Daytime Low Stratiform Cloud Detection on AVHRR Imagery

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

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PanoNet3D: Combining Semantic and Geometric Understanding for LiDAR Point Cloud Detection

2020 International Conference on 3D Vision (3DV) ◽

10.1109/3dv50981.2020.00085 ◽

2020 ◽

Author(s):

Xia Chen ◽

Jianren Wang ◽

David Held ◽

Martial Hebert

Keyword(s):

Point Cloud ◽

Cloud Detection

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Deep Networks Under Block-Level Supervision for Pixel-Level Cloud Detection in Multi-Spectral Satellite Imagery

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324486 ◽

2020 ◽

Author(s):

Wei Chen ◽

Yansheng Li ◽

Yongjun Zhang ◽

Xiaolong Hao

Keyword(s):

Satellite Imagery ◽

Cloud Detection ◽

Deep Networks ◽

Block Level

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Cloud Detection From Remote Sensing Imagery Based on Domain Translation Network

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2021.3061569 ◽

2021 ◽

pp. 1-5

Author(s):

Jianhua Guo ◽

Jingyu Yang ◽

Huanjing Yue ◽

Yang Chen ◽

Chunping Hou ◽

...

Keyword(s):

Remote Sensing ◽

Cloud Detection ◽

Remote Sensing Imagery

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Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking

Sensors ◽

10.3390/s21113625 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3625

Author(s):

Mateusz Krzysztoń ◽

Ewa Niewiadomska-Szynkiewicz

Keyword(s):

Data Exchange ◽

Mobility Model ◽

Model Parameters ◽

Toxic Substances ◽

Cloud Detection ◽

Gas Dispersion ◽

Gas Concentration ◽

Cloud Monitoring ◽

Optimal Values ◽

Gas Cloud

Intelligent wireless networks that comprise self-organizing autonomous vehicles equipped with punctual sensors and radio modules support many hostile and harsh environment monitoring systems. This work’s contribution shows the benefits of applying such networks to estimate clouds’ boundaries created by hazardous toxic substances heavier than air when accidentally released into the atmosphere. The paper addresses issues concerning sensing networks’ design, focussing on a computing scheme for online motion trajectory calculation and data exchange. A three-stage approach that incorporates three algorithms for sensing devices’ displacement calculation in a collaborative network according to the current task, namely exploration and gas cloud detection, boundary detection and estimation, and tracking the evolving cloud, is presented. A network connectivity-maintaining virtual force mobility model is used to calculate subsequent sensor positions, and multi-hop communication is used for data exchange. The main focus is on the efficient tracking of the cloud boundary. The proposed sensing scheme is sensitive to crucial mobility model parameters. The paper presents five procedures for calculating the optimal values of these parameters. In contrast to widely used techniques, the presented approach to gas cloud monitoring does not calculate sensors’ displacements based on exact values of gas concentration and concentration gradients. The sensor readings are reduced to two values: the gas concentration below or greater than the safe value. The utility and efficiency of the presented method were justified through extensive simulations, giving encouraging results. The test cases were carried out on several scenarios with regular and irregular shapes of clouds generated using a widely used box model that describes the heavy gas dispersion in the atmospheric air. The simulation results demonstrate that using only a rough measurement indicating that the threshold concentration value was exceeded can detect and efficiently track a gas cloud boundary. This makes the sensing system less sensitive to the quality of the gas concentration measurement. Thus, it can be easily used to detect real phenomena. Significant results are recommendations on selecting procedures for computing mobility model parameters while tracking clouds with different shapes and determining optimal values of these parameters in convex and nonconvex cloud boundaries.

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Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

Remote Sensing ◽

10.3390/rs13122309 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2309

Author(s):

Jingjing Tian ◽

Yunyan Zhang ◽

Stephen A. Klein ◽

Likun Wang ◽

Rusen Öktem ◽

...

Keyword(s):

Great Plains ◽

Land Surface ◽

Detection Threshold ◽

Department Of Energy ◽

Surface Reflectance ◽

Cloud Detection ◽

Southern Great Plains ◽

Clear Sky ◽

Shallow Cumulus ◽

Stereo Cameras

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

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