CN110489505A - A kind of dynamic thresholding method is come the method that identifies low clouds dense fog - Google Patents
A kind of dynamic thresholding method is come the method that identifies low clouds dense fog Download PDFInfo
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Abstract
This application discloses a kind of dynamic thresholding methods come the method that identifies low clouds dense fog, solve the problems, such as the prior art can not a wide range of high time resolution detection low clouds dense fog.Satellite band data are extracted, the input data set of low clouds dense fog recognizer is constructed.Dynamic threshold is set.First threshold judgement is carried out to input data, identifies cloud and low clouds dense fog pixel.The application uses ordinary optical satellite data, and data acquisition is easy, and observation scope is bigger, and resolution ratio is higher.
Description
Technical field
This application involves satellite remote sensing low clouds dense fog detection fields more particularly to a kind of dynamic thresholding method to identify that low clouds are big
The method of mist.
Background technique
Mist is a kind of diastrous weather phenomenon, with the rapid development of social economy and constantly mentioning for people's living standard
The harm of height, mist is more and more prominent.Mist, especially thick fog have a great impact to visibility, it, which is seriously endangered, arrives navigation, aviation
With land communications safety.In recent years the study found that the occurrence frequency of mist, the variation of range and characteristic and cause mist to ground vapour system
The radiation balance of system has an impact.One simply example is exactly that the increase of man-made pollutant discharge reduces the effective of droplet
Particle radii increase the optical thickness of mist, therefore mist enhances the reflectivity of sunlight, may have counteracting to make to greenhouse effects
With.The main means for crossing defogging monitoring are the weather stations on ground, relatively fewer using Satellite Remote Sensing mist technological means.
But distribution and observation time of the conventional monitoring method by observation website are limited, the especially prison to large-scale mist
It surveys.And satellite data is utilized to monitor dense fog, there is wide coverage, informative, temporal resolution is high, Impersonal authenticity
By force, many advantages such as information source is reliable and cost input is low are that any routine monitoring means institute is irreplaceable.Such as " sunflower -8 "
Visible light infrared radiometer (AHI) mounted, as state-of-the-art meteorological observation sensor of new generation in the world, not only substantially
Degree improves meteorological observation ability, and the time to global observation is greatly shortened.AHI shares 16 wave bands, including can
It is light-exposed, 3 wave bands (blue, green, red)；Near-infrared, 3 wave bands；It is infrared, 10 wave bands.
Scale feature and distribution shape currently to the remote sensing monitoring common method of low clouds dense fog, such as to particle at the top of cloud and mist
Condition identifies the information of cloud and mist, can also be by the spectrum and texture and structural characteristic of mist on satellite image on the basis of, to acquisition
The texture analysis of cloud and mist pixel and separation are extracted fog-zone information with the threshold value of setting, can also by atmospheric radiation transmission theory,
With the visible light and infrared spectroscopy feature of frequency spectrum analysis method analysis cloud and mist, the index of some cloud and mist identifications and classification is provided.This
A little methods are not strong to the cloud and mist identification universality of specific region due to being limited by satellite data time, range, resolution ratio.
Summary of the invention
The present invention proposes a kind of dynamic thresholding method come the method for identifying low clouds dense fog, and solving the prior art can not be a wide range of
The problem of detection low clouds dense fog of high time resolution.
A kind of method that the embodiment of the present application provides dynamic thresholding method to identify low clouds dense fog comprising the steps of:
When SOZ≤72 °, DEM < 200m, the first dynamic threshold=0.24；When SOZ≤72 °, DEM≤200m, first
Dynamic threshold=0.20；When 72 ° of SOZ >, the first dynamic threshold=- 0.014 × SOZ+1.267；Second dynamic threshold=-
0.0078×SOZ+0.6764。
Condition 1:R2.3The second dynamic threshold of >, and DEM < 3000；
Condition 2:R0.64The first dynamic threshold of >, and BT11.2< 270；
Condition 3:BT8.6–BT7.3≤ 21.415 or NDSI≤- 0.2654.
Meet condition 1 or condition 2 is any, and the pixel for meeting condition 3 again is identified as cloud, it is other to be identified as low clouds dense fog；
The SOZ is solar zenith angle, and DEM is height above sea level, and the NDSI is snow by index, and the R is reflectivity, and BT is brightness temperature.
Further, it also comprises the steps of:
Second threshold judgement is carried out to the pixel for being identified as cloud, identifies cloud and clear sky.
DEM > 0 and NDSI > 0.36 and BT7.3–BT11.2< -5 and BT11.2–BT3.9When > -9.4, when be identified as cloud, it is other
It is identified as clear sky；Or, working as R0.86/R1.6<when 0.99 and DEM>500, it is identified as clear sky.
Further, it also comprises the steps of:
Third threshold decision is carried out to the pixel for being identified as low clouds dense fog, identifies clear sky, cloud and low clouds dense fog.
BT11.2–BT12.3≤ 1.035 and 0.186 < R0.46≤ 0.259 and DEM > 500, is identified as clear sky；Or, working as BT11.2
>=270 and BT3.9/R0.64When >=1.5, it is identified as low clouds dense fog, other are identified as cloud.
Further, the satellite band data come from fixed statellite.
Preferably, the AHI sensor that the satellite band data are carried from fixed statellite sunflower 8.
The embodiment of the present application use at least one above-mentioned technical solution can reach it is following the utility model has the advantages that
The application uses ordinary optical satellite data, and data acquisition is easy, and observation scope is bigger, and resolution ratio is higher.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present application, constitutes part of this application, this Shen
Illustrative embodiments and their description please are not constituted an undue limitation on the present application for explaining the application.In the accompanying drawings:
The method flow diagram that Fig. 1 is a kind of dynamic thresholding method to identify low clouds dense fog；
A kind of method that Fig. 2 is dynamic thresholding method to identify low clouds dense fog further identifies the journey figure of clear sky and cloud stream；
Fig. 3 further identifies the process of low clouds dense fog and cloud for a kind of dynamic thresholding method come the method for identifying low clouds dense fog
Figure；
Fig. 4 is a kind of dynamic thresholding method to identify the method scene low clouds dense fog of low clouds dense fog, the flow chart of cloud and clear sky.
Specific embodiment
To keep the purposes, technical schemes and advantages of the application clearer, below in conjunction with the application specific embodiment and
Technical scheme is clearly and completely described in corresponding attached drawing.Obviously, described embodiment is only the application one
Section Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall in the protection scope of this application.
Below in conjunction with attached drawing, the technical scheme provided by various embodiments of the present application will be described in detail.
Embodiment 1
The method flow diagram that Fig. 1 is a kind of dynamic thresholding method to identify low clouds dense fog.
A kind of dynamic thresholding method is come the method that identifies low clouds dense fog comprising the steps of:
Step 101 extracts satellite band data, constructs the input data set of low clouds dense fog recognizer.
In a step 101, satellite band data are extracted first, low clouds dense fog identification basic database are established, then to low
Cloud dense fog identification basic database data are handled, and the input data set of low clouds dense fog recognizer is constructed.
The satellite band data of low clouds dense fog identification basic database include visible light wave range, near infrared band, red
17 characteristic wave bands such as wave section and solar zenith angle SOZ further include height above sea level including reflectivity R, brightness temperature value BT
Spend DEM.
The satellite band data come from all kinds of fixed statellites, it is preferable that the satellite band data come from fixed statellite
The AHI sensor that sunflower 8 is carried.
For example, 17 characteristic wave bands specifically include: R0.47(albedo_01,0.47um), R0.51(albedo_02,
0.51um)、R0.64(albedo_03,0.64um), R0.86(albedo_04,0.86um), R1.6(albedo_05,1.6um), R2.3
(albedo_06,2.3um), BT3.9(tbb_07,3.9um), BT6.2(tbb_08,6.2um), BT6.9(tbb_09,6.9um),
BT7.3(tbb_10,7.3um), BT8.6(tbb_11,8.6um), BT9.6(tbb_12,9.6um), BT10.1(tbb_13,
10.1um)、BT11.2 (tbb_14,11.2um), BT12.4(tbb_15,12.4um), BT13.3(tbb_16,13.3um) and SOZ.
It further include height above sea level DEM.The albedo and tbb is the title of 8 satellite data of sunflower.
The pretreatment includes the processes such as projective transformation, radiation calibration, band math.
The input data set of the low clouds dense fog recognizer calculates vegetation index NDCI, snow by index NDSI, subwave
Section data difference operation result and participate in wave band reflectivity ratio calculation.
For example, dynamic thresholding method identifies that low clouds dense fog data set needs to do the following processing: calculate vegetation index NDVI and
Snow is by index NDSI；The difference calculating of wave band includes: (tbb_14,11.2um) and (tbb_07,3.9um), (tbb_10,
7.3um) and (tbb_14,11.2um), (tbb_14,11.2um) and (tbb_15,12.4um), (tbb_11,8.6um) and
(tbb_10,7.3um), (tbb_16,13.3) and (tbb_14,11.2um)；Wave band reflectivity ratio calculation includes: (albedo_
03,0.64um) and (albedo_04,0.86um), (albedo_04,0.86um) and (albedo_05,1.6um), (tbb_07,
3.9um) and (albedo_03,0.64um), (tbb_11,8.6um) and (tbb_14,11.2um)；It needs to carry out dynamic threshold to set
The wave band set includes: R0.64(albedo_03,0.64um) and R2.3(albedo_06,2.3um).
The NDVI calculation method is (R0.86-R0.64)/(R0.86+R0.64), the calculation method of NDSI is (R0.51-R1.6)/
(R0.51+R1.6).The difference of wave band calculates: BT11.2–BT3.9,BT11.2–BT12.3, BT10.8–BT3.9；Participate in wave band reflectivity ratio
It calculates: R0.86/R0.64, BT brightness temperature3.9/R0.64.The threshold value of DEM, which is divided into, is set as 150 meters, 500 meters, 800 meters and 3000 meter 4
Class.
Step 102, setting dynamic threshold.
In a step 102, needing the wave band for carrying out dynamic threshold setting includes: R0.64And R0.86, wherein R0.64After calculating
Threshold value indicates with the first dynamic threshold, R0.86Threshold value after calculating indicates with the second dynamic threshold, the calculation method are as follows: when
SOZ≤72 °, when DEM < 200m, the first dynamic threshold=0.24；When SOZ≤72 °, DEM≤200m, the first dynamic threshold=
0.20；When 72 ° of SOZ >, the first dynamic threshold=- 0.014 × SOZ+1.267；Second dynamic threshold=- 0.0078 × SOZ+
0.6764.The R is reflectivity, and SOZ is solar zenith angle.
Step 103 carries out first threshold judgement to input data, identifies cloud and low clouds dense fog pixel.
The threshold data of the first threshold includes R0.64、R2.3、BT11.2、BT8.6–BT7.3And DEM.
First discriminate includes:
R2.3The second dynamic threshold of >, and DEM < 3000, are defined as condition 1；
R0.64The first dynamic threshold of >, and BT11.2< 270 is defined as condition 2；
BT8.6–BT7.3≤ 21.415 or NDSI≤- 0.2654, is defined as condition 3.
Meet one of condition 1 and condition 2, and meet the pixel of condition 3, is identified as cloud, it is other to be identified as low clouds dense fog；Institute
Stating NDSI is snow by index, and BT is brightness temperature, and DEM is height above sea level.
Step 104, output low clouds dense fog recognition result spatial distribution map.
It can be Python to the programming language that low clouds dense fog recognition result is drawn, or Idl can also be R
The programming languages such as language or Java, Python have function call interface abundant, read satellite data and are easy, data processing
It is advantageous, it is preferred that be Python to the programming language that low clouds dense fog recognition result is drawn.
Drawing low clouds dense fog identification spatial distribution map can be generated JPG picture format, and PDF format also can be generated, may be used also
To generate the picture formats such as PNG or TIF, here without limitation.
It should be noted that the format of the satellite band data and low clouds dense fog detection data is NETCDF format, HDF
Format and HSD format.Due to most satellites binary format NETCDF, it is therefore preferred that satellite band data and low
The format of cloud dense fog detection data is NETCDF format.
Embodiment 2
A kind of method that Fig. 2 is dynamic thresholding method to identify low clouds dense fog further identifies the journey figure of clear sky and cloud stream.
Step 101 extracts satellite band data, constructs the input data set of low clouds dense fog recognizer.
Step 102, setting dynamic threshold, the threshold value after R0.64 is calculated is indicated with the first dynamic threshold, R0.86 is calculated
Threshold value afterwards is indicated with the second dynamic threshold.
Step 103 carries out first threshold judgement to input data, identifies cloud and low clouds dense fog pixel.
Step 105 carries out second threshold judgement to the pixel for being identified as cloud, identifies cloud and clear sky；
The threshold data of the second threshold includes: NDSI, BT7.3–BT11.2、BT11.2–BT3.9、R0.86/R1.6And DEM；
Second discriminate are as follows: DEM > 0 and NDSI > 0.36 and BT7.3–BT11.2< -5 and BT11.2–BT3.9> -9.4
When, when be identified as cloud, it is other to be identified as clear sky；Or, working as R0.86/R1.6<when 0.99 and DEM>500, it is identified as clear sky.
Step 104, output low clouds dense fog recognition result spatial distribution map.
Embodiment 3
Fig. 3 further identifies the process of low clouds dense fog and cloud for a kind of dynamic thresholding method come the method for identifying low clouds dense fog
Figure.
Step 101 extracts satellite band data, constructs the input data set of low clouds dense fog recognizer.
Step 102, setting dynamic threshold, the threshold value after R0.64 is calculated is indicated with the first dynamic threshold, R0.86 is calculated
Threshold value afterwards is indicated with the second dynamic threshold.
Step 103 carries out first threshold judgement to input data, identifies cloud and low clouds dense fog pixel.
Step 106 carries out third threshold decision to the pixel for being identified as low clouds dense fog, identifies cloud and low clouds dense fog.
The threshold data of the third threshold value includes: BT11.2–BT12.3、R0.46、BT11.2And BT3.9/R0.64；
The third discriminate are as follows: BT11.2–BT12.3≤ 1.035 and 0.186 < R0.46≤ 0.259 and DEM > 500, identification
For clear sky；Or, working as BT11.2>=270 and BT3.9/R0.64When >=1.5, it is identified as low clouds dense fog, other are identified as cloud.
Step 104, output low clouds dense fog recognition result spatial distribution map.
Embodiment 4
Fig. 4 is a kind of dynamic thresholding method to identify the method scene low clouds dense fog of low clouds dense fog, the flow chart of cloud and clear sky.
Step 101 extracts satellite band data, constructs the input data set of low clouds dense fog recognizer.
Step 102, setting dynamic threshold, the threshold value after R0.64 is calculated is indicated with the first dynamic threshold, R0.86 is calculated
Threshold value afterwards is indicated with the second dynamic threshold.
Step 103 carries out first threshold judgement to input data, identifies cloud and low clouds dense fog pixel.
Step 105 carries out second threshold judgement to the pixel for being identified as cloud, identifies cloud and clear sky；
Step 106 carries out third threshold decision to the pixel for being identified as low clouds dense fog, identifies cloud and low clouds dense fog.
Step 104, output low clouds dense fog recognition result spatial distribution map.
The above description is only an example of the present application, is not intended to limit this application.For those skilled in the art
For, various changes and changes are possible in this application.All any modifications made within the spirit and principles of the present application are equal
Replacement, improvement etc., should be included within the scope of the claims of this application.
Claims (5)
1. a kind of method of dynamic threshold identification low clouds dense fog, which is characterized in that comprise the steps of:
When SOZ≤72 °, DEM < 200m, the first dynamic threshold=0.24；When SOZ≤72 °, DEM≤200m, the first dynamic
Threshold value=0.20；When 72 ° of SOZ >, the first dynamic threshold=- 0.014 × SOZ+1.267；Second dynamic threshold=- 0.0078
×SOZ+0.6764；
Condition 1, R2.3The second dynamic threshold of >, and DEM < 3000；
Condition 2, R0.64The first dynamic threshold of >, and BT11.2 < 270；
Condition 3 ,-BT7.3≤21.415 BT8.6 or NDSI≤- 0.2654；
Meet condition 1 or condition 2 is any, and the pixel for meeting condition 3 again is identified as cloud, other pixels are identified as low clouds dense fog；
The SOZ is solar zenith angle, and DEM is height above sea level, and the NDSI is snow by index, and the R is reflectivity, and BT is brightness temperature.
2. a kind of method of dynamic threshold identification low clouds dense fog according to claim 1, which is characterized in that also include following step
It is rapid:
The pixel for being identified as cloud is judged:
DEM > 0 and NDSI > 0.36 and BT7.3–BT11.2< -5 and BT11.2–BT3.9When > -9.4, when be identified as cloud, other identifications
For clear sky；Or, working as R0.86/R1.6<when 0.99 and DEM>500, it is identified as clear sky.
3. a kind of method of dynamic threshold identification low clouds dense fog according to claim 1, which is characterized in that also include following step
It is rapid:
The pixel for being identified as low clouds dense fog is judged；
BT11.2–BT12.3≤ 1.035 and 0.186 R0.46≤0.259 < and DEM > 500, are identified as clear sky；Or, working as BT11.2≥
270 and BT3.9/R0.64When >=1.5, it is identified as low clouds dense fog, other are identified as cloud.
4. a kind of method of dynamic threshold identification low clouds dense fog according to claim 1, which is characterized in that the satellite band
Data come from fixed statellite.
5. a kind of method of dynamic threshold identification low clouds dense fog according to claim 4, which is characterized in that the satellite band
The AHI sensor that data are carried from fixed statellite sunflower 8.
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| KR102223991B1 (en) * | 2020-06-29 | 2021-03-08 | 세종대학교산학협력단 | Apparatus for detecting sea fog based on satellite observation in visible and near-infrared bands and method thereof |
| CN113392818A (en) * | 2021-08-17 | 2021-09-14 | 江苏省气象服务中心 | Expressway severe weather identification method based on multi-scale fusion network |
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| CN104502999A (en) * | 2014-12-10 | 2015-04-08 | 中国科学院遥感与数字地球研究所 | Day and night cloud detection method and device based on static satellite multichannel data |
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| US3534367A (en) * | 1968-01-30 | 1970-10-13 | Nasa | Traffic control system and method |
| CN102636779A (en) * | 2012-05-07 | 2012-08-15 | 武汉大学 | Extraction method for coverage rate of sub-pixel accumulated snow based on resampling regression analysis |
| CN104502999A (en) * | 2014-12-10 | 2015-04-08 | 中国科学院遥感与数字地球研究所 | Day and night cloud detection method and device based on static satellite multichannel data |
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