CN109035663A - The different sensors multidate infra-red radiation normalizing method differentiated applied to forest fires hot spot - Google Patents
The different sensors multidate infra-red radiation normalizing method differentiated applied to forest fires hot spot Download PDFInfo
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Abstract
The present invention relates to a kind of different sensors multidate infra-red radiation normalizing method differentiated applied to forest fires, method influences pixel comprising steps of rejecting cloud body；NDVI value is calculated, tree and grass coverage is extracted；Different sensors infrared data is subjected to radiation normalization；Different sensors infra-red radiation normalized parameter is obtained, normalizing is carried out to different sensors infrared data, obtains normalization multidate infrared image；The infrared image data of different sensors reference, by curve matching, regression analysis, are established be based on different sensors multidate infra-red radiation normalization creep function respectively with to normalizing infrared image data；Precision analysis is carried out to different sensors infra-red radiation normalization creep function；The precision analysis uses coefficient of determination R2Multidate infra-red radiation normalization creep function precision is evaluated with mean square deviation error RMSE.Method for normalizing through the invention, can solve the radiation difference problem of sensor infrared band difference phase, and establish radiation standard.
Description
Technical field
The present invention relates to Forest Fire Monitoring technical field more particularly to a kind of different sensors differentiated applied to forest fires hot spot
Multidate infra-red radiation normalizing method.
Background technique
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 fields, has accumulated the remote sensing of many years at the same time
Data record the variation of earth's surface and atmosphere.The history resource for making full use of these valuable, for global climate research, economy
The development of society and the progress of human civilization have great historic significance.It is raw for carrying out high-precision radiation calibration to sensor
How the basis for producing quantification Remote Sensing Products is realized cross-platform between multi-platform remotely-sensed data by the data of different platform
It uses, is the new direction of remotely-sensed data development in recent years.These conception of history measured data are effectively utilized, need to solve to history
Data carry out Scaling Problem again, and the data after forming a large amount of normalization make the remotely-sensed data of sensor normalize to the same spoke
(i.e. multi-source radiation normalization) is penetrated on benchmark, both can make satellite remote sensing detection information between the sensor of different platform in this way
It is converted, makes up the missing of some area data of same type sensor in time, this has great meaning to remote sensing data application
Justice.
Using same sensor monitoring atural object and when judging variation, more require multidate image from same sensing
Device, so that monitoring has more continuity, however, same sensor is not able to satisfy wanting for researchers when monitoring atural object
It asks, so can only be monitored using different sensors.Each sensor situation is as follows: (1) returning to the inconsistent of period.Intermediate-resolution
Every 1~2 day observation earth of imaging spectrometer MODIS is observed four times and is divided into the morning and afternoon for one day.The practical intermediate-resolution of the third generation
Satellite NOAA make weather observations twice a day, is furnished with night-time observation channel.Scanning in Chinese feature cloud meteorological satellite one day is twice.Due to each
Sensor monitors discontinuous in time, causes monitoring effect bad；(2) variation of weather condition.At a certain moment due to ground
Often there is the influence of cloud and shade in domain reason, during video imaging, leads to the missing of terrestrial object information, using single biography
Sensor can not real-time monitoring feature changes.If when phase 1 is influenced not being available by cloud, in such a case, it is possible to
Consider to obtain cloudless image of the phase 2 from another sensor.Realize continuous continual monitoring；(3) satellite transit service life
Limitation.Every artificial earth satellite heavenwards, which are emitted to stop working in orbit, certain service life, based on use
Limitation, will appear sensor radiation difference imaging problem in use process, cause monitoring effect bad.
In conjunction with the monitoring time and effect of different sensors, radiation data can be carried out to normalizing, 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 is constantly mentioned
The relative radiometric normalization method of height, the high-resolution remote sensing image of different remote sensors is receive more and more attention.
Although there is certain methods that can obtain preferable effect, there is also many deficiencies, and therefore, it is necessary to one kind can be further improved
The method of radiation normalization effect.
Summary of the invention
Purpose according to the present invention provides a kind of infrared spoke of multidate of different sensors differentiated applied to forest fires hot spot
Normalizing method is penetrated, this method comprises the following steps:
Rejecting cloud body influences pixel；
NDVI value is calculated, tree and grass coverage is extracted；
Different sensors infrared data is subjected to radiation normalization；
Wherein different sensors include MODIS, AVHRR and VIRR sensor；
Wherein, described that different sensors infrared data is subjected to radiation normalization including determining different sensors infra-red radiation
Normalized parameter；
After obtaining different sensors infra-red radiation normalized parameter, normalizing is carried out to different sensors ir radiation data
Change, obtains new infra-red radiation normalization striograph；
By the infrared image data that refer to different sensors with to normalizing infrared image data by curve matching into
Row regression analysis establishes be based on different sensors multidate infra-red radiation normalization creep function respectively；
Precision analysis is carried out to the multidate infra-red radiation normalization creep function of different sensors；
The precision analysis uses coefficient of determination R2The multidate that different sensors are evaluated with mean square deviation error RMSE is red
External radiation normalization creep function precision.
Preferably, MODIS sensor carries out normalizing using brightness temperature value, can reflect atural object temperature conditions；
When carrying out temperature transition by planck formula, atmosphere has the function of that absorbing sieve subtracts to infra-red radiation, simultaneously
The height of shape has an impact infra-red radiation；
Analyze determining parameter by SPSS, parameter a is the atmosphere factor, and parameter b is landform, MODIS sensor it is more
Phase normalization creep function are as follows:
Y=ax+b
Wherein y is the value after normalization, and x is the value before normalization.
Work as a=-0.1393, when b=343.17, the multidate infra-red radiation normalization creep function of MODIS sensor are as follows:
Y=-0.1393x+343.17
Preferably, the factor for influencing AVHRR sensor surface temperature is mainly temperature and humidity；It is analyzed by SPSS
Determine parameter, parameter a is temperature, and parameter b is humidity, and parameter c is intensity of illumination, the multidate infra-red radiation of AVHRR sensor
Normalization creep function are as follows:
Y=ax2-bx+c
Wherein y is the value after normalization, and x is the value before normalization.
Work as a=0.0049, when b=3.0694, c=778.67, AVHRR sensor infra-red radiation matched curve more adjunction
Closely, the multidate infra-red radiation normalization creep function of AVHRR sensor are as follows:
Y=0.0049x2-3.0694x+778.67
Further, the factor for influencing VIRR sensor intermediate infrared radiation wave band is mainly solar flare, it is thus determined that its
For the parameter of VIRR sensor infra-red radiation normalizing；In most of time, the cloud layer that wind and cloud meteorological satellite will receive large area covers
Lid, due to cloud layer reflectivity with higher, can influence VIRR sensor multidate infra-red radiation normalizing precision；According to infrared spoke
Specificity analysis is penetrated, carries out analyzing determining parameter a being solar flare reflectivity by SPSS, b is cloud layer reflectivity；VIRR sensor
Multidate infra-red radiation normalization creep function are as follows:
Y=ax+b
Wherein y is the value after normalization, and x is the value before normalization.
Work as a=0.9231, when b=22.525, the normalized best fit effect of VIRR sensor infra-red radiation can be reached；
The multidate infra-red radiation normalization creep function of VIRR sensor are as follows:
Y=0.9231x+22.525
The present invention is when establishing multidate infra-red radiation normalization creep function, it is determined that infra-red radiation normalized parameter, and
Combined mathematical module and the methods of sampling, establish MODIS, AVHRR, VIRR sensor multidate infra-red radiation normalization creep function,
Divide atmospheric radiation error equally using normalization creep function, reduces the radiation difference from same sensor infrared band difference phase.
Detailed description of the invention
Fig. 1 is that figure is rejected in cloud detection；
Fig. 2 is that MODIS infra-red radiation normalizes flow chart；
Fig. 3 is that VIRR infra-red radiation normalizes flow chart；
Fig. 4 is that MODIS infra-red radiation normalizes fitting result；
Fig. 5 is that AVHRR infra-red radiation normalizes fitting result；
Fig. 6 is that VIRR infra-red radiation normalizes fitting result；
Fig. 7 is that MODIS sensor multidate infra-red radiation normalizes the identification of forest fires hot spot；
Fig. 8 is that AVHRR sensor multidate infra-red radiation normalizes the identification of forest fires hot spot；
Fig. 9 is that VIRR sensor multidate infra-red radiation normalizes the identification of forest fires hot spot.
Specific embodiment
When using remote sensing Forest Fire Monitoring hot spot, NOAA sensor has high spatial resolution and wide coverage,
When receiving forest fires hot spot energy, channel 3 has sensitive characteristic, can quick Forest Fire Monitoring hot spot, but when saturation temperature is lower,
3 channels are easily saturated the generation for causing false fire point, are difficult to be judged again on the time.And MODIS space division when higher
Resolution and spectral resolution can make up for it the defect of AVHRR sensor itself, and combine VIRR weather satellite data is unified to establish
Forest fires hot spot judges benchmark, can be improved the accuracy of identification of forest fires hot spot.It is main to divide therefore, it is necessary to construct radiation normalizing model
For two parts: the infra-red radiation normalizing of multidate infra-red radiation normalizing and different sensors.It is normalized in multidate infra-red radiation
On, establish MODIS respectively, tri- sensor multidate infra-red radiation models of AVHRR, VIRR, and to three sensor multidates
The determination and result accuracy test of infra-red radiation normalization creep function progress parameter.Return in the multidate infra-red radiation of different sensors
In one change, infra-red radiation unified parameters are determined, normalizing is carried out to infra-red radiation by maximin, constructs infra-red radiation normalizing
Change model, analysis and proof-tested in model precision are carried out to result.The multidate infra-red radiation normalizing of present invention research different sensors.
The multidate infra-red radiation normalizing of different sensors
Radiation normalization purpose is to eliminate the influence of cloud layer, water body, illumination to radiation, corrects sensor bias, improve
Radiation normalization precision.In radiation normalization method, rejecting cloud body first influences pixel, calculates NDVI value and extracts vegetation face
Product.By MODIS, AVHRR and VIRR infrared data radiation calibration, MODIS sensor thermal infrared radiation data are obtained, are converted
For brightness temperature value.Obtain AVHRR surface temperature data, VIRR brightness temperature data.Threshold value is set by reflectivity, identifies cloud
Body, water body, and the cloud, the water pixel that identify are rejected.By reclassification by terrain classification at land, forest cover, water
Body.The infrared image data for choosing reference carry out curve fitting with to normalizing infrared image data, obtain infra-red radiation normalization
Parameter determines infra-red radiation normalization creep function, forms new infra-red radiation normalization striograph.
Yun Shui is rejected
It, need to be under the premise of rejecting cloud water interference pixel before carrying out the normalization of MODIS sensor multidate infra-red radiation
Cloud is detected, cloud is carried out in the reflectivity of visible light wave range and the bright temperature of Thermal infrared bands according to the image of each sensor
It distinguishes, will have cloud pixel and substitute cloud sector pixel gray value using the relative change rate of the image of close phase, maintain image
Continuity.The multispectral cloud detection algorithm in MODIS cloud detection selects visible channel 1 (0.66 μm), near infrared channels 6
(1.64 μm) and channel 26 (1.38 μm) data are calculated, because the reflectivity of near infrared light is lower than visible reflectance, and cloud
There is high reflectance in visible light wave range, cloud body is detected according to the method for vegetation index.Formula is as follows:
Wherein, CH (n) is the reflectance value in n-channel, and cloud detection differentiates as follows:
CH(26)>T1
T2<Value<T3And CH (1) > T4
According to the setting of threshold range, the reflectivity on vegetation and land is less than or equal to zero, less than the reflectivity of visible light, when
T2When=0, covering pixel is vegetation land.On the reflectivity in channel 6, since the reflection of cloud layer all is from solar radiation,
Snow directly absorbs solar radiation, so that the emissivity of snow, well below cloud layer, Value value is greater than 40 percent, so setting
T3=0.4.On the reflectivity in channel 26, since cirrus is similar in spectral response with snow, in order to exclude missing inspection as a result, cirrus
Reflectivity be above other atural objects, 10 are all larger than according to statistics, so T1=0.1.It is difficult to point according to water body and cloud
Distinguish that the reflectivity of degree and water body is low, respectively less than 20 percent, so by T4=0.2.
Multichannel dynamic threshold cloud detection algorithm is then used in AVHRR cloud detection, this method is by a certain channel pixel battle array
Histogram curve in, take the maximum extreme point of Surface Peak cloud layer part side histogram curve second differnce, histogram is bent
The position of line maximum variability carries out the threshold determination of clear sky and cloud pixel.In 5 channels of AVHRR sensor, by 1,2 channels
It carries out ratio calculation and 3,4,5 channel difference values calculates, result data is carried out the selection of histogram and threshold value, judge to be covered by cloud
Pixel.Data processing is observed in multidate AVHRR, in order to keep the continuous use of data, according to satellite zenith angle and is led to
The dynamic threshold in road is weighted processing, accurate cloud detection effect.Meanwhile there is number of edges in the discontinuity based on pel data
According to discontinuity, need to be smoothed dynamic threshold.
In VIRR cloud detection, cloud identification is carried out using multiple spectrum thresholds method, utilizes cloud and other clutter reflections rates and spoke
The difference for penetrating brightness value, the given threshold on visible light and infrared channel.11 μm of Infrared window, low temperature is set, cloud is carried out
Detection.By many experiments and statistical analysis, the bright temperature value of image picture element can be identified as cloud layer lower than T < 267K.Work as T >
When 273K, it is determined as clear sky.Due to the variation of the reflectivity of earth's surface on daytime, so that the difficulty of infrared band identification cloud increases
Greatly, in conjunction with visible reflectance given threshold, cloud detection algorithm is as follows:
Wherein, R1For visible channel reflectivity, R2For near infrared channels reflectivity.RI is threshold range.
R3>RIthAnd RIth_Min<RI<RIth_MaxAnd T4<T4th
Wherein, T4For Detection Using Thermal Infrared Channel (10.3-11.3 μm) equivalent blackbody radiation brightness.RIth、T4thRespectively R1And T4's
Threshold value, RIth_Min、RIth_MaxThe bound threshold value of respectively RI.By data statistics, meet RIth< 267K, T4th=237K,
RIthmax=1.1, RIth_minThe conditions such as=0.95 can identify cloud.Cloud detection is rejected as shown in Figure 1.
In addition, difference occur in the high sensitivity and Various Seasonal threshold range due to cloud in infrared band, cloud layer is carried out
It rejects and cloud reparation, the method for substituting cloud layer using the relative change rate of multidate come inverting, algorithm is as follows:
The image that cloud is arranged is X, and substitute image is Y,
It enables
M, n are the number of image picture element and pixel of substituting, then cloudless, there is cloud image picture element value are as follows:
Wherein, xi, yi, xmax, xmin, ymax, yminRespectively image picture element and substitute image picture element value, maximum value, minimum
Value.When the overlapping of two width images is there is no varying widely, reference the method can repair the image in cloud layer region.Instead
It, can make pixel value that larger change occur using this algorithm.
Data acquisition
According to Wien's law, radiation peak wavelength λmaxIt is inversely proportional with blackbody temperature T.Temperature is higher, and wavelength is more toward shortwave side
To movement.According to the infrared band characteristic of MODIS sensor, AVHRR sensor and VIRR sensor, MODIS sensor is selected
Brightness temperature value and AVHRR sensor surface temperature and VIRR sensor bright temperature value.
Before obtaining MODIS sensor multidate infra-red radiation normalization data, need to carry out forest cover extraction.According to
For Remote Sensing Principles it is found that in the band setting of medium resolution satellite sensor, infrared band can not differentiate type of ground objects, reflectivity
Vegetative coverage situation can be reacted in visible light wave range.Forest cover is greater than in the reflectivity of visible light in near-infrared, with the two
The normalizing equation of wave band constitutes as follows:
Wherein, b1For the reflectivity of wave band 1, b2For the reflectivity of wave band 2.When normalized value is greater than zero, place pixel quilt
It is identified as vegetative coverage pixel.On the vegetation data acquisition of MODIS sensor, vegetative coverage is probably determined by visual method
Pixel, when NDVI be greater than a certain range when, corresponding pixel is confirmed to be forest cover pixel.
It is normalized, is manually calibrated DN value for radiance, after simplification based on MODIS sensor multidate infra-red radiation
Planck law calculate 21 wave bands, the bright temperature value of 31 wave bands.Brightness temperature value is subjected to radiation normalization processing.
On the AVHRR sensor radiation method for normalizing, Detection Using Thermal Infrared Channel 4,5 wave bands are carried out using division Split window algorithms
Bright temperature linear fit obtains surface temperature, is carried out the multidate normalization of AVHRR sensor.Formula is as follows:
T0=a+bT4+cT5
Wherein: a, b, c are constant, depend primarily on the content and Land surface emissivity of atmosphere moisture.It is mentioned in conjunction with coll
Improved division Split window algorithms out, 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 the bright temperature in 4 channel AVHRR and 5 channel Detection Using Thermal Infrared Channels
Value, ω are atmosphere 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 is the difference in 4 channels and 5 channel emissivitys.
When obtaining VIRR sensor multidate infra-red radiation normalization data, the present invention carries out multidate using bright temperature value
Image normalizing.Onboard process is first carried out using VIRR sensing data, after calculating, with the radiance value of linear scaled, into
Row spoke luminance non-linearity corrects last calculate and assumes blackbody temperature, finally calculates and assumes that blackbody temperature, calculation formula are as follows:
Analysis of Influential Factors
The infra-red radiation brightness value that sensor receives is Lλ, mainly include three parts during infrared radiation transmissions:
The upward radiance value L of atmosphere ↑, downward terrestrial radiantion, brightness value be L ↓, the true radiance value on ground arrives after atmosphere
Up to the energy of satellite sensor, radiation transfer equation are as follows:
Lλ=[ε * B (Ts)+(1-ε)L↓]*τ+L↑
Wherein, ε is emissivity, TSFor earth's surface true temperature, B (Ts) is the black matrix that calculates of Planck law in Ts
Thermal radiance, τ is atmospheric transmittance.Radiance value B (Ts) of the black matrix of temperature T in infrared band are as follows:
B(TS)=[Lλ-L↑-τ(1-ε)*L↓]/τ*ε
In thermal infrared transmission equation, it is seen that atmosphere influenced in Detection Using Thermal Infrared Channel it is very big, main absorption including atmosphere and
Scattering.Carbon dioxide, ozone, steam, carbon monoxide, suspended matter in atmosphere play the role of absorption to atmosphere.Wherein steam pair
The absorbability of infra-red radiation is most obvious, and absorption band of water vapor occupies wider wave band, is concentrated mainly on infrared band region.Atmosphere
In steam with season, the time, the variation of region and it is inconsistent, and change obvious, float larger.Moisture content is higher, red
External radiation wave band is bigger to the absorption of steam.Secondly, carbon dioxide gas has certain influence to infra-red radiation, although absorbing
Solar radiation energy is few, but can absorb terrestrial surface radiation energy and distribute long-wave radiation around, interferes to terrestrial information is obtained.
Solar radiation is the main source of surface energy, has selection and absorption to infra-red radiation when radiating across atmosphere, therefore
Atmosphere can absorb a part of radiation energy and be converted into internal energy.Simultaneously because the influence of atmospheric molecule and aerosol, by energy
It is converted into thermal energy and ionization energy.Cloud layer also has strong absorbability in infra red radiation band, true in sensor detection atural object
When real temperature, due to there is the covering of cloud layer, can only reflect the temperature at the top of cloud layer, atmospheric action in cloud layer variation and enhance and
Weaken.There is scattering for effect between infra red radiation band and dust, haze, steam, aerosol in atmosphere, when passing through atmosphere again
There are refractions.The effect of different sensors different wave length propagation in atmosphere is different, band setting it is inconsistent, imaging time is not
Together, atmospheric conditions is unstable, and the radiation of same atural object is caused to have differences.
Model construction
According to the atural object of the same race of same sensor have similar gray value, and the atmosphere between same sensor image and
Linear relationship is presented in the difference of sensor, i.e., the gray value of identical wave band is had linear relationship, carried out using linear equation with one unknown
Radiation normalization:
yn=anxn+bn
Wherein, ynFor the gray value of n wave band after experiment image normalization, anAnd bnFor in the slope of n wave band regression equation
And intercept, xnTo test image in the gray value of n wave band.A is obtained by least square methodnAnd bn, calculate normalized image
Figure.
Based on MODIS sensor infrared band characteristic, the present invention carries out infra-red radiation normalizing by the way of linear regression
Change, threshold value is arranged by reflectivity first, identify cloud body, water body, and reject to the cloud, the water pixel that identify, chooses ginseng
The infrared image data examined choose the infrared data of overlapping region, and according to maximin with to normalizing infrared image data
It is layered, therefrom carries out random sampling, sample size meets 60 or more the percent of total quantity.Finally, by after sampling
The infrared data of two width images, determines normalizing equation by least square method.As shown in Figure 2.
It is as follows that MODIS multidate infra-red radiation normalizes process:
(1) normalizing is carried out using 1,2 reflectance values of visible light wave range and near infrared band, given threshold carries out Yun Shuijian
It surveys, rejects cloud water pixel.
(2) it chooses the ir radiation data of reference and carries out scatterplot recurrence to normalizing ir radiation data, choose and meet hundred
/ six ten or more ir radiation data, rejecting influences pixel.
(3) using unchanged pixel as target, the normalizing equation of infrared band is determined using least square method.
(4) regressing calculation is carried out to new infrared image figure with regression equation, the infrared image figure after being normalized.
Based on AVHRR sensor multidate infra-red radiation normalizing, cloud is rejected using reflectivity wave band given threshold first
Layer part pixel and water body pixel choose whole vegetative coverage pixels according to NDVI.By AVHRR infrared image data by splitting window
Algorithm obtains surface temperature.According between multidate image, there are stronger linear relationships, and the present invention is using canonical correlation point
Normalizing model is established in analysis.Canonical correlation analysis is put forward earliest by Hotelling, basic thought and the unusual phase of principal component
Seemingly.Two width striographs have n channel image x=[x1……xn] and y=[y1……yn], two groups of linear combinations are formed,
That is:
a1x1+a2x2+...+anxn=aTX=U
b1y1+b2y2+...+bnyn=bTY=V
Wherein, t1And t2The image of time is represented by x=[x1,x2,x3...xn]T, y=[y1,y2,y3...yn]T, a=
[a1,a2,a3...an]T, b=[b1,b2,b3...bn]T, obtain the related coefficient between canonical variable are as follows:
Assuming that meeting following condition when two group image data dependences are high:
Var (u, v)=aT∑xyA=bT∑xyb
It obtains:
ρ=aT∑xyB=max
In order to solve extreme-value problem, Lagrange's multiplier is introduced, according to calculating image x, y canonical variable difference MAD and variance
It obtains:
MADi=Ui-ViI=1,2,3 ... n,
Var=MADi=var (ui,vi(the 1- ρ of)=2i)
Canonical variable data screening formula is as follows:
Wherein, t is threshold value.It assumes immediately, the variable difference of the MAD after normalizing and the value of variance sum are all satisfied card side point
Cloth.
It is as follows that AVHRR surface temperature value normalizes process:
(1) normalizing is carried out using 1,2 reflectance values of visible light wave range and near infrared band, given threshold carries out Yun Shuijian
It surveys, rejects cloud water pixel, obtain vegetation pixel.
(2) 60 percent reference infrared image data are chosen and the infrared image data to normalizing carry out scatterplot and return
Return, calculates the canonical variable of two width images.
(3) sample reconnaissance is carried out according to threshold value t, ρ value is set.
(4) sample point is chosen according to threshold value, regressing calculation is carried out to new infrared image figure with least square method, is returned
Infra-red radiation striograph after one change.
Infrared band characteristic based on VIRR sensor, the present invention carry out the infrared spoke of multidate by the way of linear regression
Normalization is penetrated, uses reflectivity wave band given threshold first, rejects part cloud layer pixel and water body pixel, then utilizes reference
Infrared image and to normalizing infrared image brightness temperature value formed scatter plot, carry out principal component analysis, calculate main shaft slope, with
Main shaft is standard, upwards downwards scope control choose 60 percent ir radiation data, by maximin layering with
Machine sampling, determines regression equation using quadratic equation with one unknown, as shown in Figure 3.
The bright temperature value normalization process of VIRR is as follows:
(1) normalizing is carried out using 1,2 reflectance values of visible light wave range and near infrared band, given threshold carries out Yun Shuijian
It surveys, rejects cloud water pixel.
(2) it chooses the infrared data of reference and carries out scatterplot recurrence to normalizing infrared data, choose in infrared data cluster
The heart carries out principal component analysis, determines slope, chooses 60 percent ir radiation data downwards upwards, rejecting influences pixel.
(3) using unchanged pixel as target, the normalizing equation of infrared band is determined with linear equation with one unknown.
(4) regressing calculation is carried out to new infrared image figure with regression equation, the infrared image figure after being normalized.
Parameter determines
Brightness temperature is generally defined as temperature corresponding to the radiation energy that remote sensor obtains on star, and brightness temperature can reflect
Atural object temperature conditions.MODIS sensor carries out normalizing using brightness temperature value.When carrying out temperature transition by planck formula,
Atmosphere has the function of that absorbing sieve subtracts to infra-red radiation, while the height of landform has an impact infra-red radiation.Pass through SPSS
20.0 carry out analyzing determining parameter, and parameter a is the atmosphere factor, and parameter b is landform.Work as a=-0.1393, when b=343.17,
MODIS sensor infra-red radiation effect is more preferable.
Surface temperature just refers to the temperature on ground.After the thermal energy radiation to ground of the sun, a part is reflected, a part
It is absorbed by earth's surface, makes the ground gain of heat, surface temperature is carried out measuring resulting temperature being exactly surface temperature.Influence AVHRR earth's surface
Temperature it is many because being known as, including temperature, humidity, atural object surface state, illumination landform etc. influence the factor of surface temperature herein
Mainly temperature and humidity.It enters data into SPSS to carry out analyzing determining parameter, parameter a is temperature, and parameter b is humidity, parameter c
For intensity of illumination.Work as a=0.0049, when b=3.0694, c=778.67, AVHRR sensor infra-red radiation fitting effect is more preferable.
Solar flare is a kind of most violent outburst phenomenon occurred in solar atmosphere regional area.It can be in a short time
Big energy is discharged, regional area is caused instantaneously to heat, launches outward various electromagnetic radiation, and associated particle radiation increases suddenly
By force.Solar flare has an impact to VIRR sensor intermediate infrared radiation wave band, so determining that it is the ginseng of VIRR infra-red radiation normalizing
Number.Simultaneously in most of time, wind and cloud meteorological satellite will receive the cloud cover of large area, while cloud layer reflection with higher
Rate, temperature is higher to influence VIRR sensor multidate infra-red radiation normalizing precision.According to Analysis of infrared radiation, ginseng is determined
Number a is solar flare reflectivity, and b is cloud layer reflectivity.Work as a=0.893, when b=31.868, VIRR infra-red radiation can be reached and returned
The one best fit effect changed.It summarizes three sensors and solves normalized parameter, analyzed with 20.0 software of SPSS, such as table 1
It is shown:
Fitting parameter after the normalization of table 1
Table 1Normalized fitting parameters
As a result accuracy test
In the normalization of MODIS sensor multidate infra-red radiation, using the image in April 12 and April 13 in 2017
Bright temperature data carries out curve fitting, firstly, screening eliminates cloud water pixel, carries out regression analysis by least square method, establishes
Regression equation of unitary, the results showed that, linear correlativity between two width image datas, so the MODIS of the present invention
Multidate infra-red radiation normalization creep function is able to satisfy normalizing requirement, and is more nearly in color.Fitting effect substantially close to
With reference to striograph, as shown in Figure 4.
In the normalization of AVHRR sensor multidate infra-red radiation, using image surface temperature data on April 12nd, 2017
Carry out curve fitting with image surface temperature data on April 13, by screening eliminate cloud water pixel, by least square method into
Row regression analysis finds that binomial relationship is presented between two width image datas.Temperature has atmospheric radiation for Detection Using Thermal Infrared Channel
The main reason for absorbing, surface temperature increases with the increase of temperature.Humidity and surface temperature within certain time,
It is negatively correlated relationship, i.e., when humidity gradually increases, surface temperature is gradually decreased.When solar radiation ground, luminous energy accumulation conversion
It is stored at thermal energy by ground, with the growth of intensity of illumination, surface temperature is gradually risen, i.e., intensity of illumination and surface temperature are in line
Property positive correlation.Fit regression curve is as shown in Figure 5.
In VIRR sensor multidate radiation normalization, using the bright temperature come out from Extraction of Image on April 12 in 2017
Data and bright temperature data on April 13rd, 2017 are fitted, and cloud water pixel is rejected in screening first, are carried out using quadratic equation with one unknown
Regression analysis, the results showed that, linear correlativity between two width image datas, so the VIRR multidate spoke that the present invention establishes
It penetrates normalization creep function and is able to satisfy normalizing requirement, and be more nearly in color.Fitting effect is substantially close to reference striograph, such as
Shown in Fig. 6.
In the effect of radiation normalizing, the linear equation with one unknown that the present invention uses can reject substantially close to image is referred to
It can guarantee Electrodynamic radiation under cloud water influence factor, meet the biggish variation of atural object temperature, divide radiation equally and returned error for the moment,
Realize VIRR sensor multidate infra-red radiation normalizing.In normalized interpretation of result, analyzed herein using principal component,
The purpose of principal component analysis is to be represented the characteristic of whole sample with less characteristic, by principal component analysis obtain slope and
Data compare before intercept and normalizing, obtain radiation normalization coefficient.According to predecessor experience, for three sensor normalizings
Change the result precision of inverting, the present invention uses coefficient of determination R2Normalized model accuracy is evaluated with mean square deviation error RMSE,
Formula is as follows:
Wherein, y0For the infrared data after normalizing via radiation, y1For original infrared 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.Table 2 is the radiation of different sensors
Normalize accuracy test.
The radiation normalization accuracy test of 2 different sensors of table
Table2 Radiation normalized accuracy test for different sensors
If table 2 carries out result precision analysis it is found that after radiation normalization, three sensor radiation normalization fittings are tied
Fruit is all inconsistent, is ranked up the radiation normalization it is found that MODIS sensor close to 1 normalization fitting effect according to slope
Fitting effect is better than VIRR sensor, and the radiation normalization fitting effect of VIRR sensor is better than AVHRR sensor.
Forest fires hot pixel threshold after different sensors multidate infra-red radiation normalizing determines
By carrying out multidate infra-red radiation normalizing to MODIS, AVHRR, VIRR sensor infrared image data, divide equally
The atmosphere errors for not changing bright temperature in infrared image have carried out normalizing to the bright temperature value of background, have highlighted fiery high temperature pixel, exclude
Influence of the factors such as atmosphere, landform, temperature to MODIS, AVHRR, VIRR sensor monitoring atural object temperature, improve MODIS,
The precision of AVHRR, VIRR sensor monitoring atural object temperature.Using normalized infrared image figure by the setting of threshold range,
Forest fires hot spot is differentiated.Weather and geographical background of the invention based on Hunan Province, using 4 μm of bright temperature value of infrared band, with
And dual channel difference carries out the differentiation of MODIS, AVHRR, VIRR sensor forest fires hot spot between 4 μm and 11 μm of infrared bands:
(1) the bright temperature value of 4 μm of infrared bands of MODIS sensor is subjected to multidate infra-red radiation normalizing, when 4 μm of infrared bands
When bright temperature value is less than 317K, i.e. 317K bright temperature value below is in unsaturated state, and table open firing point intensity is smaller, excludes the pixel
A possibility that for high temperature hotspot, determines that formula is as follows:
T4μm< 317K
When 4 μm of bright temperature value of infrared band of MODIS sensor are greater than or equal to 317K, meet high temperature dot requirement, i.e. 317K
Above bright temperature value is in a saturated state, and table open firing point intensity is larger, meets the judgement requirement of high temperature hotspot, and infrared by 4 μm
The bright temperature value of wave band and the bright temperature of 11 μm of infrared bands carry out difference calculating, and when its difference is greater than or equal to 20.9K, determining should
Pixel is doubtful forest fires hot spot, determines that formula is as follows:
T4μm≥317K
T4μm-T11μm≥20.9K
(2) when 4 μm of bright temperature value of infrared band of AVHRR sensor are greater than or equal to 317K, meet high temperature dot requirement, i.e.,
The bright temperature value of 317K or more is in a saturated state, and table open firing point intensity is larger, meets the judgement requirement of high temperature hotspot, and by 4 μm
The bright temperature value of the background of infrared band and 11 μm of infrared bands carries out difference calculating, when its difference is greater than or equal to 21.85K, sentences
The fixed pixel is doubtful forest fires hot spot, determines that formula is as follows:
T4μm≥317K
T4μm-T11μm≥21.85K
(3) the bright temperature value of 4 μm of infrared bands of VIRR sensor is subjected to multidate infra-red radiation normalizing, when 4 μm of infrared bands
When bright temperature value is less than 310K, i.e. 310K bright temperature value below is in unsaturated state, and table open firing point intensity is smaller, excludes the pixel
A possibility that for high temperature hotspot, determines that formula is as follows:
T4μm< 310K
When 4 μm of bright temperature value of infrared band of VIRR sensor are greater than or equal to 310K, meet high temperature dot requirement, i.e. 310K with
On bright temperature value it is in a saturated state, table open firing point intensity is larger, meets the judgement requirement of high temperature hotspot, and by 4 μm of infrared waves
Section and the bright temperature value of the background of 11 μm of infrared bands carry out difference calculating, when its difference is greater than or equal to 19.67K, determine the picture
Member is doubtful forest fires hot spot, determines that formula is as follows:
T4μm≥310K
T4μm-T11μm≥19.67K
Different sensors multidate infra-red radiation normalizing method validation
The present invention uses the VIRR of AVHRR, 15:04:32 of MODIS, 15:47:20 of the April in 2017 of 13:55:45 on the 1st
The partly cloudy image data of clear sky, using three multidate infra-red radiation normalization creep functions constructing of the present invention to infra-red radiation image
Data carry out normalizing, and the multidate image forest fires hot pixel threshold model established after normalizing using the present invention is carried out forest fires hot spot and mentioned
It takes.Respectively as shown in Fig. 7,8,9:
For the present invention by having carried out research and discussion to the above, centering low resolution different sensors remotely-sensed data is red
External radiation Normalization has carried out deep analysis and discussion.
The present invention is when establishing multidate infra-red radiation normalization creep function, it is determined that infra-red radiation normalized parameter, and
Combined mathematical module and the methods of sampling, establish MODIS, AVHRR, VIRR sensor infra-red radiation normalization creep function, using returning
One change model divides atmospheric radiation error equally, reduces the radiation difference from same sensor infrared band difference phase.
Method for normalizing through the invention solves MODIS, AVHRR, the spoke of VIRR sensor infrared band difference phase
Difference problem is penetrated, radiation standard is established, constructs MODIS, AVHRR, the infra-red radiation normalization creep function of VIRR sensor.The present invention
The infra-red radiation normalization creep function of foundation can preferably eliminate radiation differentia influence, make up the time difference of satellite sensor
Property, improve MODIS, AVHRR, accuracy of the VIRR sensor infra-red radiation to atural object variation monitoring.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (7)
1. a kind of different sensors multidate infra-red radiation normalizing method differentiated applied to forest fires hot spot, which is characterized in that should
Method includes the following steps:
Rejecting cloud body influences pixel；
NDVI value is calculated, tree and grass coverage is extracted；
The infrared data of different sensors is subjected to radiation normalization；
Wherein different sensors include MODIS, AVHRR and VIRR sensor；
Wherein, it includes determining that different sensors infra-red radiation is returned that the infrared data by different sensors, which carries out radiation normalization,
One changes parameter；
After obtaining different sensors infra-red radiation normalized parameter, different sensors infrared data is normalized, is obtained new
Infra-red radiation normalize striograph；
The infrared image data of different sensors reference are passed through into curve matching, regression analysis with to normalizing infrared image data,
It establishes respectively and is based on different sensors multidate infra-red radiation normalization creep function；
Precision analysis is carried out to different sensors multidate infra-red radiation normalization creep function；
The precision analysis uses coefficient of determination R2Multidate infra-red radiation normalization creep function essence is evaluated with mean square deviation error RMSE
Degree.
2. the method according to claim 1, wherein MODIS sensor carries out normalizing, energy using brightness temperature value
Reflect atural object temperature conditions；
When carrying out temperature transition by planck formula, atmosphere has the function of attenuation by absorption to infra-red radiation, meanwhile, landform
Height has an impact infra-red radiation；It carrying out analyzing determining parameter by SPSS, parameter a is the atmosphere factor, and parameter b is landform,
The multidate infra-red radiation normalization creep function of MODIS sensor are as follows:
Y=ax+b
Wherein y is the value after normalization, and x is the value before normalization.
3. according to the method described in claim 2, it is characterized in that, a=-0.1393, b=343.17, MODIS sensor
Multidate infra-red radiation normalization creep function are as follows:
Y=-01393x2+343.17 。
4. the method according to claim 1, wherein the factor for influencing AVHRR sensor surface temperature is mainly
Temperature and humidity；It carrying out analyzing determining parameter by SPSS, parameter a is temperature, and parameter b is humidity, and parameter c is intensity of illumination,
The multidate infra-red radiation normalization creep function of AVHRR sensor are as follows:
Y=ax2-bx+c
Wherein y is the value after normalization, and x is the value before normalization.
5. according to the method described in claim 4, it is characterized in that, work as a=0.0049, when b=3.0694, c=778.67,
AVHRR sensor infra-red radiation matched curve is more nearly, the multidate infra-red radiation normalization creep function of AVHRR sensor are as follows:
Y=0.0049x2-3.0694x+778.67。
6. the method according to claim 1, wherein influencing the factor master of VIRR sensor intermediate infrared radiation wave band
If solar flare, it is thus determined that it is the parameter of VIRR sensor infra-red radiation normalizing；In most of time, wind and cloud meteorology is defended
Star will receive the cloud cover of large area, and due to cloud layer reflectivity with higher, it is infrared to influence VIRR sensor multidate
Radiate normalizing precision；According to Analysis of infrared radiation, carry out analyzing determining parameter a being solar flare reflectivity, b by SPSS
For cloud layer reflectivity；The multidate infra-red radiation normalization creep function of VIRR sensor are as follows:
Y=ax+b
Wherein y is the value after normalization, and x is the value before normalization.
7. according to the method described in claim 6, it is characterized in that, work as a=0.9231, when b=22.525, VIRR biography can be reached
The normalized final fitting effect of sensor infra-red radiation；The multidate infra-red radiation normalization creep function of VIRR sensor are as follows: y=
0.9231x+22.525。
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