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 automated cloud detection method based on green channel of total sky visible images

2015 ◽  
Vol 8 (5) ◽  
pp. 4581-4605 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
W. Yao ◽  
Y. Ma ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Mie Scattering ◽  
Brightness Distribution ◽  
Adaptive Threshold ◽  
Cloud Detection ◽  
Visual Effects ◽  
Blue Band ◽  
Green Channel ◽  
Visible Images ◽  
Cloud Mask

Abstract. Getting 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, green channel has been selected to replace the 2-D red-to-blue band for total sky cloud detection. The brightness distribution in a total sky image is usually non-uniform, 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, adaptive threshold, and binarization. Several experimental cases show that the GBSAT algorithm is robust for all types of test total sky images and has more complete and accurate retrievals of visual effects than those found through traditional methods.

The improved thin cloud detection using BRDF model based background reflectance from GOCI geostationary satellite imagery

2020 ◽  
Author(s):  
Jong-Min Yeom ◽  
Hye-Won Kim ◽  
Jeongho Lee ◽  
Seonyoung Park ◽  
Sangcherl Lee
Keyword(s):  
Land Surface ◽  
Ocean Color ◽  
Detection Algorithm ◽  
Land Application ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
Land Area ◽  
Spectral Bands ◽  
Thin Cloud ◽  
Cloud Mask

<p>In this study, the improved algorithm of thin cloud detection for geostationary ocean color imager (GOCI) satellite was developed to classify the thin cloud area over land area. The new cloud mask approach of GOCI satellite is required to expand its ocean dedicated application to other applications such for vegetation in land or aerosol optical properties (AOPs) in atmosphere due to its attractive shortwave wavelength bands of ocean color sensors. However, when trying to apply the advantages of the ocean color bands to the land area, only visible spectral bands of GOCI make it difficult to expand the land application the other way due to its limitation of cloud detection for relatively bright land surface. Furthermore, the geostationary of GOCI satellite has highly sensitive to geometry location of sun, meaning that high effective (Bidirectional Reflectance Distribution Function) BRDF effects make it also difficult to detect cloud mask in land surface due to its anisotropically scattered surface reflectance. In this paper, cloud mask algorithm of GOCI is proposed to consider those limitations by mainly using background surface reflectance from BRDF model. Therefore, minimum difference in reflectance between TOA and land as baseline of clear atmosphere and background surface reflectance underneath cloud were estimated from BRDF model. In conclusion, our new thin cloud detection is effectively detect the thin cloud over land surface area under limited ocean color bands of GOCI. The improved thin cloud detection algorithm of GOCI will be not only useful for following on instruments such as GOCI-II of Geo-KOMPSAT-2B and Sentinel 3 Ocean and Land Color Instrument (OLCL), but also applicable for existing geostationary satellites such as Geo-KOMPSAT-2A AMI, Himawari, and GOES-R as alternative cloud masking approach.</p>

scholarly journals Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

2012 ◽  
Vol 5 (6) ◽  
pp. 8189-8222 ◽  
Author(s):  
X. Wang ◽  
W. Li ◽  
Y. Zhu ◽  
B. Zhao
Keyword(s):  
Complex Terrain ◽  
Detection Algorithm ◽  
Difficult Problem ◽  
Probability Of Detection ◽  
Official Method ◽  
Cloud Detection ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces ◽  
Northwest Region ◽  
Cloud Mask

Abstract. The existence of various land surfaces has always been a difficult problem for researchers who study cloud detection using satellite observations, especially over bright surfaces such as snow and desert. To improve the cloud mask result over complex terrain, an unbiased daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. Based on the statistical seasonal threshold tests, the algorithm consists of six main channels centered on the wavelengths of 0.63, 0.865, 10.8, 1.595, 0.455, and 1.36 μm. The combination of the unbiased algorithm and the specific threshold tests for special surfaces has effectively improved the cloud mask results over complex terrain and decreased the false identifications of clouds. The visual images over snow and desert adopting the proposed scheme exhibit better correlations with true-color images than do the VIRR official cloud mask results. The validation with the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product shows that the probability of detection for clear-sky regions over snow of the new scheme has increased nearly five times over the official method, and the false-alarm ratio for cloudy areas over desert has reduced by half compared with the official result. With regard to comparisons between ground measurements and cloud mask results, this approach also provides acceptable correspondence with the ground observations except for some cases, which are mainly obscured by cirrus clouds.

scholarly journals A total sky cloud detection method using real clear sky background

2015 ◽  
Vol 8 (12) ◽  
pp. 13073-13098 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
Y. Ma ◽  
W. Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show, as long as the positions of the sun in the two images are the same, the cloud detection results are satisfactory. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals A total sky cloud detection method using real clear sky background

2016 ◽  
Vol 9 (2) ◽  
pp. 587-597 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals Enhanced MOPITT data coverage through cloud detection improvement

2021 ◽  
Author(s):  
Heba S. Marey ◽  
James R. Drummond ◽  
Dylan B. A. Jones ◽  
Helen Worden ◽  
Merritt N. Deeter ◽  
...  
Keyword(s):  
Lower Troposphere ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Detection Scheme ◽  
Cloud Height ◽  
Standard Product ◽  
Low Cloud ◽  
Data Coverage ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. The Measurements of Pollution in the Troposphere (MOPITT) satellite instrument has been measuring global tropospheric carbon monoxide (CO) since March 2000, providing the longest nearly continuous record of CO from space. During its long mission the data processing algorithms have been updated to improve the quality of CO retrievals and the sensitivity to the lower troposphere. Currently, MOPITT retrievals are only performed for clear-sky observations or over low clouds for ocean scenes. Compared to all observed radiances, successful retrieval rates are about 30 % and 40 % between 90° S–90° N and 60° S–60° N, respectively. Spatial seasonal variations show that while MOPITT data coverage in some places reaches 30 % in summer, this number can drop to less than 10 % in winter due to significantly increased cloud cover. Therefore, we investigate the current MOPITT cloud detection algorithm and consider approaches to increase the data coverage. The MOPITT CO total column (TC) data were modified by turning off the cloud detection scheme to allow a CO retrieval result regardless of their cloud status. Analyses of the standard CO TC product (cloud filtered) and non-standard product (non-cloud masked) were conducted for selected days. Results showed some coherent structures that were observed frequently in the non-masked CO product that were not present in the standard product and could potentially be actual CO features. A corresponding analysis of Moderate Resolution Imaging Spectroradiometer(MODIS) cloud height and cloud mask products along with MOPITT cloud flag descriptors was conducted in order to understand the cloud conditions present for these apparently physical CO features. Results show that a significant number of low cloud CO retrievals were rejected in the standard product. Those missing areas match the coherent patterns that were detected in the non-masked CO product. Many times, these structures were also seen in the Infrared Atmospheric Sounding Interferometer (IASI) CO TC product indicating actual CO plumes. Multi-angle Imaging SpectroRadiometer (MISR) data on the Terra satellite were also employed for cloud height comparison with MODIS. Comparisons of MODIS and MISR cloud height data indicate remarkable agreement which is encouraging for the possibility of incorporating MODIS cloud height in the MOPITT cloud detection scheme. Statistics of the global assessment of the potential use of MODIS cloud height shows that MOPITT data increases significantly when cloud heights less than 2 km in height are incorporated in the retrievals. However quality indices should be defined and produced to ensure sufficient retrieval quality.

scholarly journals An Automated Cirrus Cloud Detection Method for a Ground-Based Cloud Image

2012 ◽  
Vol 29 (4) ◽  
pp. 527-537 ◽  
Author(s):  
Jun Yang ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao
Keyword(s):  
Background Subtraction ◽  
Estimation Method ◽  
Basic Research ◽  
Adaptive Threshold ◽  
Cirrus Clouds ◽  
Detection Methods ◽  
Cirrus Cloud ◽  
Cloud Detection ◽  
Threshold Method ◽  
Blue Band

Abstract Cloud detection is a basic research for achieving cloud-cover state and other cloud characteristics. Because of the influence of sunlight, the brightness of sky background on the ground-based cloud image is usually nonuniform, which increases the difficulty for cirrus cloud detection, and few detection methods perform well for thin cirrus clouds. This paper presents an effective background estimation method to eliminate the influence of variable illumination conditions and proposes a background subtraction adaptive threshold method (BSAT) to detect cirrus clouds in visible images for the small field of view and mixed clear–cloud scenes. The BSAT algorithm consists of red-to-blue band operation, background subtraction, adaptive threshold selection, and binarization. The experimental results show that the BSAT algorithm is robust for all types of cirrus clouds, and the quantitative evaluation results demonstrate that the BSAT algorithm outperforms the fixed threshold (FT) and adaptive threshold (AT) methods in cirrus cloud detection.

scholarly journals Improved cloud mask algorithm for FY-3A/VIRR data over the northwest region of China

2013 ◽  
Vol 6 (3) ◽  
pp. 549-563 ◽  
Author(s):  
X. Wang ◽  
W. Li ◽  
Y. Zhu ◽  
B. Zhao
Keyword(s):  
Confidence Level ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Data Set ◽  
Four Seasons ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces ◽  
Northwest Region ◽  
Cloud Mask

Abstract. The existence of various land surfaces always leads to more difficulties in cloud detection based on satellite observations, especially over bright surfaces such as snow and deserts. To improve the cloud mask result over complex terrain, an unbiased, daytime cloud detection algorithm for the Visible and InfRared Radiometer (VIRR) on board the Chinese FengYun-3A polar-orbiting meteorological satellite is applied over the northwest region of China. The algorithm refers to the concept of the clear confidence level from Moderate Resolution Imaging Spectroradiometer (MODIS) and the unbiased structure of the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA). Six main channels of VIRR centered at the wavelengths of 0.455, 0.63, 0.865, 1.595, 1.36, and 10.8 μm are designed to estimate the degree of a pixel's cloud contamination judged by the clear confidence level. Based on the statistical data set during four months (January, April, July, and October) in 2010, seasonal thresholds are applied to improve the accuracy of the cloud detection results. Flags depicting snow and water are also generated by the specific threshold tests for special surfaces. As shown in image inspections, the cloud detection results over snow and deserts, adopting the proposed scheme, exhibit better correlations with true-color images than the VIRR official cloud mask results do. The performance of the proposed algorithm has been evaluated in detail for four seasons in 2011, using cloud mask products from MODIS and the ground-based observations. The evaluation is based on, overall, 47 scenes collocated with MODIS and 96 individual matchups between VIRR and the ground-based observations from two weather stations located in the research region. The quantitative validations suggest that the estimations of clear-sky regions have been greatly improved by the proposed algorithm, while a poor identification of the cirrus clouds occurs over deserts.

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 Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions

2021 ◽  
Vol 13 (2) ◽  
pp. 196
Author(s):  
Xiaoman Lu ◽  
Xiaoyang Zhang ◽  
Fangjun Li ◽  
Mark A. Cochrane ◽  
Pubu Ciren
Keyword(s):  
Infrared Imaging ◽  
Scattered Light ◽  
Detection Algorithm ◽  
Mie Scattering ◽  
Spectral Contrast ◽  
Aerosol Scattering ◽  
Smoke Plumes ◽  
Fire Smoke ◽  
Weather And Human Health ◽  
Aerosol Index

Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

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