scholarly journals A Cloud Detection Algorithm Over Land Based on the Polarized Characteristics Difference Between Cloudless and Cloud Targets

2019 ◽  
Vol 6 (9) ◽  
pp. 1769-1780 ◽  
Author(s):  
Xin Gao ◽  
Xiuqing Hu ◽  
Wei Fang ◽  
Dekui Yin
Keyword(s):  
Detection Algorithm ◽  
Cloud Detection

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

2021 ◽  
Author(s):  
Chao Liu ◽  
Shu Yang ◽  
Di Di ◽  
Yuanjian Yang ◽  
Chen Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Spectral Image

scholarly journals An automated cloud detection method based on the green channel of total-sky visible images

2015 ◽  
Vol 8 (11) ◽  
pp. 4671-4679 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Blue Band ◽  
Green Channel ◽  
The Past ◽  
Visible Images ◽  
Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

scholarly journals AN OPTIONAL THRESHOLD WITH SVM CLOUD DETECTION ALGORITHM AND DSP IMPLEMENTATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 771-777 ◽  
Author(s):  
Guoqing Zhou ◽  
Xiang Zhou ◽  
Tao Yue ◽  
Yilong Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Threshold Method ◽  
Dsp Implementation ◽  
Svm Algorithm

This paper presents a method which combines the traditional threshold method and SVM method, to detect the cloud of Landsat-8 images. The proposed method is implemented using DSP for real-time cloud detection. The DSP platform connects with emulator and personal computer. The threshold method is firstly utilized to obtain a coarse cloud detection result, and then the SVM classifier is used to obtain high accuracy of cloud detection. More than 200 cloudy images from Lansat-8 were experimented to test the proposed method. Comparing the proposed method with SVM method, it is demonstrated that the cloud detection accuracy of each image using the proposed algorithm is higher than those of SVM algorithm. The results of the experiment demonstrate that the implementation of the proposed method on DSP can effectively realize the real-time cloud detection accurately.

Improvement of Universal Dynamic Threshold Cloud Detection Algorithm and Its Application in High Resolution Satellite

2018 ◽  
Vol 38 (10) ◽  
pp. 1028002 ◽  
Author(s):  
王权 Wang Quan ◽  
孙林 Sun Lin ◽  
韦晶 Wei Jing ◽  
周雪莹 Zhou Xueying ◽  
陈婷婷 Chen Tingting ◽  
...  
Keyword(s):  
High Resolution ◽  
Detection Algorithm ◽  
Dynamic Threshold ◽  
Cloud Detection

scholarly journals Cloud screening in AVHRR digital data over Arctic regions

1993 ◽  
Vol 17 ◽  
pp. 386-390 ◽  
Author(s):  
Sonia C. Gallegos ◽  
Jeffrey D. Hawkins ◽  
Chiu Fu Cheng
Keyword(s):  
Single Channel ◽  
Screening Method ◽  
Detection Algorithm ◽  
Digital Data ◽  
Cloud Detection ◽  
Arctic Regions ◽  
Identification Technique ◽  
Multiple Band ◽  
Cloud Screening ◽  
Very High

A cloud screening method initially generated to mask cloud contaminated pixels over the ocean in visible/infrared imagery, has been revised and adapted to detect clouds over Arctic regions with encouraging results. Although the method is quite successful in eliminating very cold clouds, it underestimates low level clouds. However, this does not appear to interfere with monitoring of ice related features such as leads or the ice edge in Advanced Very High Resolution Radiometer (AVHRR) scenes. The method uses: a multiple-band approach to produce signatures not readily available in single channel data, an edge detection/dilation technique to locate features in the clouds and to join isolated edges, and a polygon identification technique to remove noise in the form of isolated pixels and separate clear regions from cloud contaminated areas. The method has been tested over a limited set of data with consistent results. Initial evaluation of the usefulness of this cloud-detection algorithm in data-fusion experiments indicate a potential in locating areas in AVHRR data which are cloud contaminated and which could yield a far superior representation of the ice features if replaced with data from a different sensor such as the Special Sensor Microwave/lmager (SSM/I).

scholarly journals Validation of the LaSRC Cloud Detection Algorithm for Landsat 8 Images

2019 ◽  
Vol 12 (7) ◽  
pp. 2439-2446 ◽  
Author(s):  
Sergii Skakun ◽  
Eric F. Vermote ◽  
Jean-Claude Roger ◽  
Christopher O. Justice ◽  
Jeffrey G. Masek
Keyword(s):  
Detection Algorithm ◽  
Landsat 8 ◽  
Cloud Detection

scholarly journals Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

2010 ◽  
Vol 49 (11) ◽  
pp. 2315-2333 ◽  
Author(s):  
Galina Wind ◽  
Steven Platnick ◽  
Michael D. King ◽  
Paul A. Hubanks ◽  
Michael J. Pavolonis ◽  
...  
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Single Layer ◽  
Precipitable Water ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Water Vapor Absorption ◽  
Cloud Properties ◽  
Cloud Models ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.

scholarly journals A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

2015 ◽  
Vol 8 (2) ◽  
pp. 553-566 ◽  
Author(s):  
M.-H. Ahn ◽  
D. Han ◽  
H. Y. Won ◽  
V. Morris
Keyword(s):  
Water Vapor Pressure ◽  
Microwave Radiometer ◽  
Surface Air Temperature ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Cloud Base ◽  
Cloud Detection ◽  
Clear Sky ◽  
Spectral Test ◽  
Free Data

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

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