CN108731817A - The different sensors infra-red radiation normalizing modeling method differentiated applied to forest fires hot spot - Google Patents
The different sensors infra-red radiation normalizing modeling method differentiated applied to forest fires hot spot Download PDFInfo
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- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- G01J5/804—Calibration using atmospheric correction
Abstract
The present invention relates to a kind of different sensors multidate infra-red radiation normalizing modeling methods differentiated applied to forest fires hot spot, including analyze MODIS, AVHRR, VIRR sensor infrared signature；Obtain sensor MODIS brightness temperatures value, AVHRR surface temperatures, the bright temperature value of VIRR；It is pre-processed；Threshold value, identification cloud body, water body are set by reflectivity, and the cloud to identifying, water pixel are rejected, and NDVI values are calculated；MODIS, AVHRR, VIRR sensor ir radiation data are standardized, setting MODIS sensors are standard transducer, AVHRR, VIRR sensor ir radiation data are subjected to normalizing, choose reference infrared image data and wait for the infrared image data of normalizing carry out curve fitting, regression analysis, determine the infra-red radiation normalized parameter of different sensors, different sensors infra-red radiation normalization creep function is established, infra-red radiation is formed and normalizes striograph.The model established through the invention can preferably eliminate radiation differentia influence, divide atmospheric radiation error equally, reduce the radiation difference from same sensor infrared band difference phase.
Description
Technical field
The present invention relates to Forest Fire Monitoring technical field more particularly to a kind of different sensings differentiated applied to forest fires hot spot
Device infra-red radiation normalizing modeling method.
Background technology
Remote sensing earth observation has the observation history of decades, since the sensor that spaceborne or airborne platform uses is different,
So the image data obtained has different geometry, radiation in time as characteristic.Existing remote sensing satellite throughout meteorology, resource,
Every application such as ocean, environment disaster reduction, has been realized in the industry development of related field, at the same time has accumulated for many years distant
Feel data, records the variation of earth's surface and air.The history resource for making full use of these valuable, for global climate research, warp
The development of Ji society and the progress of human civilization have great historic significance.High-precision radiation calibration is carried out to sensor
The basis for producing quantification Remote Sensing Products, for the data of different platform, how to realize between multi-platform remotely-sensed data across
Platform uses, and is the new direction of remotely-sensed data development in recent years.These conception of history measured data are effectively utilized, need to solve
Scaling Problem again is carried out to historical data, the data after a large amount of normalization is formed, the remotely-sensed data of sensor is made to normalize to together
On one radiation benchmark (i.e. multi-source radiation normalization), both satellite remote sensing detection information can be made in the biography of different platform in this way
It is converted between sensor, makes up the missing of some area data of same type sensor in time, this has remote sensing data application
It is of great importance.
Using same Sensor monitoring atural object and when judging to change, multidate image is more required to come from same biography
Sensor so that monitoring has more continuity, however, same sensor can not meet researchers when monitoring atural object
Requirement, so can only be monitored using different sensors.Each sensor situation is as follows：(1) the inconsistent of period is returned to.In
Every 1~2 day observation earth of resolution imaging spectrometer MODIS, observes four times and is divided into the morning and afternoon for one day.The third generation is practical
Intermediate-resolution makes weather observations satellite NOAA twice a day, is furnished with night-time observation channel.The scanning two in one day of Chinese feature cloud meteorological satellite
It is secondary.Since each sensor monitors discontinuous in time, cause monitoring effect bad；(2) variation of weather condition.At certain
Due to region, often there is the influence of cloud and shade in moment, during video imaging, leads to lacking for terrestrial object information
It loses, feature changes can not be monitored using single-sensor in real time.If when phase 1 by cloud influenced can not in use,
In this case, it may be considered that obtain cloudless image of the phase 2 from another sensor.Realize continuous continual monitoring；
(3) limitation in satellite transit service life.Every artificial earth satellite heavenwards be emitted to be stopped in orbit have it is certain
Service life will appear sensor radiation difference imaging problem using process, led to monitoring effect based on the limitation used
It is bad.
Radiation wave band can be carried out normalizing by the monitoring time in conjunction with different sensors and effect, realize different sensors
Multidate radiates normalizing.Before this, remote sensing radiation normalization using it is more be still for middle low resolution based on pixel
Relative radiometric normalization method.In recent years, as Aid of Space Remote Sensing Technology continues to develop, remote sensing image spatial resolution constantly carries
The relative radiometric normalization method of height, the high-resolution remote sensing image of different remote sensors is of increased attention.
Although there is certain methods that can obtain preferable effect, there is also many deficiencies, can further be carried therefore, it is necessary to a kind of
The method of high radiation normalization effect.
Invention content
Purpose according to the present invention provides a kind of based on the infrared of different sensors according to one embodiment of present invention
Normalizing modeling method is radiated, including：The infrared signature of MODIS, AVHRR, VIRR sensor is analyzed；By upper
Analysis is stated, the brightness temperature value of MODIS sensors, the bright temperature value of the surface temperature of AVHRR sensors, VIRR sensors are obtained；
Image data is pre-processed；By reflectivity, threshold value, identification cloud body, water body, and the cloud to identifying, water pixel are set
It is rejected, calculates NDVI values and determine vegetative coverage range；MODIS, AVHRR, VIRR sensor ir radiation data are carried out
Standardization sets MODIS sensors to standard transducer, and AVHRR, VIRR sensor ir radiation data are carried out
Normalizing, choose reference infrared image data and wait for normalizing infrared image data carry out curve fitting, regression analysis, determine red
External radiation normalized parameter establishes different sensors infra-red radiation normalization creep function, forms new infra-red radiation normalization image
Figure.
Preferably, it selects bright temperature value to replace surface temperature, bright temperature value progress normalizing can be largely reduced to the calculating of parameter,
Improve the speed of forest fires hot spot identification.
It further, need to be by MODIS, AVHRR, VIRR sensor infra-red radiation before carrying out brightness temperature and carrying out normalizing
Data unify dimension, obtain normalization data, including：
All sensors ir radiation data is subjected to parameter conversion, is calculated using the planck formula after simplification,
By 4 μm and 11 μm channels of thermal infrared of MODIS sensors, centre wavelength 3.99 × 10-6M and 11.01 × 10-6M is substituted into respectively
Planck formula obtains bright temperature value：
Surface temperature is calculated by Split window algorithms in AVHRR sensors ir radiation data, and coll is combined to propose
Improvement divide Split window algorithms, formula is as follows：
T0=T4+[1.34+0.39(T4-T5)(T4-T5)+α(1-ε)-βΔε+0.56]
α=ω3-8ω2+17ω+40
β=150 (ω/4.5 1-)
Wherein：T0For surface temperature, unit is (K), T4And T5For AVHRR 4 channels and 5 channel Detection Using Thermal Infrared Channels it is bright
Temperature value, ω are air water content, and unit is (g/cm2), ε is being averaged for the emissivity of 4 channels and 5 channel Detection Using Thermal Infrared Channels
Value, Δ ε are the difference in 4 channels and 5 channel emissivitys.It corrects to obtain VIRR by onboard process, spoke luminance non-linearity simultaneously
The bright temperature value of the effective black matrix of sensor.Calculation formula is as follows：
TBB=(TBB *-A)/B
Preferably, in the unified benchmark between establishing different sensors infra-red radiation, reference sensor itself is to forest fires
The sensitivity of hot spot, to the radiation number of ranging from 4 μm and 11 μm of the middle infrared spectrum of MODIS, AVHRR, VIRR sensor
According to, brightness temperature value is uniformly converted by Planck law, finally by NDVI combination maximin normalizing methods carry out
Temperature divided rank, rapid extraction forest fires hot information are established and are known based on the forest fires hot spot infra-red radiation between different sensors
Other model.
According to one embodiment of present invention, a kind of improvement is proposed on the basis of typical clear sky ground bright temperature model herein
MODIS, AVHRR, VIRR based on long-term sequence the bright temperature model of typical case, in the case where not considering other influences factor, it is assumed that
Air (ato) and brightness temperature (BT) are in power function relationship：
BT=b1ato3
And surface temperature (TS) is under to a certain degree, with brightness temperature B (TS) quadratic function relation is presented：
BT=b2TS2+b3TS+b4
With the raising of the increase and surface temperature of air (ato), brightness temperature value presents incremental brightness temperature (BT)
Trend obtains the two factor progress product with drag：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)
Ground elevation (h) while to brightness temperature B (TS) there is certain influence, elevation is higher, and temperature is lower, presents linear
Relationship：
BT=b5h+b6
Typical brightness temperature model can be obtained：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)+(b5h+b6)
Formula is unfolded to obtain formula：
BT (ato, TS, h)=b1ato3b2TS2+b1b3ato3TS+b1b4ato3+b5h+b6
By typical brightness temperature model, determines its parameter using SPSS multiple linear regression equations, obtain each sensor
Bright temperature model after normalization；
Meanwhile needing to establish normalization benchmark in different sensors, realize that the infra-red radiation between different sensors is returned
One changes, and using MODIS sensors as standard, establishes MODIS and AVHRR, VIRR sensor infra-red radiation normalization creep function respectively,
Assuming that there are linear relationships for the bright temperature value between three sensors, following functional relation is established：
f(x1)→B(TSx)=B (TSy)×a1+b1
f(x2)→B(TSx)=B (TSz)×a2+b2
Wherein, B (TSx) be MODIS sensors brightness temperature value, B (TSy) be AVHRR sensors brightness temperature value,
(TSz) be VIRR sensors brightness temperature value.Linear fit is carried out by least square method, finds out AVHRR, VIRR sensings
The normalized parameter a of device1, a2, b1, b2, establish the linear model between the two and MODIS sensors.By the bright temperature value of the two
It is mapped in MODIS sensor normalizing numerical value by mapping relations.In order to preferably to MODIS, AVHRR, VIRR sensor
Ir radiation data is compared and evaluates, and removes the unit limitation of data, is translated into nondimensional pure values, is convenient for
The index of commensurate or magnitude can not be compared and weight, and data are uniformly mapped on [0,1] section；It is true in parameter
Before fixed, the brightness temperature value of MODIS, AVHRR, VIRR sensor is standardized, formula is as follows：
Wherein, xkFor infrared image figure point brightness temperature value, xmaxFor maximum brightness temperature value, xminFor minimum temperature
Brightness value.By determining normalized parameter, the normalization brightness temperature model based on MODIS, AVHRR, VIRR sensor is established
For：
NBT=[0.033 × B (TSx)2-0.161×B(TSy)+338.556]/10
Wherein, NBT is the brightness temperature value after three sensor normalization, B (TSx) be AVHRR sensors brightness temperature
Angle value, B (TSy) be VIRR sensors brightness temperature value, the R of model is calculated with SPSS softwares2It is for 0.752, RMSE
2.070。
The present invention is when establishing the infra-red radiation normalizing model based on different sensors, it is determined that infra-red radiation normalizes
Parameter, and mathematical model and the methods of sampling are combined, establish the infra-red radiation normalizing of MODIS, AVHRR, VIRR sensor
Change model, has divided atmospheric radiation error equally using normalization creep function, reduced from same sensor infrared band difference phase
Radiation difference.
Description of the drawings
Fig. 1 is the spectral response functions schematic diagram of MODIS；
Fig. 2 is the spectral response functions schematic diagram of AVHRR；
Fig. 3 is the spectral response functions schematic diagram of VIRR；
Fig. 4 is the radiation of different temperatures blackbody spectrum and wavelength plot figure；
Fig. 5 is that image picture element radiates schematic diagram；
Fig. 6 carries out forest fires to normalize forest fires hot pixel threshold model using the different sensors that the present invention establishes after normalizing
Striograph after hot spot extraction.
Specific implementation mode
It, can be by air, light when extracting same terrestrial object information due to the difference of the spectral response functions of each sensor
According to the influence of, sensor itself, the present invention is based in the infra-red radiation normalizing method of different sensors, to MODIS, AVHRR,
The infrared signature of VIRR sensors is analyzed.Obtain the brightness temperature value of MODIS sensors, the ground of AVHRR sensors
Threshold value is arranged by reflectivity in table temperature, the brightness temperature value of VIRR sensors, identification cloud body, water body, and to identifying
Cloud, water pixel are rejected, and are calculated NDVI values and are determined vegetative coverage range.By the infrared spoke of MODIS, AVHRR, VIRR sensor
It penetrates data to be standardized, setting MODIS sensors are standard transducer, by AVHRR, VIRR sensor infra-red radiation
Data carry out normalizing, choose the infrared image data of reference and wait for that normalizing infrared image data carry out curve fitting, return and divide
Analysis, determines infra-red radiation normalized parameter, establishes different sensors infra-red radiation normalization creep function, form new infra-red radiation
Normalize striograph.
Infrared spectral characteristic is analyzed
It is instrument spectral that the respective spectrum channel of different sensors, which has respective spectral response functions, spectral response functions,
A kind of measurement of characteristic, in Sensor Design just it has been determined that and may with the growth of time in orbit after emitting
There is certain variation.After radiation energy enters sensor, by the light splitting of optical system, converted by the detector of different spectral coverage
For measured value, each measured value is related with the spectral response functions of sensor passage.Spectral response functions and optical system are set
In respect of pass, general related to light transmittance to detector sensitivity, the spectral response of sensor is typically expressed as：
Wherein, λ1And λ2For the upper offline of wavelength band, E (λ) is incident radiation brightness, and S (λ) is each channel of sensor
Spectral response, ESFor the measured value in each channel of sensor.The channel setting of sensor refers to that each channel is equal to spectrum undetermined
There are a response section, different sensors according to purpose of design generally there is different response sections, sensor each to lead to
The relationship of road entrance pupil energy and measured value is determined by spectral response functions.Different sensors have different band setting and light
Spectrum response, therefore need to consider their difference, i.e. spectral response letter when carrying out quantitative Application using different sensors data
Several influences to signal.The spectral response functions schematic diagram of MODIS and AVHRR and VIRR is as shown in Figure 1, Figure 2, Figure 3 shows.
From Fig. 1-3 it was determined that the spectral response of each wave band and wave band response section have certain difference, this difference
It is different to be mainly manifested in each wave band exoatmosphere solar irradiance of different sensors, atmospheric transmittance and Reflectivity for Growing Season.So
It is the factor for having to consider when using multi-source data.Meanwhile even if the identical band setting of different sensors and response characteristic phase
Together, atmospheric conditions, observation geometry also can be variant when obtaining data, cause different sensors to the observation of same atural object not
Together.So when different sensors carry out radiation normalization, it is considered as the difference, atmospheric condition, light of each sensor band setting
Compose the factors such as response difference.
The difference for comparing MODIS, AVHRR, VIRR sensor wave band and receptance function, can obtain according to images above,
Bimodal distribution is presented in the MODIS curves of spectrum, and peak wavelength is 3.95 μm, and wavelength cover is relatively narrow.AVHRR and VIRR light
Spectral curve is in Unimodal Distribution, and corresponding peak wavelength is respectively positioned on 3.95 μm.The covering of AVHRR wavelength is most wide.It is passed by 6S V2.0 radiation
The atmospheric transmittance of defeated modeling is analyzed, and in 3.55~3.95 areas μmChuan channels, the gas mainly absorbed is：Aerosol,
Carbon dioxide mix, nitrous oxide, methane, steam, these gases to total absorption of atmospheric transmittance contribution for 20%~
30%, it is affected by atmospheric effects closer to 4 μ ms smaller, therefore the degree that is affected by atmospheric effects of VIRR is maximum, MODIS influence degrees
It is minimum.Peak temperature calculating can be obtained using Wien's law, VIRR 790K, the highest in three sensors；MODIS
It is minimum in three sensors for 730K.But it is existing to will appear saturation to the response of radiation for different sensors in practical applications
As MODIS is up to 500K, the minimum 320K of AVHRR from the point of view of saturation temperature.It is specific as shown in table 1：
1 MODIS, AVHRR, VIRR wave band of table compares
Table 1 The comparison of MODIS、AVHRR、VIRR
Data acquisition
Usually when forest fires hot spot occurs, surface temperature activity in the range of thermal field can maintain a higher number
Value.In the angle of meteorological observation, the temperature higher than 1.5 meters or more is not equivalent to bright temperature.It is bright in most research
Mild temperature can be converted by formula.Conceptually, bright temperature refer to Radiation intensity of the entity in a certain wavelength with it is absolutely black
Radiation intensity of the body under Same Wavelength is equal, then the temperature of black matrix at this time is referred to as the actual object at that wavelength bright
Spend temperature.Under certain conditions, since tellurian atural object temperature is not absolute temperature, surface radiation process again by
The influence of air and radiation source.The parameter that surface temperature is related to is complex, if by calculating layer by layer, precision obtains instead
Less than guarantee.When forest fires hot spot occurs, bright temperature value is replaced with the data at field observation station, can not illustrate large stretch of region
Temperature.It is not that each pixel represents 1000m because the resolution ratio of MODIS and AVHRR sensors is 1km and 1.1km2.Due to
Only the bright temperature in region can change, and bright temperature value is selected to replace earth's surface temperature region temperature, and bright temperature value is carried out normalizing can be a large amount of
The calculating of parameter is reduced, the speed of forest fires hot spot identification is improved.
It, need to be unified by MODIS, AVHRR, VIRR sensor ir radiation data before carrying out brightness temperature and carrying out normalizing
Dimension obtains normalization data.First, all sensors ir radiation data is subjected to parameter conversion.The present invention is using simplification
Planck formula afterwards is calculated, by 4 μm and 11 μm channels of thermal infrared of MODIS sensors, centre wavelength 3.99 × 10-6m
With 11.01 × 10-6M brings planck formula into and obtains bright temperature value respectively is：
3.4 wave band of AVHRR sensors thermal infrared is passed through into Radiometric Correction to Calibration
AVHRR tools are converted into brightness temperature value.
By VIRR sensor infrared channel data by linear scaled on star and radiate it is non-linear correct, calculate black matrix temperature
Degree obtains brightness temperature value.
Analysis of Influential Factors
Brightness temperature is generally defined as the temperature corresponding to the radiation energy that remote sensing obtains on star, and bright temperature value is to weigh object
The index of temperature.To a certain extent, surface temperature and Land surface emissivity have codetermined the brightness temperature value of earth's surface.
Radiation temperature is directly proportional to the biquadratic of surface temperature, when minor change occurs for surface temperature, will cause infra-red radiation
Variation.Air has absorption and scattering process to thermal infrared radiation simultaneously, and moisture content is more, and infrared radiation absorption ability is more
By force.Topography and geomorphology can influence the distribution situation of ground temperature, and in general, elevation is bigger, and surface temperature is lower, and night, bright temperature value was higher than
Daytime bright temperature value.It is meteorological between bright temperature to there is complicated relationship form influence to infra-red radiation when there is mist so that
Satellite sensor is beyond affordability to arrive the true radiation temperature from earth's surface.
Model construction
In remote sensing monitoring principle, sensor can be received from tellurian any terrestrial object information, be because differently
In reflection process, sensor can distinguish different atural object radiation features object.Every object temperature is higher than absolutely temperature
Spending zero degree atural object can be to external diffusion infra-red radiation, and atural object can be identified in short-wave band visible light, near infrared channels, and passes
Sensor is in Forest Fire Monitoring hot spot, on the basis of receiving long-wave radiation thermal energy, by Planck law, by unit area spectrum
Radiance value B (λ, T) represents temperature T and the function of wavelength X, and formula is as follows：
Wherein λ is radiation wavelength, and unit is μm；T is kelvin rating under black matrix unit, and unit is open type temperature K；C is
The light velocity；H is Planck's constant value, is equal to 6.626*10-34J/S；K is Boltzmann constant.To all spectral wavelengths and radiation
Energy is integrated to obtain Bohr's hereby constant, it is found that the biquadratic of total radiation energy and surface temperature is proportional, formula is such as
Under：
B=σ * T4
Wherein σ is equal to 5.6697*10-3(w/m2*k4), the relationship of radiation energy and wavelength, works as radiation energy under different temperatures
When amount reaches peak, it can be moved toward shortwave direction with the raising of temperature.Based on MODIS, AVHRR, VIRR sensors from
Body difference, different sensor wave bands have different recognition reactions, red in generally use when carrying out the identification of forest fires hot spot
Wave section.As shown in figure 4, being the corresponding wavelength band of MODIS temperature.
According to Forest Fire Monitoring principle, the flame temperatures of forest fires can reach 1000K or so, and the energy peak of thermal infrared radiation
It is worth wavelength 3-5 μm of centre, forest fires temperature is higher, and the wavelength the close toward short wave ranges, such as following formula：
λmax=2897.8/T
Include mainly surface temperature, brightness temperature value, radiance, reflectivity, than radiation in forest fires hot spot IR parameters
Rate.Traditional forest fires hot spot recognizer identifies woods according to the absolute threshold of a certain or certain characteristic parameters (such as bright temperature)
Burning hot, the size of backdrop pels characteristic parameter has larger impact to the judging result of conventional method.However know in identical atural object
In not, Various Seasonal and different phases make bright temperature value change.In the definition of bright temperature temperature, surface temperature and compare spoke
The rate of penetrating has codetermined the bright temperature value of atural object, acts on its large effect of bright temperature.Followed by air is logical to sensor thermal infrared
Influence of the attenuating and topography and geomorphology, meteorologic factor in road to bright temperature.
In the threshold model algorithm for carrying out forest fires hot spot monitoring using MODIS, the channels MODIS radiation characteristic is combined
It with forest fires hot spot characteristic, sets bright temperature and background value and carries out judgement and require a great deal of time and energy, it is hot to reduce forest fires
The actual effect of point monitoring, and set by the normalized difference index NDTI of the reflection forest fires intensity of hot spots based on radiance
Threshold range has region dependence.The present invention is standardized normalization method given threshold by bright temperature value and carries out.
First, sensor is influenced in road radiation transmission process by air, cloud layer and other factors, in order to eliminate shadow
It rings, identical sensor ir radiation data need to be normalized.Establish between different sensors infra-red radiation normalizing it
Before, MODIS, AVHRR, VIRR ir radiation data need to be pre-processed.It usually changes in geometric position, pixel is big
Small and position can change, and in order to precisely react truth of the atural object on image, geometric correction is carried out to striograph.
Radiation is in transmission process, it may occur that reflect, reflect, scatter, diffuse etc., it influences maximum to be to absorb and scatter, to weaken
The energy of radiation needs to carry out atmospheric correction.Sensor scans the dimensionless number of atural object record according to no practical significance, needs
DN values are converted to the radiance value and reflectivity of Top Of Atmosphere.Radiation calibration is intended to eliminate the difference of sensor itself,
The relationship between dimensionless number and radiance value is established, it is upper in the present invention main using to MODIS, AVHRR, VIRR
Sensing data carries out onboard process.By pretreated striograph, due to cloud layer block brought to Objects recognition it is dry
The effect of disturbing need to carry out cloud detection using the reflectance formula of MODIS, AVHRR, VIRR.Based on generally in the feelings for having cloud cover
Under condition, it is difficult to detect the presence of forest fires hot spot, the present invention is using rejecting spissatus, thin cloud data, using radiating number before and after image
According to part cloud compensation is carried out, the usage degree of image is improved, true atural object image is restored.Using MODIS, AVHRR reflectivity 1,
2 band ratios find out NDVI values, and forest cover NDVI values are obtained by Arcgis software reclassifications.Establishing different sensors
When unified benchmark between infra-red radiation, reference sensor itself to the sensitivity of forest fires hot spot, to MODIS, AVHRR,
The radiation data that ranging from 4 μm and 11 μm of the middle infrared spectrum of VIRR sensors, brightness is uniformly converted by Planck law
Temperature value, finally by NDVI combinations maximin normalizing method into trip temperature divided rank, rapid extraction forest fires hot spot is believed
Breath is established based on the forest fires hot spot infra-red radiation identification model between different sensors.
In fiery point combustion process, the present invention is ground using the radiance value under simulation blackbody temperature with radiation characteristic
Study carefully, using the radiance value of forest fires hot spot pixel as target, discovery sensor is when receiving high temperature image element information, it will usually wrap
The attenuation by air is included, and contains background radiation value.Based on forest fires hot spot, it is analyzed in 4 μm of channels
Radiation effect relationship, as shown in Figure 5.
Herein the typical clear sky ground bright temperature model on the basis of, propose a kind of improved based on long-term sequence
The bright temperature model of typical case of MODIS, AVHRR, VIRR, in the case where not considering other influences factor, it is assumed that air (ato) and brightness temperature
It is in power function relationship to spend (BT)：
BT=b1ato3
And surface temperature (TS) is under to a certain degree, with brightness temperature B (TS) quadratic function relation is presented：
BT=b2TS2+b3TS+b4
With the raising of the increase and surface temperature of air (ato), brightness temperature value presents incremental brightness temperature (BT)
Trend obtains the two factor progress product with drag：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)
Meanwhile ground elevation (h) is to brightness temperature B (TS) there is certain influence, elevation is higher, and temperature is lower, presents linear
Relationship：
BT=b5h+b6
Typical brightness temperature model can be obtained：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)+(b5h+b6)
Formula is unfolded to obtain formula：
BT (ato, TS, h)=b1ato3b2TS2+b1b3ato3TS+b1b4ato3+b5h+b6
It uses 20.0 softwares of SPSS to carry out multiple linear regression analysis herein, determines infrared band between different sensors
Normalizing parameter obtains the bright temperature model after each sensor normalization.Meanwhile normalizing process in different sensors infra-red radiation
In, the benchmark for establishing normalizing is needed, realizes the infra-red radiation normalization between different sensors, the present invention is with MODIS sensors
For standard, the normalization creep function between MODIS sensors and AVHRR sensors, VIRR sensors is established respectively, it is assumed that three
There are linear relationships for bright temperature value between sensor, establish following functional relation：
f(x1)→B(TSx)=B (TSy)×a1+b1
f(x2)→B(TSx)=B (TSz)×a2+b2
Wherein, B (TSx) be MODIS sensors brightness temperature value, B (TSy) be AVHRR sensors brightness temperature value,
B(TSy) be VIRR sensors brightness temperature value.Linear fit is carried out by least square method, finds out AVHRR, VIRR sensing
The normalized parameter a of device1, a2, b1, b2, establish the linear model between the two and MODIS sensors.By the bright temperature value of the two
It is mapped in MODIS sensor normalizing numerical value by mapping relations.In order to preferably to MODIS, AVHRR, VIRR sensor
Ir radiation data is compared and evaluates, and removes the unit limitation of data, is translated into nondimensional pure values, is convenient for
The index of commensurate or magnitude can not be compared and weight.Data are uniformly mapped on [0,1] section.So
Before parameter determines, the brightness temperature value of MODIS, AVHRR, VIRR sensor is standardized, formula is as follows：
Wherein, xkFor the brightness temperature value of infrared image figure K points, xmaxFor maximum brightness temperature value, xminFor minimum brightness
Temperature value.
Parameter determines
Air can absorb radiation energy and cause to scatter, air is set as affecting parameters during infrared radiation transmissions
a；When Terrain Elevation is inconsistent, landform is higher, and temperature is lower, and it is b that setting landform, which is affecting parameters,；When surface temperature increases
When, bright temperature is significantly increased, and setting surface temperature is affecting parameters c.Hunan Province's on April 13rd, 2017 is extracted in the way of N+1
MODIS, AVHRR, VIRR bright temperature data, bright temperature value input 20.0 softwares of SPSS of three sensors are subjected to polynary line
Property regression analysis, obtains three sensor normalized parameters, as shown in table 1：
Fitting parameter after the normalization of 1 sensor of table
Table 1 Normalized fitting parameters of sensors
As a result accuracy test
In the effect of radiation normalization, use multiple linear regression equations that can eliminate radiation difference substantially herein, bright
When degree temperature has greatly changed, the normalized error of brightness temperature can be divided equally, realize the infrared spoke between different sensors
Penetrate normalization.In normalized interpretation of result, normalizing is carried out to parameter using least square method, passes through three sensors
Data are compared before brightness temperature data and normalizing, finally obtain infra-red radiation normalization coefficient.In conjunction with the same day remaining bright temperature
Data carry out the inverting of result precision with the bright temperature data after three sensor normalization, using coefficient of determination R2And mean square deviation
Error RMSE evaluates normalized model accuracy, and formula is as follows：
Wherein, y0For the ir radiation data after radiation normalization, y1For original ir radiation data, n is pixel
Number.RMSE value is smaller, indicates that the effect of fitting is better；RMSE value is bigger, fitting it is ineffective.Different satellites are sensed
Model after device normalization carries out result precision analysis it is found that after relative radiometric normalization, three infrared spokes of sensor
The result that is fitted is more consistent after penetrating normalization, and the slope of matched curve is close to 1.It is normalized in different sensors infra-red radiation
In model, establishing a kind of normalization brightness temperature model based on MODIS, AVHRR, VIRR sensor is:
NBT=[0.033] × B (TSx)2-0.161×B(TSy)+338.556]/10
Wherein, NBT is the brightness temperature value after three sensor normalization.B(TSx) be AVHRR sensors brightness temperature
Angle value, B (TSy) be VIRR sensors brightness temperature value.The R of model is calculated using 20.0 softwares of SPSS2It is 0.752,
RMSE is 2.070.The result shows that different sensors infra-red radiation normalization effect is preferable, can eliminate between different sensors
Radiate difference and error.
Forest fires hot pixel threshold after different sensors infra-red radiation normalizing determines
By build MODIS, AVHRR, VIRR sensor infra-red radiation normalizing model, eliminate three sensors between
Infra-red radiation difference divides the normalized error of brightness temperature equally, realizes the infra-red radiation normalization between different sensors.This
Invention is established red between MODIS sensors and AVHRR sensors, VIRR sensors respectively using MODIS sensors as standard
External radiation normalization creep function sentences forest fires hot spot using the infrared image figure after normalization by given threshold range
Not.Weather and geographical background of the invention based on Hunan Province, using 4 μm of bright temperature value of infrared band, between 4 μm and 11 mu m wavebands
Dual channel difference carries out forest fires hot spot identification.The determination of threshold value include exclude high temperature dot, identification high temperature dot, exclude non-forest fires hot spot,
Forest fires hot spot identifies that decision condition is as follows：
(1) non high temperature point threshold value is excluded
Returned using the infra-red radiation between MODIS sensors and AVHRR sensors, MODIS sensors and VIRR sensors
One changes model, and the bright temperature value of 4 mu m wavebands is carried out normalizing heel row unless high temperature dot.Using 10.2 softwares of Arcgis into line mask
The pixel is determined as non high temperature point, i.e. daytime, times monitored at night by extraction when the bright temperature value of 4 mu m wavebands is less than 308K
What pixel, as long as meeting the following conditions, it may be determined that be non high temperature point.Judge that formula is as follows：
T4μm< 308K
(2) high temperature dot is identified
Returned using the infra-red radiation between MODIS sensors and AVHRR sensors, MODIS sensors and VIRR sensors
One changes model, and high temperature dot is identified after the bright temperature value of 4 μm of infrared bands is carried out normalizing.It is carried out using 10.2 softwares of Arcgis
Mask extracts, and when the bright temperature value of 4 mu m wavebands is greater than or equal to 312K, meets high temperature dot requirement, and by 4 mu m wavebands and 11 μm of waves
The earth's surface ambient temperature of section carries out mathematic interpolation, when its difference is greater than or equal to 10K, judges the pixel for high temperature dot, i.e., full
Sufficient the following conditions, the pixel are judged as doubtful forest fires hot spot：
T4μm≥312K
T4μm-T11μm≥10K
(3) forest fires hot spot judges with non-forest fires hot spot
Bright temperature value after normalization is subjected to 0-1 standardizations, is mapped by maximin, obtains bright temperature range.
It is unified standard with the bright temperature value of MODIS sensors, when the bright temperature value of 4 μm of wave bands of AVHRR, VIRR sensor is less than or equal to
When 0.9, judge that the pixel is non-forest fires hot spot pixel.When 4 mu m waveband of AVHRR, VIRR sensor bright temperature value be more than 0.9 and
When less than or equal to 1, judge the pixel for forest fires hot spot pixel.Meet the following conditions, forest fires hot spot and non-forest fires can be carried out
The judgement of hot spot, decision condition are as follows：
T4μm≤0.9
0.9 < T4μm≤1
(4) judgement of non-forest fires hot spot
Solar flare identification is carried out according to MODIS, AVHRR, VIRR sensor image data 1,2 wave bands, if identified pixel
For solar flare when, then this pixel be non-forest fires hot spot.
Different sensors infra-red radiation normalizing method validation
To the infra-red radiation normalizing between MODIS sensors and AVHRR sensors, MODIS sensors and VIRR sensors
Change method is verified, and the present invention uses 1 day 13 April in 2017:30 to 15:The partly cloudy image number of multidate clear sky between 30
According to the different sensors infra-red radiation normalization creep function built using the present invention returns infra-red radiation image data progress normalizing
The different sensors normalization forest fires hot pixel threshold model established using the present invention after one carries out forest fires hot spot extraction.Such as Fig. 6 institutes
Show.
Claims (7)
1. a kind of different sensors infra-red radiation normalizing modeling method differentiated applied to forest fires, which is characterized in that including following
Step：
The infrared signature of MODIS, AVHRR, VIRR sensor is analyzed；
By above-mentioned analysis, sensor MODIS brightness temperatures value, AVHRR surface temperatures, the bright temperature value of VIRR are obtained；
Data are pre-processed；
Threshold value, identification cloud body, water body are set by reflectivity, and the cloud to identifying, water pixel are rejected, and NDVI values are calculated
Determine vegetative coverage range；
MODIS, AVHRR, VIRR sensor ir radiation data are standardized, MODIS sensors are set as marking
AVHRR, VIRR sensor ir radiation data are carried out normalizing, choose the infrared image data of reference and wait returning by quasi- sensor
One infrared image data carry out curve fitting, regression analysis, determine different sensors infra-red radiation normalized parameter, establish not
With sensor infra-red radiation normalization creep function, new infra-red radiation normalization striograph is formed.
2. according to the method described in claim 1, it is characterized in that, select bright temperature value replace surface temperature, bright temperature value is carried out
Normalizing can largely reduce the calculating of parameter, improve the recognition speed of forest fires hot spot.
3. according to the method described in claim 2, it is characterized in that, before carrying out brightness temperature normalizing, need to by MODIS,
AVHRR, VIRR sensor ir radiation data unify dimension, including：
MODIS sensor ir radiation datas are subjected to parameter conversion, are calculated using the planck formula after simplification, it will
4 μm of MODIS sensors and 11 μm of ir radiation datas, centre wavelength 3.99 × 10-6M and 11.01 × 10-6M substitutes into general respectively
Bright gram of formula obtains bright temperature value：
Surface temperature is calculated by Split window algorithms in AVHRR ir radiation datas, is split in conjunction with the improvement division that coll is proposed
Window algorithm, formula are as follows：
T0=T4+[1.34+0.39(T4-T5)(T4-T5)=α (1- ε)-β Δs ε+0.56
α=ω3-8ω2+17ω+40
β=150 (ω/4.5 1-)
Wherein：T0For surface temperature, unit is (K), T4And T5For the bright temperature value in 4 channels and 5 channel Detection Using Thermal Infrared Channels of AVHRR,
ω is air water content, and unit is (g/cm2), ε is the average value of the emissivity of 4 channels and 5 channel Detection Using Thermal Infrared Channels, Δ ε
For the difference in 4 channels and 5 channel emissivitys.It corrects to obtain VIRR sensors by onboard process, spoke luminance non-linearity simultaneously
The bright temperature value of effective black matrix.Calculation formula is as follows：
4. according to the method described in claim 3, it is characterized in that, establishing different sensors infra-red radiation normalization creep function
When, sensitivity of the sensor to forest fires hot spot itself is needed to refer to, to MODIS, AVHRR, VIRR sensor middle infrared spectrum
Ranging from 4 μm and 11 μm of radiation datas, brightness temperature value is uniformly converted by Planck law, is combined most finally by NDVI
Big Returning to one for minimum value method is established into trip temperature divided rank, rapid extraction forest fires hot information based between different sensors
Forest fires hot spot infra-red radiation identification model.
5. according to the method described in claim 4, it is characterized in that, being carried on the basis of typical clear sky ground bright temperature model herein
The bright temperature model of typical case for going out a kind of improved MODIS, AVHRR, VIRR based on long-term sequence, do not consider other influences because
Under element, it is assumed that air (ato) and brightness temperature (BT) are in power function relationship：
BT=b1ato3
And surface temperature (TS) is in lower to a certain degree and brightness temperature B (TS) quadratic function relation is presented：
BT=b2TS2+b3TS+b4
For brightness temperature (BT) with the raising of the increase and surface temperature of air (ato), incremental trend is presented in brightness temperature value,
The two factor progress product is obtained with drag：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)
Ground elevation (h) while to brightness temperature B (TS) there is certain influence, elevation is higher, and temperature is lower, and linear relationship is presented：
BT=b5h+b6
Typical brightness temperature model can be obtained：
BT (ato, TS, h)=(b1ato3)×(b2TS2+b3TS+b4)+(b5h+b6)
Formula is unfolded to obtain formula：
BT (ato, TS, h)=b1ato3b2TS2+b1b3ato3TS+b1b4ato3+b5h+b6
By typical brightness temperature model, its parameter is determined using SPSS multiple linear regression equations, obtains each sensor normalizing
Bright temperature model after change；
Meanwhile needing to establish normalization benchmark in different sensors, realize the infra-red radiation normalization between different sensors,
Using MODIS sensors as standard, MODIS and AVHRR, VIRR sensor infra-red radiation normalization creep functions, it is assumed that three are established respectively
There are linear relationships for bright temperature value between a sensor, establish following functional relation：
f(x1)→B(TSx)=B (TSy)×a1+b1
f(x2)→B(TSx)=B (TSz)×a2+b2
Wherein, B (TSx) be MODIS sensors brightness temperature value, B (TSy) be AVHRR sensors brightness temperature value, B
(TSz) be VIRR sensors brightness temperature value.Linear fit is carried out by least square method, finds out AVHRR, VIRR sensors
Normalized parameter a1, a2, b1, b2, establish the linear model between the two and MODIS sensors.The bright temperature value of the two is passed through
Mapping relations are mapped to MODIS sensor normalizing numerical value.
6. according to the method described in claim 5, it is characterized in that, in order to preferably red to MODIS, AVHRR, VIRR sensor
External radiation data are compared and evaluate, and remove the unit limitation of data, nondimensional pure values are translated into, convenient for difference
The index of unit or magnitude can be compared and weight, and data are uniformly mapped on [0,1] section；
Before parameter determination, the brightness temperature value of MODIS, AVHRR, VIRR sensor is standardized, formula is as follows：
Wherein, xkFor infrared image figure point brightness temperature value, xmaxFor maximum brightness temperature value, xminFor minimum temperature brightness value.
7. according to the method described in claim 6, it is characterized in that, through determination normalized parameter, based on MODIS, AVHRR,
The normalization brightness temperature model of VIRR sensors is：
NBT=[0.033 × B (TSx)2-0.161×B(TSy)+338.556]/10
Wherein, NBT is the brightness temperature value after three sensor normalization, B (TSx) be sensors A VHRR brightness temperature value, B
(TSy) be sensor VIRR brightness temperature value, the R of model is calculated with SPSS softwares2For 0.752, RMSE 2.070.
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