Radiometric Characteristics Research Articles

GAN-Based Dual Image Super Resolution for Satellite Imagery Decreasing Radiometric Uncertainty

Abstract. Super resolution for satellite imagery possesses specific challenges due to its unique feature geometry compared to classic computer vision. Specifically, satellite images contain a high amount of relatively small, distributed high-frequency features that are hard to preserve when sampling up to a higher resolution. General adversarial networks (GANs) are suitable for super resolution tasks but need special attention concerning the geometric and radiometric accuracy of the results. We propose a network for versatile satellite imagery super resolution (VSISR) that focuses on high-frequency detail preservation and radiometric consistency. As a novelty, it is able to utilize a reference high resolution image during inference to lower hallucinations without altering the source image’s radiometric characteristics.A GAN that is already handling well high frequencies in standard computer vision cases is adapted for satellite imagery and supplemented with a mixed pixel approach for data augmentation. Training on a diverse RGB dataset from four satellite missions results in a versatile super resolution model that is optimized to preserve radiometric features and minimize hallucinations. Compared to other neural networks for satellite image super resolution in their respective datasets, VSISR performs in the middle regarding mean PSNR (25.30 dB) and SSIM (0.8098). Nevertheless, it is the first time that, using mixed pixel training on a comparably small dataset, this versatility concerning the satellite data source is achieved while maintaining their unique radiometric traits.

Radiometric characterization of daytime luminescent materials directly under the solar illumination

The materials efficiently emitting photoluminescence (PL) under solar irradiation could find broad applications in passive radiative cooling of buildings, urban heat mitigation, and solar energy harvesting. In this work, we discuss the limitations of common laboratory characterization of such materials (using tunable light sources or solar simulators) and propose a methodology for direct outdoor characterization of PL efficiency under full solar irradiation. A simple, portable radiometry setup with an integrating sphere is described, and its capability of rapid determination of the spectral distribution of the absorbed and emitted power, as well as the overall PL power efficiency and quantum yield, is demonstrated. By characterization of three materials developed for daytime radiative cooling applications, we reveal deviations of obtained parameters caused by the replacement of the real solar irradiation by that of solar simulators. The described method is suitable for studies of long-term stability, photo-induced degradation, or thermal effects.

Radiometric and petrographic characterization of El-Yatima granite: Evaluating radiological risks and mineralogical features

The current investigation aims to assess the potential of El-Yatima granitic pluton as a decorative stone by examining its natural radioactivity and mineralogical elements. Specifically, the study aims to determine the concentrations of radionuclides 40K, 232Th, and 226Ra, perform petrographic analyses, and evaluate the associated radiological risks to determine the safety and usability of the granite in construction and decoration, as well as to assess the potential of these granitic rocks as a safe and effective decorative stone for various applications. It has a 12 km2 surface area of 812 m above sea level. It is situated in the Eastern Central Desert. Its hypidiomorphic texture, medium-to-coarse grain size, and predominant composition of K-feldspars, quartz, and plagioclase are observed, along with minute amounts of muscovite and biotite. By employing NaI (Tl) gamma-ray spectroscopy, the natural radionuclide concentrations of 40K, 232Th, and 226Ra in El-Yatima granitic rocks were ascertained. Additionally, radiological concerns were identified for the materials that were examined. The average concentrations of radionuclides 40K, 232Th, and 226Ra are 178 Bqkg−1, 44 Bqkg−1, and 41 Bqkg−1, respectively. The following are the computed values: 117 Bqkg−1, 0.43, 0.32, 53 nGyh−1, average radium equivalent (Raeq), hazard indices (Hin and Hex) and dose rate (D), respectively. When compared to levels typically indicated, it was discovered that its values were within the worldwide standard. The study's findings could be a starting point for radiometric data for upcoming surveillance and epidemiological research.

Prediction of metastases in confusing mediastinal lymph nodes based on flourine-18 fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) imaging using machine learning.

For patient management and prognosis, accurate assessment of mediastinal lymph node (LN) status is essential. This study aimed to use machine learning approaches to assess the status of confusing LNs in the mediastinum using positron emission tomography/computed tomography (PET/CT) images; the results were then compared with the diagnostic conclusions of nuclear medicine physicians. A total of 509 confusing mediastinal LNs that had undergone pathological assessment or follow-up from 320 patients from three centres were retrospectively included in the study. LNs from centres I and II were randomised into a training cohort (N=324) and an internal validation cohort (N=81), while those from centre III patients formed an external validation cohort (N=104). Various parameters measured from PET and CT images and extracted radiomics and deep learning features were used to construct PET/CT-parameter, radiomics, and deep learning models, respectively. Model performance was compared with the diagnostic results of nuclear medicine physicians using the area under the curve (AUC), sensitivity, specificity, and decision curve analysis (DCA). The coupled model of gradient boosting decision tree-logistic regression (GBDT-LR) incorporating radiomic features showed AUCs of 92.2% [95% confidence interval (CI), 0.890-0.953], 84.6% (95% CI, 0.761-0.930) and 84.6% (95% CI, 0.770-0.922) across the three cohorts. It significantly outperformed the deep learning model, the parametric PET/CT model and the physician's diagnosis. DCA demonstrated the clinical usefulness of the GBDT-LR model. The presented GBDT-LR model performed well in evaluating confusing mediastinal LNs in both internal and external validation sets. It not only crossed radiometric features but also avoided overfitting.

Classifying Microwave Radiometer Observations over the Netherlands into Dry, Shallow, and Nonshallow Precipitation Using a Random Forest Model

Abstract Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands—a region with varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels (≥85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles. Significance Statement Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

Mapping Longitudinal and Transverse Displacements of a Dam Crest Based on the Synergy of High-Precision Remote Sensing

Reservoirs are highly relevant infrastructure assets, and now, more than ever, they play an essential role in society’s welfare and national security. Their importance is related to regional socioeconomic development due to their capacity to store water for different uses, such as human consumption, agricultural irrigation, flood control, and hydroelectric energy production, among other important services. However, many reservoirs are reaching the end of their period of life, and others are showing undesired displacements and cracking. Four 3D surveys were conducted on a reservoir that serves the Metropolitan Area of Monterrey City in Mexico. These surveys were carried out over a period of 5 years using GNSS observation to assist in understanding the actual dam kinematics, i.e., the behavior of its longitudinal and transversal displacements and the possible correlation with the reservoir level. The high-precision leveling and close-range remote sensing data were assessed and then mapped. The high-precision geodetic and leveling techniques allowed us to locate and measure 84 established permanent control points with errors of about ± 0.003 m. The mapping of displacements was made possible by modeling the positive and negative translations. The highest uplifts (11 mm) occurred at the left riverbank, and the highest subsidences (−5 mm) occurred along the downstream piers from the middle of the dam crest to the right riverbank. A ground laser scanner (GLS) produced 3D digital models with geometrical and radiometric characteristics, detecting displacements among the dam crest elements. The synergy of GNSS and high-leveling techniques allows the possibility to measure displacements, while the use of geographical information system (GIS) and geomatic techniques allows a better visualization through 2D and 3D maps validated using traditional topographical methods.

Gamma spectrometry data suggest opportunities for mineral research in Alagoas, Brazil

This article presents the processing and quantitative interpretation outcomes of aero-gamma-spectrometric data within six regions renowned for copper and iron occurrences in Alagoas State, Brazil, situated within the geological framework of the Sergipan Orogen. The study primarily focuses on radioelement concen-tration data and its derived relationships, encompassing the creation of a ternary map and the utilization of targeting techniques to identify radiometric anomalies, such as potassium and anomalous uranium, along with the Factor F. These relationships enable the identification of behaviors associated with distinct geological models of mineralization, establishing connections between geophysical anomalies and well-known mineral occurrences. By integrating concentration information of radioelements K, Th, and U, in conjunction with geological data and existing mineralizations, the radiometric distributions of the region are thoroughly scrutinized. Radiomet-ric ratios and ternary maps serve to facilitate the identification of enrichment centers for target minerals and the observation of desirable radiometric signatures. Hydrothermal enrichment enhancement techniques unveil a more pronounced correlation between copper targets and potassium (K) anomalies, whereas iron targets ex-hibit a relationship with uranium (U) enrichment. A ternary map, amalgamating anomalous potassium, K/Th ratio, and Factor F, aids in the identification of hydrothermal alterations. Magnetic maps emphasize anomalies near significant indications, accentuating structurally advantageous contours for hydrothermal enrichment and contacts with magnetic minerals associated with iron deposits. Future exploration endeavors should accord pri-ority to these selected areas to unveil their mineral potential. Furthermore, the findings from this study offer valuable insights into the radiometric characteristics and their associations with ore deposits, thereby guiding investigations in other mineral-rich regions, always considering local geology as a vital initial datum.

The ARGOS Instrument for Stratospheric Aerosol Measurements

Atmospheric aerosols represent an important component of the Earth’s climate system because they can contribute both positive and negative forcing to the energy budget. We are developing the Aerosol Radiometer for Global Observations of the Stratosphere (ARGOS) instrument to provide improved measurements of stratospheric aerosols in a compact package. ARGOS makes limb scattering measurements from space in eight directions simultaneously, using two near-IR wavelengths for each viewing direction. The combination of forward and backward scattering views along the orbit track gives additional information to constrain the aerosol phase function and size distribution. Cross-track views provide expanded spatial coverage. ARGOS will have a demonstration flight through a hosted payload provider in the fall of 2024. The instrument has completed pre-launch environmental testing and radiometric characterization tests. The hosted payload approach offers advantages in size, weight, and power margins for instrument design compared to other approaches, with significant benefits in terms of reducing infrastructure requirements for the instrument team.

Predicting Microwave Ablation Early Efficacy in Pulmonary Malignancies via Δ Radiomics Models.

This study aimed to explore the value of preoperative and postoperative computed tomography (CT)-based radiomic signatures and Δ radiomic signatures for evaluating the early efficacy of microwave ablation (MWA) for pulmonary malignancies. In total, 115 patients with pulmonary malignancies who underwent MWA treatment were categorized into response and nonresponse groups according to relevant guidelines and consensus. Quantitative image features of the largest pulmonary malignancies were extracted from CT noncontrast scan images preoperatively (time point 0, TP0) and immediately postoperatively (time point 1, TP1). Critical features were selected from TP0 and TP1 and as Δ radiomics signatures for building radiomics models. In addition, a combined radiomics model (C-RO) was developed by integrating radiomics parameters with clinical risk factors. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). The radiomics model using Δ features outperformed the radiomics model using TP0 and TP1 features, with training and validation AUCs of 0.892, 0.808, and 0.787, and 0.705, 0.825, and 0.778, respectively. By combining the TP0, TP1, and Δ features, the logistic regression model exhibited the best performance, with training and validation AUCs of 0.945 and 0.744, respectively. The DCA confirmed the clinical utility of the Δ radiomics model. A combined prediction model, including TP0, TP1, and Δ radiometric features, can be used to evaluate the early efficacy of MWA in pulmonary malignancies.

A calibration methodology for the novel radioluminescence detector systems

Radioluminescence mapping using well-characterized and optimized optical systems is an effective method for localizing contamination with alpha-emitting radionuclides. To apply this novel approach to radiological emergency management, nuclear safeguards, nuclear decommissioning, and nuclear forensics, an established traceability chain is required. This work presents the development and implementation of a novel calibration methodology to provide valuable information about, and confidence in, the performance of radioluminescence detection systems. The proposed calibration methodology is based on two complementary approaches: (a) application of well-characterized activity standards to establish a traceable relationship between radioluminescence intensity and alpha activity, and (b) use of all optical radiation-based devices that, when calibrated against an alpha activity standard, simulate the radioluminescence induced in nitrogen (N2) and nitric oxide (NO) gases by alpha particles in specific spectral regions. A dedicated 210Po alpha activity standard with a narrow peak of less than 32keV FWHM at 5.3MeV has been developed and used to characterize a lens-based radioluminescence detection system in terms of its sensitivity to alpha-induced radioluminescence in different atmospheres (air, N2, N2 ＋ NO mixture) in the UV-A and UV-C (solar blind) spectral regions. The characterized lens system was then used as an intermediary transfer device to cross-calibrate two portable integrating sphere-based radiance standards designed to simulate radioluminescence in the UV-A and UV-C spectral regions. Both radiance standards can be used as transfer standards simulating up to 510MBq when used in the UV-A and up to 8.7GBq when used in the UV-C spectral regions. They substantially simplify routine quality control of radioluminescence detection systems by eliminating the need for open alpha sources, which are always associated with strict radiation safety precautions. Furthermore, since radiance standards can operate in a wide range of intensities, linearity and detection limits of radioluminescence detectors can be readily determined. The design, construction, radiometric characterization, and calibration of dedicated transfer standards, as well as the development of new calibration procedures for radiometric traceability of radioluminescence detection systems, will enable appropriate accident and post-accident radiation measurements that will lead to more effective countermeasures and better protection of people, wildlife, and the environment.

Landsat 9 Transfer to Orbit of Pre-Launch Absolute Calibration of Operational Land Imager (OLI)

Landsat 9 Operational Land Imager (L9-OLI) was launched on 27 September 2021, after completing a successful radiometric pre-launch calibration and characterization phase. The radiometric math model that governs the ground system—the data processing and analysis system (DPAS)—uses various calibration parameters that had been derived based on the pre-launch tests and analysis. During the on-orbit commissioning phase, the OLI system acquired specific sets of data collects, which enabled the revalidation of the pre-launch absolute calibration scale and other associated instrument performance characteristics. The analysis results shown in this paper focus on the activities and results related to the transfer-to-orbit analysis for the SI-traceable pre-launch radiometric scale. Key topics discussed in this paper include: radiance and reflectance calibration parameters for OLI; solar diffuser collects; stimulation-lamp collects; dark response; signal-to-noise ratios; and noise characteristics; radiometric response stability and the on-orbit update to the radiance to reflectance conversion factors. It will be shown that the OLI response during the early on-orbit operation matched pre-launch results and therefore this re-validates the absolute radiometric scaling at the predicted pre-launch level within the expected level of uncertainties. The launch did not cause any significant changes to the OLI system from the perspective of the absolute radiometric calibration performance. Once the transfer to orbit of the absolute calibration was confirmed, it created a solid basis for further on-orbit refinements of the radiance calibration parameters. As such, follow-on calibration refinements are discussed in other articles within this special issue, and they address issues such as uniformity as well as cross-calibration activities.

VIIRS Version 2 Deep Blue Aerosol Products

AbstractNASA's Deep Blue aerosol project has developed global aerosol data records using consistent retrieval algorithms applied to various satellite sensors. The primary components of these data records are derived from the series of Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar‐orbiting Partnership (SNPP) and the National Oceanic and Atmospheric Administration or NOAA‐20+ satellites as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), among others. These instruments provide over 23 years of measurements with similar radiometric characteristics for aerosol retrievals. The algorithms used for the initial Version 1 SNPP VIIRS data set were based on the MODIS Collection 6.1 Deep Blue algorithm over land and Satellite Ocean Aerosol Retrieval (SOAR) algorithm over water. For VIIRS Version 2 data reprocessing, major updates have been made to the algorithm suite, including better accounting for effects of surface pressure, improved determination of surface reflectance, and the inclusion of fine‐mode aerosol optical models to better represent anthropogenic aerosols over land. Cross‐calibration gain factors are derived for the NOAA‐20 VIIRS measurements to be consistent with the SNPP VIIRS, which allows the use of a unified algorithm package for both instruments. Comparisons against AERONET observations indicate that the Version 2 AOD data from SNPP VIIRS are significantly better than the Version 1 counterpart over land and slightly degraded over water in exchange for better spatial coverage. The AOD data from SNPP and NOAA‐20 VIIRS are comparable, indicating that cross‐calibration enables the creation of consistent aerosol data records using the series of VIIRS sensors.

Enhancing SDGSAT-1 night light images using a panchromatic guidance denoising algorithm

The Glimmer Imager on board the Sustainable Development Science Satellite 1 (SDGSAT-1) is capable of providing high-resolution nighttime light images. However, the presence of stripe noise and dark current noise in L4A images affects the authenticity of image information, thus limiting its scientific applications. This study proposes a panchromatic guidance denoising algorithm to enhance the quality of nighttime light images based on the spectral consistency properties between the panchromatic and RGB channels. This algorithm utilizes two low-noise panchromatic bands to obtain a denoised light area and subsequently removes a significant amount of noise from non-light areas in the RGB bands based on this light area. Our results indicate that this algorithm can effectively remove noise from SDGSAT-1 nighttime light images, achieving a noise removal rate greater than 99.8% in the selected dark ocean area. It also preserves more reasonable radiometric characteristics and spatial details of high-spatial resolution nighttime light images when compared to other popular denoising algorithms. The perturbation caused by moonlight can be effectively eliminated using this algorithm, which further improves the comparability and application of SDGSAT-1 nighttime light images under different lunar phases.

Quality control of instruments for measuring the characteristics of bactericidal UV radiation

Objectives. Quality control of instruments for measuring bactericidal irradiance of ultraviolet (UV) radiation is based on studying the main metrological characteristics. These characteristics include: angular and spectral sensitivity; linearity range; and absolute calibration in irradiance units. Deviations of the angular sensitivity of measuring instruments from the ideal cosine characteristic can significantly impact error estimation. They can also lead to the distortion of measurement results and a significant difference in instrument readings. The aim of this work is to enhance accuracy in resolving metrological problems of determining irradiance of bactericidal radiation.Methods. An effective method of resolving this problem is to introduce correction coefficients for the angular sensitivity of radiometers, spectroradiometers and dosimeters. The values are calculated based on the results of measurements on the goniometer when testing measuring instruments. An important role is played by computer models and digital twins of measuring instruments based on the results of studies of the metrological characteristics of radiometers by means of software. This includes modeling the measuring task.Results. The study of angular dependence of bactericidal UV radiometer sensitivity complemented by an analysis of measurement results obtained by other authors allows determining the value of the angular sensitivity correction coefficients by the deviation of the angular sensitivity of the irradiance measuring instruments of bactericidal radiation from the standard cosine dependence.Conclusions. Deviations of the angular dependence of bactericidal radiation UV radiometer sensitivity from the cosine characteristic lead to a significant underestimation of the irradiance measurements results from extended emitters. An effective solution is the use of digital angular sensitivity correction coefficients to measure the irradiance of bactericidal radiation determined during tests. When assessing the quality of radiometers, spectroradiometers and dosimeters for bactericidal radiation, incomplete control of the main metrological characteristics of the measuring instruments creates risks of serious errors in the measurement results of bactericidal irradiance.

Radiometric characteristics of some metallic ores and nonmetallic deposits: an example, Wadi Al-Allaqi, South Eastern Desert, Egypt

Hydrothermal alteration processes are connected to many mineral formations, particularly auriferous deposits. In this study, airborne gamma-ray spectrometry (GRS) data and the analysis of radioactive materials (eU, eTh, and K) are applied to search for regions with hydrothermal alteration activity. An example is presented from Wadi Al-Allaqi, South Eastern Desert, Egypt. GRS was used to analyse various radiometric data to address potential mineral deposit areas, to map regions potentially showing metallic ore mineralisation, and to point out new geological mineral resources. The Kd (potassium deviation), “F” parameter and Th-normalisation of the K and eU anomalies were calculated, and locating new exploratory targets in the study area that exhibit high F-parameter, Kd, and K/eTh values was recommended. Additionally, the research region has a few isolated enriched spots of (K). Therefore, GRS data was used to characterise and estimate potential metallic ores, nonmetallic deposits, and gold ore zones associated with the alteration zones. Results show that most of the known mineral deposits and gold occurrences in the area, according to the metallogenic map of Egypt, are located in zones with a ratio value of (0.25–0.30) (K%/(U or Th ppm)) maps which may suggest a moderate degree of alteration. Also, most mineral deposits and gold occurrences are found in intermediate altered zones, or K-enriched sites, with a Kd% of (0.2. The work represents an attempt to map hydrothermal alteration zones associated with mineral deposits in the Wadi Al-Allaqi area. Generally, natural radiation characteristics and attributes suggest criteria that can be used globally for regional mineral exploration.

RAWIW: RAW Image Watermarking robust to ISP pipeline

Invisible image watermarking is essential for image copyright protection. Compared to RGB images, RAW format images use a higher dynamic range to capture the radiometric characteristics of the camera sensor, providing greater flexibility in post-processing and retouching. RAW images are considered the original format for distribution and image production, thus requiring copyright protection. Existing watermarking methods typically target RGB images, leaving a gap for RAW images. To address this issue, we propose the first deep learning-based RAWImage Watermarking (RAWIW) framework for copyright protection. Unlike RGB image watermarking, our method achieves cross-domain copyright protection. We directly embed copyright information into RAW images, which can be later extracted from the corresponding RGB images generated by different post-processing methods. To achieve end-to-end training of the framework, we integrate a neural network that simulates the Image Signal Processing (ISP) pipeline to handle the RAW-to-RGB conversion process. To further validate the generalization of our framework to traditional ISP pipelines and its robustness to transmission distortion, we adopt a distortion network. This network simulates noises introduced during the traditional ISP pipeline and transmission. Our extensive experiments demonstrate that RAWIW successfully achieves cross-domain copyright protection for RAW images while maintaining their visual quality and robustness to ISP pipeline distortions.

Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model

Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.

Radiometric, Mechanical and Agronomic Characterization of Four Commercial Polymeric Films for Greenhouse Applications

The objective of this work was the study of four experimental plastic covers (EPCs) to assess the improvement in microclimatic conditions inside a greenhouse. This experiment was based on the comparison of four trilayer plastics: blue, grey, yellow and colorless ECPs. Radiometric and mechanical properties were studied along with the films’ behavior under semi-field conditions through their microclimate parameters. The results show that the addition of the blue pigment causes a considerable reduction in transmission (32.04%) with a reduction in the maximum temperature (+9.8 °C) compared to the other films (14% for grey, 52% for yellow and 46% for colorless). The anti-thermal additive used in the grey EPC did not achieve the desired effect, since it reduced both the photosynthetic active radiation (PAR) (62.33%) and near-infrared radiation (NIR) (62.83%) transmission equally. None of the EPCs achieved a PAR/NIR ratio greater than 1 (0.52 for blue, 0.99 for grey, 0.98 for yellow and 0.99 for colorless). Hindered amine light stabilizers (HALSs) photo-stabilizing additives block UV-A radiation (36.85 for grey) more efficiently compared to nickel quenchers (38.64 for yellow), as they allow earlier PAR transmission. The tensile test showed that all the EPCs manifested a linear relationship between stress and deformation, which defines Young’s modulus.

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality.With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM.

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically

This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropathy (IgAN) and non-IgAN. We prospectively enrolled patients with chronic kidney disease who underwent renal biopsy from May 2022 to December 2022 and performed an ultrasound and SMI the day before renal biopsy. The selected patients were randomly divided into training and testing cohorts in a 7:3 ratio. We extracted DL and radiometric features from the two-dimensional ultrasound and SMI images. A combined nomograph model was developed by combining the predictive probability of DL with clinical factors using multivariate logistic regression analysis. The proposed model’s utility was evaluated using receiver operating characteristics, calibration, and decision curve analysis. In this study, 120 patients with primary glomerular disease were included, including 84 in the training and 36 in the test cohorts. In the testing cohort, the ROC of the radiomics model was 0.816 (95% CI:0.663–0.968), and the ROC of the DL model was 0.844 (95% CI:0.717–0.971). The nomogram model combined with independent clinical risk factors (IgA and hematuria) showed strong discrimination, with an ROC of 0.884 (95% CI:0.773–0.996) in the testing cohort. Decision curve analysis verified the clinical practicability of the combined nomogram. The combined nomogram model based on SMI can accurately and noninvasively distinguish IgAN from non-IgAN and help physicians make clearer patient treatment plans.