Cloud Images Research Articles

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them.

Oil Palm Bunch Ripeness Classification and Plantation Verification Platform: Leveraging Deep Learning and Geospatial Analysis and Visualization

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions.

A novel sequence-to-sequence based deep learning model for satellite cloud image time series prediction

Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns, detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud image forecasting is crucial, and while deep learning models have shown promise in predicting spatio-temporal data, traditional methods face challenges with extracting long-term spatio-temporal features and high computation costs. To address these issues, we propose the Re-parameterized Sequence-to-Sequence Satellite Cloud Imagery Prediction Network (Rep-SSCIPN). Rep-SSCIPN utilizes Rep-convolution layers to reduce inference-time cost and memory consumption, enhancing efficiency by converting re-parameterized blocks into a single convolution layer during inference. The sequence normalization attention mechanism in Rep-SSCIPN highlights crucial feature sequences and establishes their inter-dependencies. We validate our novel method using a real-world satellite cloud image dataset from the meteorological satellite “Himawari.” Experimental results showcase significant improvements in prediction accuracy and reconstruction quality compared to ConvLSTM, PredRNN, FCLSTM, LMC, SimVP and SCSTque models. The efficiency gains make Rep-SSCIPN a promising advancement for satellite cloud image prediction.ARTICLE INFO.

Revealing forest structural "fingerprints": An integration of LiDAR and deep learning uncovers topographical influences on Central Amazon forests

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change.

Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach

Abstract. This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager.

구름 분류 방법에 대한 연구

This study is based on the mentorship course in CNU Science Education Institute for the Gifted. Clouds are classified into ten types proposed by Luke Howard and documented in the Cloud Atlas. In this study, we performed the cloud classification using manual approach and machine learning techniques. A total of 365 cloud images were directly taken for the mentorship course. Initially, we visually assessed cloud features in each image and manually classified cloud images into 11 types considering cloud features. The 11 types were named according to their distinguishing features. Subsequently, cloud features in each image were quantified based on the HSV color space. We applied the quantified cloud features to decision tree and k-means clustering algorithm, resulting in classification of clouds into six and three types, respectively. The disparity in the number of classifications between manual and machine learning methods is originated from the relative less features derived from the cloud images and discrepancies between human intuitive criteria and those used in machine learning classification. In the further study, by utilizing a more extensive set of cloud features and samples, well-trained machine learning techniques will allow for a more precise classification of clouds. This is expected to enable the utilization of these results in short-term weather forecasts.

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

FY-4A Measurement of Cloud-Seeding Effect and Validation of a Catalyst T&D Algorithm

Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN

Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN

The Sensitivity of Polar Mesospheric Clouds to Mesospheric Temperature and Water Vapor

Polar mesospheric cloud (PMC) data obtained from the Aeronomy of Ice in the Mesosphere (AIM)/Cloud Imaging and Particle Size (CIPS) experiment and Himawari-8/Advanced Himawari Imager (AHI) observations are analyzed for multi-year climatology and interannual variations. Linkages between PMCs, mesospheric temperature, and water vapor (H2O) are further investigated with data from the Microwave Limb Sounder (MLS). Our analysis shows that PMC onset date and occurrence rate are strongly dependent on the atmospheric environment, i.e., the underlying seasonal behavior of temperature and water vapor. Upper-mesospheric dehydration by PMCs is evident in the MLS water vapor observations. The spatial patterns of the depleted water vapor correspond to the PMC occurrence region over the Arctic and Antarctic during the days after the summer solstice. The year-to-year variabilities in PMC occurrence rates and onset dates are highly correlated with mesospheric temperature and H2O. They show quasi-quadrennial oscillation (QQO) with 4–5-year periods, particularly in the southern hemisphere (SH). The combined influence of mesospheric cooling and the mesospheric H2O increase provides favorable conditions for PMC formation. The global increase in mesospheric H2O during the last decade may explain the increased PMC occurrence in the northern hemisphere (NH). Although mesospheric temperature and H2O exhibit a strong 11-year variation, little solar cycle signatures are found in the PMC occurrence during 2007–2021.

A region feature fusion network for point cloud and image to detect 3D object

AbstractSensor fusion is very important for collaborative intelligent systems. A regional feature fusion network called ReFuNet for detecting 3D Object is proposed. It is difficult to detect distant or small objects accurately for the sparsity of LiDAR point cloud. The LiDAR point cloud and camera image information to solve the problem of point cloud sparsity is used, which can integrate image‐rich semantic information to enhance point cloud features. Also, the authors’ ReFuNet method segments the possible areas of objects by the results of 2D image detection. A cross‐attention mechanism adaptively fuses image and point cloud features within the areas. Then, the authors’ ReFuNet uses fused features to predict the 3D bounding boxes of objects. Experiments on the KITTI 3D object detection dataset showed that the authors’ proposed fusion method effectively improved the performance of 3D object detection.

A Worldwide Bibliometric Analysis of Published Literature Assessing Fear of COVID-19.

Many people experience intense fear of COVID-19. The purpose of this study was to provide a comprehensive visual overview of the published literature from 2020 to 2022 assessing fear of COVID-19. From 2020 to 2022, we employed the Scopus database to conduct a bibliometric analysis. We used the VOSviewer program to perform the author co-citation analysis, Mapchart to produce a worldwide map, and Wordart to make a word cloud image. From the 1769 records examined, 1654 (93.50%) were articles, with English being the most common language (96.31%). From 2020 to 2022, annual citations experienced significant growth (R2 = 99.91%; p = 0.0195). The Institut National de la Santé et de la Recherche Médicale (INSERM, France) and China led in terms of publication output (n = 36; n = 255). M. D. Griffiths authored the highest number of articles (n = 21). The most active journal was the International Journal of Environmental Research and Public Health (n = 146), and the most prevalent keyword was "human/s" (11.51%). This bibliometric analysis evaluates the quality of the research on fear of the pandemic and the crisis management of COVID-19, which can provide managers and researchers with crucial insights for future decision making.

A subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image

The paper presents an intelligent tunnel 3D laser image acquisition system. The system can quickly acquire the 3D point cloud and tunnel surface image of the whole tunnel section. The paper proposes the subway tunnel image stitching method based on point cloud mapping relationships and high-resolution image. Firstly, the method extracts the homonymous points from the mapped image generated from the 3D point cloud and the camera image based on the maximum information coefficient method. Secondly, the 3D point cloud positions corresponding to the homonymous points are determined. The optimization of camera parameters is achieved based on the gradient descent method. Then, the tunnel point cloud is expanded to generate a gray scale image based on spherical projection. Based on the optimized parameters, the point cloud is projected to the camera image to determine the depth value of each camera image pixel point. Finally, the pixel values of the camera image are projected based on the spherical projection to obtain the stitching result images. This method stitches individual images together to form a more complete tunnel surface image. The larger tunnel surface image helps in identifying tunnel defects.

Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning

Cloud-induced atmospheric extinction and occlusion significantly affect the effectiveness and quality of telescope observations. Real-time cloud-cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. Visual inspection is currently the primary approach for analyzing cloud distribution at ground-based astronomical sites. However, the main disadvantages of manual observation methods are human subjectivity, heavy workloads, and poor real-time performance. Therefore, a real-time automatic cloud image classification method is desperately needed. This paper presents a novel cloud identification method named the PSO+XGBoost model, which combines eXtreme Gradient Boosting (XGBoost) with particle-swarm optimization (PSO). The entire cloud image is divided into 37 sub-regions to identify the distribution of the clouds more precisely. Nineteen features, including the sky background, star density, lighting conditions, and subregion grayscale values, are extracted. The experimental results have shown that the overall classification accuracy is 96.91%, and our model can outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Structure of pellet cloud emission and relation with the local ablation rate

The experimental reproduction of the conditions of pellet injection expected in future large devices being not possible in present day machines, it is mandatory to validate as thoroughly as possible the available ablation models. Among the different points still under discussion, there is the relation between the spectroscopic measurement of the ablation clouds and the local ablation rate. This relation is investigated by coupling an emission model to the time-dependent simulation of ablation clouds with the HPI2 pellet ablation/deposition code. The simulated quantities are the time-evolution of the cloud visible spectrum (λ = 400–700 nm) and images in different wavelength domains (e.g. Hα,Hβ or the continuum centered at λ=576nm ). It is found that the cloud emission is anisotropic, this is particularly the case for H α and H β lines, and that the relation between the cloud emission and the ablation rate depends not only on the conditions of pellet injection, but also on the direction of observation. It follows that, in general, it is not possible to estimate the ablation profile from that of an emission line (Hα or Hβ) . The code predictions are compared with corresponding measurements for a welldocumented pellet injected in LHD, showing a good agreement for global values and main trends. The reasons for observed discrepancies are discussed.

Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold

In the intra-hour time scale, shielding of solar radiation by clouds is the main reason for the fluctuation of photovoltaic, so cloud parameters are important for intra-hour solar irradiance and photovoltaic power forecasting. Extracting cloud regions from cloud images provides a basis for quantifying cloud size and shape. It is difficult to detect clouds in these three cases, such as clouds in circumsolar region, clouds in haze weather and thin clouds at cloud edge. Aiming at such problems, this study firstly establishes a clear sky library based on pixel-level sun positions and haze conditions to deal with the complex changes of sky brightness. Then this study proposes a cloud detection method for ground-based images, which performs two segmentations on the cloud image. The initial segmentation process is a combination method that uses clear sky library method when the sun is visible and uses adaptive threshold method when the sun is occluded. The secondary segmentation process is based on superpixels and local threshold method, which restores some thin clouds that are easily ignored. Finally, for the influence of ghosts, this study summarizes the position and color features of ghosts, and uses different color channel information to deal with them.

Three-dimensional reconstruction of polarized ambient light separation in complex illumination

In current research, it is still a hot topic for 3D reconstruction under complex illumination. This paper uses a polarization camera combined with a coding technique to propose a new 3D reconstruction method for polarized ambient light separation. Based on the polarization camera, a specific separation model is established to analyze the relationship between the polarization characteristics of polarized and natural light. Specular reflections were filtered first and then analyzed based on the stocks vector and muller matrix. A specific calculation process was used to calculate different polarization azimuths according to the polarization characteristics, and finally, the polarized light and ambient light were separated. The experimental results show that the use of this polarization camera approach reduces the number of steps required to rotate the polarizer multiple times. This not only reduces the shooting time but also improves the efficiency. Moreover, after separating the ambient light, polarization imaging suppresses the interference of the ambient light, which helps to highlight the complete point cloud image more clearly in the 3D reconstruction. The standard deviation of 3D reconstruction was improved to 0.1675 mm by using this method in indoor and outdoor experiments.

Data Pre-processing of Website Browsing Records: To Prepare Quality Dataset for Web Page Classification

The increased usage of the internet worldwide has led to an abundance of web pages designed to supply information to internet users. The use of web page classification is becoming increasingly necessary to organize the growing number of web pages. This classification model serves as a tool to restrict internet usage to specific categories of web pages. To develop the classification model, it’s crucial to check the quality of the dataset, as it determines the performance of the web page classification model. Raw datasets are typically unreliable and subject to noise, which complicates data analysis. This is why data pre-processing is necessary to prepare the dataset properly. In this study, website browsing records serve as the dataset. The primary goal of this paper is to investigate data pre-processing techniques for website browsing records, focusing on Game and Online Video Streaming web pages. Data pre-processing involves two main steps: data cleaning and web content pre-processing. After completing the data cleaning process, the datasets are reduced from the original. This demonstrates that many datasets can be eliminated due to their inactivity or unsuitability as the datasets for Game and Online Video Streaming web pages. Meanwhile, web content pre-processing removes noise from an HTML document, retaining only relevant words that can represent the web page by creating a word cloud image. Convolutional Neural Networks (CNN) will be used to construct a model for categorizing web pages to determine whether they fall under Game or Online Video Streaming. The pre-processed data will be used as the input for this model.

Enhancing angular resolution in single-photon LIDAR for daytime long-range 3D imaging

Single photon LIDAR exhibits exceptional detection sensitivity and finds extensive applications in long-distance measurement and three-dimensional imaging domains. However, it is susceptible to background noise interference, leading to a trade-off between scanning imaging efficiency and angular resolution, thereby limiting its overall performance. In this paper, we propose a novel ‘compression-resampling’ high-resolution imaging method for single-photon scanning LIDAR based on the spatial correlation of the target scene. This approach significantly enhances the system's angular resolution without compromising imaging efficiency. By employing this method, we successfully conducted 3D imaging over distances exceeding 26 km even under challenging conditions with high levels of background noise during daylight hours. Within a target field of view measuring 1° × 1°, we reconstructed a high-resolution 3D point cloud image comprising 1000 × 1410 pixels. Notably, this achievement yielded an impressive angular resolution of 17.4 urad while maintaining an acceptable imaging time of approximately 20 minutes. The results unequivocally demonstrate the effectiveness of our proposed method in improving the quality of three-dimensional imaging and enhancing angular resolution while simultaneously reducing imaging time requirements, thus expanding potential application scenarios for single-photon LIDAR technology.

Point2Real: Bridging the Gap between Point Cloud and Realistic Image for Open-World 3D Recognition

Recognition in open-world scenarios is an important and challenging field, where Vision-Language Pre-training paradigms have greatly impacted the 2D domain. This inspires a growing interest in introducing 2D pre-trained models, such as CLIP, into the 3D domain to enhance the ability of point cloud understanding. Considering the difference between discrete 3D point clouds and real-world 2D images, reducing the domain gap is crucial. Some recent works project point clouds onto a 2D plane to enable 3D zero-shot capabilities without training. However, this simplistic approach leads to an unclear or even distorted geometric structure, limiting the potential of 2D pre-trained models in 3D. To address the domain gap, we propose Point2Real, a training-free framework based on the realistic rendering technique to automate the transformation of the 3D point cloud domain into the Vision-Language domain. Specifically, Point2Real leverages a shape recovery module that devises an iterative ball-pivoting algorithm to convert point clouds into meshes, narrowing the gap in shape at first. To simulate photo-realistic images, a set of refined textures as candidates is applied for rendering, where the CLIP confidence is utilized to select the suitable one. Moreover, to tackle the viewpoint challenge, a heuristic multi-view adapter is implemented for feature aggregation, which exploits the depth surface as an effective indicator of view-specific discriminability for recognition. We conduct experiments on ModelNet10, ModelNet40, and ScanObjectNN datasets, and the results demonstrate that Point2Real outperforms other approaches in zero-shot and few-shot tasks by a large margin.

Joint Retrieval of Multiple Species of Ice Hydrometeor Parameters from Millimeter and Submillimeter Wave Brightness Temperature Based on Convolutional Neural Networks

Submillimeter wave radiometers are promising remote sensing tools for sounding ice cloud parameters. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS−SG) is the first operational submillimeter wave radiometer used for ice cloud remote sensing. Ice clouds simultaneously contain three species of ice hydrometeors—ice, snow, and graupel—the physical distributions and submillimeter wave radiation characteristics of which differ. Therefore, jointly retrieving the mass parameters of the three ice hydrometeors from submillimeter brightness temperatures is very challenging. In this paper, we propose a multiple species of ice hydrometeor parameters retrieval algorithm based on convolutional neural networks (CNNs) that can jointly retrieve the total content and vertical profiles of ice, snow, and graupel particles from submillimeter brightness temperatures. The training dataset is generated by a numerical weather prediction (NWP) model and a submillimeter wave radiative transfer (RT) model. In this study, an end to end ICI simulation experiment involving forward modeling of the brightness temperature and retrieval of ice cloud parameters was conducted to verify the effectiveness of the proposed CNN retrieval algorithm. Compared with the classical Unet, the average relative errors of the improved RCNN–ResUnet are reduced by 11%, 25%, and 18% in GWP, IWP, and SWP retrieval, respectively. Compared with Bayesian Monte Carlo integration algorithm, the average relative error of the total content retrieved by RCNN–ResUnet is reduced by 71%. Compared with BP neural network algorithm, the average relative error of the vertical profiles retrieved by RCNN–ResUnet is reduced by 69%. In addition, this algorithm was applied to actual Advanced Technology Microwave Sounder (ATMS) 183 GHz observed brightness temperatures to retrieve graupel particle parameters with a relative error in the total content of less than 25% and a relative error in the profile of less than 35%. The results show that the proposed CNN algorithm can be applied to future space borne submillimeter wave radiometers to jointly retrieve mass parameters of ice, snow, and graupel.