CN108763782A - The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot - Google Patents
The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot Download PDFInfo
- Publication number
- CN108763782A CN108763782A CN201810549903.3A CN201810549903A CN108763782A CN 108763782 A CN108763782 A CN 108763782A CN 201810549903 A CN201810549903 A CN 201810549903A CN 108763782 A CN108763782 A CN 108763782A
- Authority
- CN
- China
- Prior art keywords
- infra
- radiation
- modis
- sensor
- red radiation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Radiation Pyrometers (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Geometry (AREA)
- Algebra (AREA)
- Life Sciences & Earth Sciences (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Image Processing (AREA)
- Evolutionary Biology (AREA)
Abstract
The present invention relates to a kind of MODIS sensor multidate infra-red radiation normalizing methods that forest fires hot spot differentiates, include the following steps：Rejecting cloud body influences pixel；NDVI values are calculated, tree and grass coverage is extracted；MODIS sensor infrared datas are subjected to radiation calibration；The infrared image data of selection reference carry out curve fitting with normalizing infrared image data are waited for, obtain infra-red radiation normalized parameter, determine infra-red radiation normalization creep function, form new infra-red radiation normalization striograph.Method for normalizing through the invention solves the radiation difference problem of MODIS sensor infrared channel difference phases, establishes radiation standard, builds MODIS sensor multidate infra-red radiation normalization creep functions.The infra-red radiation normalization creep function that the present invention establishes can preferably eliminate radiation differentia influence, and the time difference for making up satellite sensor is anisotropic, improve accuracy of the MODIS sensors infra-red radiation to atural object variation monitoring.
Description
Technical field
The present invention relates to Forest Fire Monitoring technical field more particularly to a kind of MODIS sensings differentiated applied to forest fires hot spot
Device multidate infra-red radiation normalizing 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 remote sensing for many years
Data record the variation of earth's surface and air.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 to carry 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, form the data after a large amount of normalization, the remotely-sensed data of sensor is made to 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 to change, multidate image is more required to come from same sensing
Device so that monitoring has more continuity, however, same sensor can not meet wanting for researchers when monitoring atural object
It asks, so can only be monitored using different sensors.Each sensor situation is as follows：(1) the inconsistent of period is returned to.Intermediate-resolution
Every 1~2 day observation earth of imaging spectrometer MODIS, observes four times and is divided into the morning and afternoon for one day.Third generation practicality intermediate-resolution
Satellite NOAA make weather observations twice a day, is furnished with night-time observation channel.The scanning in one day of Chinese feature cloud meteorological satellite 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 monitor feature changes in real time.If when phase 1 by cloud influenced can not in use, in such a case, it is possible to
Consider to obtain cloudless image of the phase 2 from another sensor, realizes continuous continual monitoring；(3) satellite transit service life
Limitation.Every artificial earth satellite heavenwards, which are emitted to be stopped in orbit, certain service life, based on use
Limitation, will appear sensor radiation difference imaging problem using process, causing monitoring effect 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 be further increased therefore, it is necessary to a kind of
The method of radiation normalization effect.
Invention content
Purpose according to the present invention provides a kind of infrared spoke of MODIS sensor multidates differentiated applied to forest fires hot spot
Penetrate normalizing method, which is characterized in that this method comprises the following steps：
Rejecting cloud body influences pixel；
NDVI values are calculated, tree and grass coverage is extracted；
MODIS sensor ir radiation datas are subjected to radiation calibration；
The infrared image data of selection reference carry out curve fitting with normalizing infrared image data are waited for, obtain infra-red radiation and return
One changes parameter, determines infra-red radiation normalization creep function, forms new infra-red radiation normalization striograph.
According to the atural object of the same race of same sensor have similar gray value, and the air between same sensor image and
Linear relationship is presented in the difference of sensor, and radiation normalization is carried out using linear equation with one unknown：
yn=anxn+bn
Wherein, ynFor the gray value in n wave bands after experiment image normalization, anAnd bnFor in the oblique of n wave band regression equations
Rate and intercept, xnFor experiment image n wave bands gray value；A is obtained by least square methodnAnd bn, calculate normalized shadow
As figure.
The sensing data is MODIS sensor ir radiation datas.
The multidate infra-red radiation normalizing includes that MODIS sensor ir radiation datas are normalized, and is based on
The infrared band characteristic of MODIS selects brightness temperature value to be normalized.
It is as follows that MODIS sensor brightness temperature values normalize flow：
(1) 1,2 reflectance values of visible light wave range and near infrared band are utilized to carry out normalizing, given threshold is examined into the water that racks
It surveys, rejects cloud water pixel；
(2) it chooses the infrared data of reference and waits for that normalizing ir radiation data carries out scatterplot recurrence, choose and meet percent
60 or more infrared 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.
The vegetation-cover index is calculated as：
Wherein, b1For the reflectivity of wave band 1, b2For the reflectivity of wave band 2；When normalized value is more than zero, place pixel quilt
It is identified as vegetative coverage pixel.
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 sensor infra-red radiation normalization creep functions, divide equally using normalization creep function
Atmospheric radiation error reduces the infra-red radiation difference from same sensor difference phase.
Description of the drawings
Fig. 1 is that figure is rejected in cloud detection；
Fig. 2 is that MODIS infra-red radiations normalize flow chart；
Fig. 3 is that MODIS infra-red radiations normalize fitting result；
Fig. 4 is that MODIS sensor multidate infra-red radiations normalize the identification of forest fires hot spot.
Specific implementation mode
When using remote sensing Forest Fire Monitoring hot spot, AVHRR sensors have 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 relatively low,
3 channels are easily saturated the generation for causing false fire point, are difficult to be judged again on the time.And MODIS has higher space-time
Resolution ratio and spectral resolution can make up the defect of AVHRR sensors itself, and uniformly be built in conjunction with VIRR weather satellite datas
Vertical forest fires hot spot judges benchmark, can improve the accuracy of identification of forest fires hot spot.Therefore, it is necessary to build radiation normalizing model, mainly
It is divided into two parts：The infra-red radiation normalizing of multidate infra-red radiation normalizing and different sensors.First, in multidate infra-red radiation
In normalization, MODIS is established respectively, tri- sensor infra-red radiation normalization creep functions of AVHRR, VIRR, and it is different to three
Infra-red radiation normalization creep function carries out determination and the result accuracy test of parameter.The multidate of present invention research MODIS sensors
Infra-red radiation normalizing.
MODIS sensor multidate infra-red radiation normalizings
Radiation normalization purpose is to eliminate cloud layer, water body, influence of the illumination to radiation, correction sensor bias, raising
Radiation normalization precision.In infra-red radiation method for normalizing, rejecting cloud body first influences pixel, calculates NDVI values and extracts vegetation
Area.It obtains MODIS sensors ir radiation data and carries out radiation calibration, Thermal Infrared Data is subjected to planck formula calculating
Brightness temperature value is converted to, atmospheric temperature data are obtained.Threshold value is set by reflectivity, identifies cloud body, water body, and to identifying
Cloud, water pixel rejected, calculate NDVI values extract tree and grass coverage.It chooses the infrared image data referred to and waits for that normalizing is infrared
Image data carries out curve fitting, and obtains infra-red radiation normalized parameter, determines infra-red radiation normalization creep function, is formed new red
External radiation normalizes 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 radiations
Cloud is detected, according to the image of each sensor in the reflectivity of visible light wave range and the bright temperature of Thermal infrared bands into racking
It distinguishes, will have cloud pixel using the relative change rate of the image of close phase to substitute cloud sector pixel gray value, maintain image
Continuity.Multispectral cloud detection algorithm is used in MODIS cloud detection, selects visible channel 1 (0.66 μm), near-infrared logical
Road 6 (1.64 μm are calculated with channel 26 (1.38 μm) data, because the reflectivity of near infrared light is less than visible reflectance, and
Cloud has high reflectance in visible light wave range, and 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, is 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 is more than 40 percent well below cloud layer, Value values, 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 higher 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.Cloud detection is rejected as shown in Figure 1.
There is difference in high sensitivity and Various Seasonal threshold range due to cloud in infrared band, to cloud layer carry out reject and
Cloud reparation, the method for substituting cloud layer using the relative change rate of multidate come inverting, algorithm are 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, have the cloud image picture element value to be：
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 does not vary 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 MODIS sensors, AVHRR sensors and VIRR sensor infrared band characteristics, MODIS sensors are selected
The bright temperature value of the surface temperature and VIRR sensors of brightness temperature value and AVHRR sensors.
Before obtaining MODIS sensor multidate infra-red radiation normalization datas, 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 more than in the reflectivity of visible light wave range in near-infrared wave
Section, is constituted as follows with the normalizing equation of the two wave band：
Wherein, b1For the reflectivity of wave band 1, b2For the reflectivity of wave band 2.When normalized value is more than zero, place pixel quilt
It is identified as vegetative coverage pixel.On sensor MODIS vegetation data acquisitions, vegetative coverage is probably determined by visual method
Pixel, when NDVI is more than a certain range, corresponding pixel is confirmed to be forest cover pixel.
It is normalized based on MODIS sensor multidate infra-red radiations, is manually radiance by the calibration of DN values, after simplification
Planck law calculate 21 wave bands, 31 wave bands bright temperature value.Brightness temperature value is subjected to radiation normalization processing.
Analysis of Influential Factors
The infra-red radiation brightness value that satellite sensor receives is Lλ, include mainly three during infrared radiation transmissions
Part：The upward radiance value L of air ↑, downward terrestrial radiantion, brightness value be L ↓, the true radiance value on ground passes through air
The energy of satellite sensor is reached afterwards, and radiation transfer equation is：
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.The black matrix of temperature T is in the radiance value B (Ts) of infrared band：
B(TS)=[Lλ-L↑-τ(1-ε)*L↓]/τ*ε
In thermal infrared transmission equation, it is seen that air influenced in Detection Using Thermal Infrared Channel it is very big, main absorption including air and
Scattering.Carbon dioxide, ozone, steam, carbon monoxide, suspended matter in air play the role of absorption to air.Wherein steam pair
The absorbability of infra-red radiation is most apparent, and absorption band of water vapor occupies wider wave band, is concentrated mainly on infrared band region.Air
In steam with season, the time, the variation of region and it is inconsistent, and change apparent, 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, is interfered to obtaining terrestrial information.
Solar radiation is the main source of surface energy, has selection and absorption to infra-red radiation when radiating across air, therefore
Air 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, it can only reflect the temperature at the top of cloud layer.Air for infra red radiation band and dust,
Between haze, steam, aerosol there is scattering in effect, there is refraction again when passing through air.Because different sensors different wave length is big
The effect of gas transmission is different, inconsistent, the difference of imaging time of band setting, atmospheric conditions it is unstable, cause samely
The infra-red radiation of object has differences.
Model construction
According to the atural object of the same race of same sensor have similar gray value, and the air 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 in n wave bands after experiment image normalization, anAnd bnFor in the oblique of n wave band regression equations
Rate and intercept, xnFor experiment image n wave bands gray value.A is obtained by least square methodnAnd bn, calculate normalized shadow
As figure.
Infrared band characteristic based on MODIS sensors, it is infrared that the present invention carries out multidate by the way of linear regression
Threshold value, identification cloud body, water body is arranged by reflectivity first in radiation normalization, and the cloud to identifying, water pixel pick
It removes, choose the infrared image data of reference and waits for normalizing infrared image data, choose the infrared data of overlapping region, and according to most
Big minimum value is layered, and therefrom carries out random sampling, sample size meets 60 or more the percent of total quantity.Finally, root
According to the infrared data of two width images after sampling, infra-red radiation normalizing equation is determined by least square method.As shown in Figure 2.
It is as follows that MODIS brightness temperature values normalize flow：
(1) 1,2 reflectance values of visible light wave range and near infrared band are utilized to carry out normalizing, given threshold is examined into the water that racks
It surveys, rejects cloud water pixel.
(2) it chooses the infrared data of reference and waits for that normalizing infrared data carries out scatterplot recurrence, choose and meet 60 percent
Above infrared 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.
Parameter determines
Brightness temperature is generally defined as the temperature corresponding to the radiation energy that remote sensor obtains on star, can reflect atural object temperature
Situation.MODIS sensors carry out normalizing using brightness temperature value.When carrying out temperature transition by planck formula, air is to red
External radiation has the function of attenuation by absorption, while the height of landform also has an impact normalizing process.It is analyzed by SPSS
Determine that parameter, parameter a are the air factor, parameter b is landform.Work as a=-0.1393, when b=343.17, MODIS sensors are infrared
It is more preferable to radiate fitting effect.It summarizes MODIS sensors and solves infra-red radiation normalized parameter, divided with 20.0 softwares of SPSS
Analysis, obtaining MODIS sensor multidate infra-red radiation normalization creep functions is：Y=-0.1535x+347.39.
As a result accuracy test
In the normalization of MODIS sensor multidate infra-red radiations, using the image in April 12 and April 13 in 2017
Bright temperature data carries out curve fitting, and first, 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 normalizing model can meet normalizing requirement, and be more nearly in color.Fitting effect is substantially close to ginseng
Striograph is examined, as shown in Figure 3.
In the effect of radiation normalizing, the linear equation with one unknown that the present invention uses can be approached with reference to image substantially, rejected
It can ensure Electrodynamic radiation under cloud water influence factor, meet the larger variation of atural object temperature, divide radiation equally and returned error for the moment,
Realize MODIS sensor multidate infra-red radiation normalizings.According to the experience of forefathers, inverting is normalized for MODIS sensors
As a result precision, the present invention use 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 1 is that MODIS sensors are infrared
Radiation normalization accuracy test.
Table 1MODIS sensor infra-red radiations normalize accuracy test
Table1 The infrared radiation normalized precision test of MODIS
sensor
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.
Forest fires hot pixel threshold after MODIS sensor multidate infra-red radiation normalizings determines
By carrying out multidate infra-red radiation normalizing to MODIS sensor infrared image data, divide equally in infrared image
The atmosphere errors for not changing bright temperature have carried out normalizing to the bright temperature value of background, have highlighted fiery high temperature pixel, eliminate air,
Influence of the shape to MODIS Sensor monitoring atural object temperature, improves the precision of Sensor monitoring atural object temperature.Using normalized
Infrared image figure differentiates forest fires hot spot by the setting of threshold range.Weather and geography of the invention based on Hunan Province
Background carries out forest fires hot spot differentiation using dual channel difference between the bright temperature value of 4 μm of infrared bands and 4 μm and 11 μm of infrared bands：
(1) the bright temperature value of 4 μm of infrared bands of MODIS sensors 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
For the possibility of high temperature hotspot, judge that formula is as follows：
T4 μm of < 317K
(2) when 4 μm of bright temperature value of infrared band of MODIS sensors are greater than or equal to 317K, meet high temperature dot requirement, i.e.,
The bright temperature value of 317K or more is in saturation 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 infrared band and the bright temperature of 11 μm of infrared bands carry out mathematic interpolation, when its difference is greater than or equal to 20.9K, sentence
The fixed pixel is doubtful forest fires hot spot, and judgement formula is as follows：
T4μm≥317K
T4μm-T11μm≥20.9K
MODIS sensor multidate infra-red radiation normalizing method validations
The present invention uses 1 day 13 April in 2017:55:The 45 partly cloudy image data of clear sky is built using the present invention
MODIS multidate infra-red radiation normalization creep functions carry out normalizing to multidate ir radiation data, are built using the present invention after normalizing
Vertical forest fires hot pixel threshold model carries out forest fires hot spot extraction.As shown in Figure 4：
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 the multidate infra-red radiation normalization creep function of MODIS sensors, using normalization
Model has divided atmospheric radiation error equally, reduces the infra-red radiation difference from same sensor difference phase.
Method for normalizing through the invention solves MODIS sensor difference phase infra-red radiation difference problems, establishes spoke
Standard is penetrated, MODIS sensor multidate infra-red radiation normalization creep functions are built.The infra-red radiation normalization creep function that the present invention establishes
Radiation differentia influence can be preferably eliminated, the time difference for making up satellite sensor is anisotropic, improves MODIS sensor infra-red radiations pair
The accuracy of feature changes monitoring.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Cannot the limitation to the scope of the claims of the present invention therefore 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 the guarantor of the present invention
Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (6)
1. a kind of MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot, which is characterized in that should
Method includes the following steps：
Rejecting cloud body influences pixel；
NDVI values are calculated, tree and grass coverage is extracted；
MODIS sensor infrared datas are subjected to radiation calibration；
The infrared image data of selection reference carry out curve fitting with normalizing infrared image data are waited for, obtain infra-red radiation normalization
Parameter determines infra-red radiation normalization creep function, forms new infra-red radiation normalization striograph.
The sensing data is MODIS sensor ir radiation datas.
The multidate infra-red radiation normalizing includes that the ir radiation data of MODIS sensors is normalized；Based on MODIS
Infrared band characteristic, select brightness temperature value be normalized.
2. according to the method described in claim 1, it is characterized in that, there is similar ash according to the atural object of the same race of same sensor
Angle value, and the air and sensor between same sensor image difference present linear relationship, using linear equation with one unknown into
Row radiation normalization：
yn=anxn+bn,
Wherein, ynFor the gray value in n wave bands after experiment image data normalization, anAnd bnFor in the oblique of n wave band regression equations
Rate and intercept, xnFor experiment image data n wave bands gray value；A is obtained by least square methodnAnd bn, calculate normalization
Striograph.
3. according to the method described in claim 1, it is characterized in that, the sensing data is MODIS sensor infra-red radiations
Data.
4. according to the method described in claim 3, it is characterized in that, the multidate infra-red radiation normalizing includes to MODIS
Sensor ir radiation data is normalized, and brightness temperature value is normalized in the infrared band characteristic based on MODIS.
5. according to the method described in claim 4, it is characterized in that, MODIS sensor brightness temperature values normalization flow is as follows：
(1) 1,2 reflectance values of visible light wave range and near infrared band is utilized to carry out normalizing, given threshold is detected into the water that racks,
Reject cloud water pixel；
(2) it chooses the infrared data of reference and waits for that normalizing infrared data carries out scatterplot recurrence, choose and meet 60 or more percent
Infrared data, reject influence pixel
(3) using unchanged pixel as target, the normalizing equation of infra red radiation band is determined using least square method；
(4) regressing calculation is carried out to new infrared image figure with regression equation, the infra-red radiation striograph after being normalized.
6. according to the method described in claim 5, it is characterized in that, the vegetation-cover index is calculated as：
Wherein, b1For the reflectivity of wave band 1, b2For the reflectivity of wave band 2；When normalized value is more than zero, place pixel is identified
For vegetative coverage pixel.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810549903.3A CN108763782A (en) | 2018-05-31 | 2018-05-31 | The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810549903.3A CN108763782A (en) | 2018-05-31 | 2018-05-31 | The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108763782A true CN108763782A (en) | 2018-11-06 |
Family
ID=64001331
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810549903.3A Pending CN108763782A (en) | 2018-05-31 | 2018-05-31 | The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108763782A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110532986A (en) * | 2019-09-04 | 2019-12-03 | 云南电网有限责任公司带电作业分公司 | Mountain fire detection algorithm, system and its storage medium based on MODIS remote sensing image |
| CN113221057A (en) * | 2021-05-14 | 2021-08-06 | 山东省生态环境监测中心 | Straw burning fire point monitoring method based on multi-temporal satellite images |
| CN113221634A (en) * | 2021-03-29 | 2021-08-06 | 重庆市规划和自然资源调查监测院 | Low-radiation calibration precision optical remote sensing image cloud identification method and system and computer storage medium |
| CN117371333A (en) * | 2023-11-17 | 2024-01-09 | 中国农业科学院农业资源与农业区划研究所 | Surface temperature product angle normalization method based on nuclear drive model |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777174A (en) * | 2009-12-30 | 2010-07-14 | 深圳先进技术研究院 | Relative radiation normalization method for multi-temporal remote sensing image |
| CN102565778A (en) * | 2011-12-12 | 2012-07-11 | 中国科学院遥感应用研究所 | Relative radiometric correction method for automatically extracting pseudo-invariant features for remote sensing image |
| US20140270569A1 (en) * | 2013-03-15 | 2014-09-18 | Digitalglobe, Inc. | Automated geospatial image mosaic generation |
| CN106295696A (en) * | 2016-08-09 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of multi-source Remote Sensing Images radiation normalization method |
-
2018
- 2018-05-31 CN CN201810549903.3A patent/CN108763782A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101777174A (en) * | 2009-12-30 | 2010-07-14 | 深圳先进技术研究院 | Relative radiation normalization method for multi-temporal remote sensing image |
| CN102565778A (en) * | 2011-12-12 | 2012-07-11 | 中国科学院遥感应用研究所 | Relative radiometric correction method for automatically extracting pseudo-invariant features for remote sensing image |
| US20140270569A1 (en) * | 2013-03-15 | 2014-09-18 | Digitalglobe, Inc. | Automated geospatial image mosaic generation |
| CN106295696A (en) * | 2016-08-09 | 2017-01-04 | 中国科学院遥感与数字地球研究所 | A kind of multi-source Remote Sensing Images radiation normalization method |
Non-Patent Citations (5)
| Title |
|---|
| 余晓敏 等: "基于改进的自动散点控制回归算法的遥感影像相对辐射归一化", 《光学技术》 * |
| 周小成 等: "MODIS林火识别算法的验证分析", 《火灾科学》 * |
| 周永宝: "基于MODIS的森林火灾预警与检测研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
| 李微 等: "基于光谱分析的MODIS云检测算法研究", 《武汉大学学报》 * |
| 杨铁利 等: "MODIS数据的云检测处理", 《鞍山科技大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110532986A (en) * | 2019-09-04 | 2019-12-03 | 云南电网有限责任公司带电作业分公司 | Mountain fire detection algorithm, system and its storage medium based on MODIS remote sensing image |
| CN113221634A (en) * | 2021-03-29 | 2021-08-06 | 重庆市规划和自然资源调查监测院 | Low-radiation calibration precision optical remote sensing image cloud identification method and system and computer storage medium |
| CN113221634B (en) * | 2021-03-29 | 2023-09-22 | 重庆市规划和自然资源调查监测院 | Low-radiation calibration precision optical remote sensing image cloud identification method, system and computer storage medium |
| CN113221057A (en) * | 2021-05-14 | 2021-08-06 | 山东省生态环境监测中心 | Straw burning fire point monitoring method based on multi-temporal satellite images |
| CN117371333A (en) * | 2023-11-17 | 2024-01-09 | 中国农业科学院农业资源与农业区划研究所 | Surface temperature product angle normalization method based on nuclear drive model |
| CN117371333B (en) * | 2023-11-17 | 2024-03-22 | 中国农业科学院农业资源与农业区划研究所 | Surface temperature product angle normalization method based on nuclear drive model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108763782A (en) | The MODIS sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot | |
| CN108731817B (en) | Different sensor infrared radiation normalization modeling method applied to forest fire hot spot discrimination | |
| North et al. | Retrieval of land surface bidirectional reflectance and aerosol opacity from ATSR-2 multiangle imagery | |
| US5471056A (en) | Airborne scanner image spectrometer | |
| CN104697943B (en) | A kind of lossless detection method of rape water stress | |
| Kaufman et al. | Detection of dust over deserts using satellite data in the solar wavelengths | |
| Lück et al. | Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images | |
| Thomas et al. | Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers | |
| Stubenrauch et al. | Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications | |
| Saltz et al. | Assessing grazing impacts by remote sensing in hyper-arid environments. | |
| CN108734150A (en) | The AVHRR sensor multidate infra-red radiation normalizing methods differentiated applied to forest fires hot spot | |
| Parker et al. | Consequences of environmental heterogeneity for the photosynthetic light environment of a tropical forest | |
| CN113553907A (en) | Forest ecological environment condition evaluation method based on remote sensing technology | |
| CN115292616B (en) | Vegetation blue sky albedo estimation method and device based on spectrum invariant theory | |
| CN108680534B (en) | Optical channel on-orbit calibration verification method based on medium wave infrared reflectivity reference | |
| Ogunjobi et al. | Intercomparison and validation of satellite and ground-based aerosol optical depth (AOD) retrievals over six AERONET sites in West Africa | |
| CN110838098B (en) | Method for determining surface fractures of underground coal fire area | |
| Ientilucci | Leveraging lidar data to aid in hyperspectral image target detection in the radiance domain | |
| CN109035663B (en) | Multi-temporal infrared radiation normalization method for different sensors applied to forest fire hot spot discrimination | |
| EP2162709A2 (en) | Atmospheric gas detection apparatus and method | |
| Schläpfer et al. | Correction of shadowing in imaging spectroscopy data by quantification of the proportion of diffuse illumination | |
| Li et al. | Restoration of wintertime ocean color remote sensing products for the high-latitude oceans of the Southern Hemisphere | |
| Sun et al. | A satellite imagery smoke detection framework based on the Mahalanobis distance for early fire identification and positioning | |
| CN107945162A (en) | Detection recognition method based on computer vision technique to flourishing ant nest | |
| Petropoulos et al. | Multispectral vegetation indices in remote sensing: an overview |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181106 |