSC deep field images, complemented with photometric redshifts from COSMOS2020. The simulated telescope images include realistic observational and instrumental effects. By applying the same processing and selection to real data and simulations, we obtain a sample of n(z) distributions from the ABC posterior. The photometric properties of the simulated galaxies are in good agreement with those from the real data, including magnitude, colour and redshift joint distributions. We compare the posterior n(z) from our simulations to the COSMOS2020 redshift distributions obtained via template fitting photometric data spanning the wavelength range from UV to IR. We mitigate sample variance in COSMOS by applying a reweighting technique. We thus obtain a good agreement between the simulated and observed redshift distributions, with a difference in the mean at the 1σ level up to a magnitude of 24 in the i band. We discuss how our forward model can be applied to current and future surveys and be further extended. The ABC posterior and further material will be made publicly available at https://cosmology.ethz.ch/research/software-lab/ufig.html.

Enhancing skin lesion segmentation with a fusion of convolutional neural networks and transformer models

Accurate segmentation is crucial in diagnosing and analyzing skin lesions. However, automatic segmentation of skin lesions is extremely challenging because of their variable sizes, uneven color distributions, irregular shapes, hair occlusions, and blurred boundaries. Owing to the limited range of convolutional networks receptive fields, shallow convolution cannot extract the global features of images and thus has limited segmentation performance. Because medical image datasets are small in scale, the use of excessively deep networks could cause overfitting and increase computational complexity. Although transformer networks can focus on extracting global information, they cannot extract sufficient local information and accurately segment detailed lesion features. In this study, we designed a dual-branch encoder that combines a convolution neural network (CNN) and a transformer. The CNN branch of the encoder comprises four layers, which learn the local features of images through layer-wise downsampling. The transformer branch also comprises four layers, enabling the learning of global image information through attention mechanisms. The feature fusion module in the network integrates local features and global information, emphasizes important channel features through the channel attention mechanism, and filters irrelevant feature expressions. The information exchange between the decoder and encoder is finally achieved through skip connections to supplement the information lost during the sampling process, thereby enhancing segmentation accuracy. The data used in this paper are from four public datasets, including images of melanoma, basal cell tumor, fibroma, and benign nevus. Because of the limited size of the image data, we enhanced them using methods such as random horizontal flipping, random vertical flipping, random brightness enhancement, random contrast enhancement, and rotation. The segmentation accuracy is evaluated through intersection over union and duration, integrity, commitment, and effort indicators, reaching 87.7 % and 93.21 %, 82.05 % and 89.19 %, 86.81 % and 92.72 %, and 92.79 % and 96.21 %, respectively, on the ISIC 2016, ISIC 2017, ISIC 2018, and PH2 datasets, respectively (code: https://github.com/hyjane/CCT-Net).

A Novel Laboratory Technique for Measuring Grain‐Size‐Specific Transport Characteristics of Bed Load Pulses

AbstractAlthough several laboratory studies on the propagation of bed load pulses were carried out in the last decades, most studies neglect grain‐size‐specific aspects or use invasive measurement techniques. To remedy the situation, we present a novel, time‐efficient and non‐destructive laboratory technique to investigate grain‐size‐specific transport characteristics of bed load pulses. The method consists of a through‐water, high‐resolution image acquisition followed by the application of a supervised color classification algorithm (Gaussian Maximum Likelihood Classification). The analyzed bed load pulse consisted of five different grain size classes of dyed quartz sand and gravel, each having a unique color. The initial experimental bed was uni‐colored and contained the same size fractions as the augmented pulse. Quality assessment based on a confusion matrix approach and basic random sampling showed a high classification performance. By statistically analyzing the temporal and spatial color distribution of the experimental reach, characteristic parameters to describe the propagation behavior were determined. The bed load pulse presented in the application example initially showed strong deviations in the grain‐size‐specific advection and dispersion, and advection proved to be predominant in the transport process.