scholarly journals DECISION TREE CLOUD DETECTION ALGORITHM BASED ON FY-4A SATELLITE DATA

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 219-226
Author(s):  
Z. F. Yu ◽  
W. H. Ai ◽  
Z. H. Tan ◽  
W. Yan
Keyword(s):  
Decision Tree ◽  
Brightness Temperature ◽  
Temperature Difference ◽  
Satellite Data ◽  
Detection Algorithm ◽  
Processing Technology ◽  
Cloud Detection ◽  
Detection Model ◽  
Meteorological Satellite

Abstract. In order to study the on-board processing technology of meteorological satellites, a decision tree cloud detection algorithm is proposed by taking FY-4A satellite data as an example. According to the channel setting of the Advanced Geosynchronous Radiation Imager (AGRI) on FY-4A satellite, the 0.65 μm, 1.375 μm, 3.75 μm, and 10.7 μm bands are selected as the cloud detection channels, and the reflectance, brightness temperature or bright temperature difference of the four channels are used as the cloud detection indicators, the thresholds of the four cloud detection indicators are obtained through statistics. On this basis, the decision tree cloud detection model is constructed and validated using FY-4A satellite data. The results show that the algorithm is simple, convenient and efficient, and the overall effect of cloud detection is good. It is an effective way for meteorological satellite cloud detection on-board processing technology.

scholarly journals A Daytime Complement to the Reverse Absorption Technique for Improved Automated Detection of Volcanic Ash

2006 ◽  
Vol 23 (11) ◽  
pp. 1422-1444 ◽  
Author(s):  
Michael J. Pavolonis ◽  
Wayne F. Feltz ◽  
Andrew K. Heidinger ◽  
Gregory M. Gallina
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Detection Algorithm ◽  
Detection Sensitivity ◽  
False Alarms ◽  
Cloud Detection ◽  
Cloud Optical Depth ◽  
Absorption Signal ◽  
Volcanic Clouds ◽  
Volcanic Cloud

Abstract An automated volcanic cloud detection algorithm that utilizes four spectral channels (0.65, 3.75, 11, and 12 μm) that are common among several satellite-based instruments is presented. The new algorithm is physically based and globally applicable and can provide quick information on the horizontal location of volcanic clouds that can be used to improve real-time ash hazard assessments. It can also provide needed input into volcanic cloud optical depth and particle size retrieval algorithms, the products of which can help improve ash dispersion forecasts. The results of this new four-channel algorithm for several scenes were compared to a threshold-based reverse absorption algorithm, where the reverse absorption algorithm is used to identify measurements with a negative 11–12-μm brightness temperature difference. The results indicate that the new four-channel algorithm is not only more sensitive to the presence of volcanic clouds but also generally less prone to false alarms than the standard reverse absorption algorithm. The greatest impact on detection sensitivity is seen in the Tropics, where water vapor can often mask the reverse absorption signal. The four-channel algorithm was able to detect volcanic clouds even when the 11–12-μm brightness temperature difference was greater than +2 K. In the higher latitudes, the greatest impact seen was the significant reduction in false alarms compared to the reverse absorption algorithm and the improved ability to detect optically thick volcanic clouds. Cloud water can also mask the reverse absorption signal. The four-channel algorithm was shown to be more sensitive to volcanic clouds that have a water (ice or liquid water) component than the reverse absorption algorithm.

Bispectral and brightness temperature difference analysis of 1991 FIRE cirrus IFO satellite data

1993 ◽  
Author(s):  
Eric O. Schmidt ◽  
Robert F. Arduini ◽  
Bruce A. Wielicki ◽  
Bryan A. Baum
Keyword(s):  
Brightness Temperature ◽  
Temperature Difference ◽  
Satellite Data ◽  
Difference Analysis

Geostationary Meteorological Satellite Data Processing System

2011 ◽  
Vol 181-182 ◽  
pp. 257-260
Author(s):  
Hong Tao Guo ◽  
Zhi Guo Chang ◽  
Shan Wei He
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Satellite Data ◽  
Processing System ◽  
Weather Forecast ◽  
Cloud Detection ◽  
Data Processing System ◽  
Dynamic Link Library ◽  
Geostationary Meteorological Satellite ◽  
Meteorological Satellite

In order to design a set of geostationary meteorological satellite data processing system，which have common data processing，practical remote sensing products and rich visual stylet，used VC++6.0 MFC and dynamic link library and the mature remote sensing products processing algorithms, to design it. Multi-satellite source geostationary meteorological satellite data are integrated, remote sensing products are generated, for example, cloud detection, cloud classification, cloud top height. The original cloud image, remote sensing and geographic information products are displayed vividly. The three-dimensional cloud image processing retrieval based on cloud detection product takes into account the visual effect, and also has a clear physical meaning, practicality is strong. The system makes geostationary meteorological satellite information play a more important role in the current weather forecast.

Visual analysis of meteorological satellite data via model-agnostic meta-learning

2021 ◽  
Author(s):  
Shiyu Cheng ◽  
Hanwei Shen ◽  
Guihua Shan ◽  
Beifang Niu ◽  
Weihua Bai
Keyword(s):  
Satellite Data ◽  
Visual Analysis ◽  
Meta Learning ◽  
Meteorological Satellite

Temporal behaviour of Asian dust aerosols observed in 2001 using meteorological satellite data

2004 ◽  
Vol 4 (3) ◽  
pp. 208 ◽  
Author(s):  
Naoko Iino ◽  
Toshiaki Yano ◽  
Toshikatsu Masumizu ◽  
Kisei Kinoshita ◽  
Itsushi Uno ◽  
...  
Keyword(s):  
Satellite Data ◽  
Asian Dust ◽  
Temporal Behaviour ◽  
Dust Aerosols ◽  
Meteorological Satellite

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.

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