CN109035663B - Multi-temporal infrared radiation normalization method for different sensors applied to forest fire hot spot discrimination - Google Patents
Multi-temporal infrared radiation normalization method for different sensors applied to forest fire hot spot discrimination Download PDFInfo
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Abstract
The invention relates to a multi-temporal infrared radiation normalization method for different sensors applied to forest fire discrimination, which comprises the following steps: eliminating cloud influence pixels; calculating an NDVI value, and extracting vegetation area; carrying out radiation normalization on infrared data of different sensors; acquiring infrared radiation normalization parameters of different sensors, and normalizing infrared data of different sensors to obtain a normalized multi-temporal infrared image; respectively establishing multi-temporal infrared radiation normalization models based on different sensors by curve fitting and regression analysis of infrared image data referenced by the different sensors and infrared image data to be normalized; carrying out precision analysis on infrared radiation normalized models of different sensors; the precision analysis adopts a determination coefficient R2And the mean square error RMSE is used for evaluating the accuracy of the multi-temporal infrared radiation normalization model. By the normalization method, the problem of radiation difference of different time phases of the infrared wave band of the sensor can be solved, and a radiation standard is established.
Description
Technical Field
The invention relates to the technical field of forest fire monitoring, in particular to a multi-temporal infrared radiation normalization method for different sensors applied to forest fire hot spot discrimination.
Background
Remote sensing has been observed to the ground for decades of observation history, and because the sensors that satellite-borne or airborne platforms used are different, the image data that acquires have different geometry, radiation and image characteristics. The existing remote sensing satellite has been applied to various weather, resource, ocean, environment disaster reduction and other fields, has already realized the industrial development of the related fields, and meanwhile, has accumulated the remote sensing data for many years and records the changes of the earth surface and the atmosphere. The precious historical resources are fully utilized, and the method has great historical significance for global climate research, development of economic society and development of human civilization. The high-precision radiometric calibration of the sensor is the basis for producing the quantitative remote sensing products, and for data of different platforms, how to realize cross-platform use among the multi-platform remote sensing data is a new direction for development of the remote sensing data in recent years. By effectively utilizing the historical observation data, the problem of recalibration of the historical data needs to be solved, a large amount of normalized data is formed, and the remote sensing data of the sensors are normalized to the same radiation reference (namely multi-source radiation normalization), so that the satellite remote sensing detection data can be converted among the sensors of different platforms, the data loss of a certain area of the same type of sensor can be compensated in time, and the method has great significance for remote sensing data application.
The same sensor is adopted to monitor the ground object, and when the change is judged, multi-time-phase images are required to come from the same sensor, so that the monitoring is more continuous, however, the same sensor cannot meet the requirements of researchers when monitoring the ground object, and only different sensors can be adopted to monitor. The sensor conditions were as follows: (1) inconsistency of the reentry period. The medium-resolution imaging spectrometer MODIS observes the earth every 1-2 days, and four times of observation in one day are divided into the morning and the afternoon. In the third generation, the meteorological satellite NOAA is observed twice a day with resolution ratio and is provided with a night observation channel. Chinese wind and cloud meteorological satellites scan twice a day. Due to the discontinuity of the monitoring of each sensor in time, the monitoring effect is poor; (2) a change in weather conditions. At a certain moment, due to regional reasons, the influence of cloud and shadow often appears, and in the imaging process of the image, the loss of ground feature information is caused, and the change of the ground features cannot be monitored in real time by adopting a single sensor. If, when phase 1 is not usable due to the influence of clouds, in this case, it may be considered to acquire a cloud-free image of phase 2 from another sensor. Continuous and uninterrupted monitoring is realized; (3) the operational life of the satellite. Each artificial earth satellite is launched to the sky and stops working when operating in orbit, and the problem of radiation difference imaging of the sensor can occur in the using process based on the use limitation, so that the monitoring effect is poor.
The radiation data can be normalized by combining the monitoring time and the monitoring effect of different sensors, and multi-time-phase radiation normalization of different sensors is realized. Previously, the remote sensing radiation normalization is still a pixel-based relative radiation normalization method aiming at medium and low resolution ratio. In recent years, with the continuous development of the space remote sensing technology, the spatial resolution of remote sensing images is continuously improved, and the relative radiation normalization method of high-resolution remote sensing images of different remote sensing sensors is receiving more and more attention. Although some methods can achieve better results, there are many disadvantages, and therefore, a method for further improving the radiation normalization effect is needed.
Disclosure of Invention
According to the purpose of the invention, the multi-temporal infrared radiation normalization method applied to different sensors for forest fire hot spot discrimination is provided, and the method comprises the following steps:
eliminating cloud influence pixels;
calculating an NDVI value, and extracting vegetation area;
carrying out radiation normalization on infrared data of different sensors;
wherein the different sensors include MODIS, AVHRR, and VIRR sensors;
performing radiation normalization on the infrared data of different sensors comprises determining infrared radiation normalization parameters of different sensors;
after infrared radiation normalization parameters of different sensors are obtained, normalization is carried out on infrared radiation data of different sensors to obtain a new infrared radiation normalization image map;
performing regression analysis on infrared image data referenced by different sensors and infrared image data to be normalized through curve fitting, and respectively establishing multi-temporal infrared radiation normalization models based on different sensors;
performing precision analysis on multi-temporal infrared radiation normalized models of different sensors;
the precision analysis adopts a determination coefficient R2And the mean square error RMSE is used for evaluating the accuracy of the multi-temporal infrared radiation normalization model of different sensors.
Preferably, the MODIS sensor is normalized by adopting a brightness temperature value, and can reflect the temperature condition of the ground objects;
when temperature conversion is carried out through a Planck formula, the atmosphere has the function of absorbing and screening infrared radiation, and meanwhile, the height of the terrain influences the infrared radiation;
analyzing and determining parameters through SPSS, wherein the parameter a is an atmospheric factor, the parameter b is a terrain, and a multi-temporal normalization model of the MODIS sensor is as follows:
y＝ax+b
where y is the normalized value and x is the value before normalization.
When a is-0.1393 and b is 343.17, the multi-temporal infrared radiation normalized model of the MODIS sensor is:
y＝-0.1393x+343.17
preferably, the factors influencing the surface temperature of the AVHRR sensor are mainly air temperature and humidity; analyzing and determining parameters through the SPSS, wherein the parameter a is air temperature, the parameter b is humidity, the parameter c is illumination intensity, and a multi-temporal infrared radiation normalization model of the AVHRR sensor is as follows:
y＝ax2-bx+c
where y is the normalized value and x is the value before normalization.
When a is 0.0049, b is 3.0694, and c is 778.67, the AVHRR sensor infrared radiation fitting curve is closer, and the multi-temporal infrared radiation normalization model of the AVHRR sensor is:
y＝0.0049x2-3.0694x+778.67
further, the factor influencing the infrared radiation wave band in the VIRR sensor is mainly solar flare, so that the factor is determined as a parameter for normalizing the infrared radiation of the VIRR sensor; in most of time, the wind cloud meteorological satellite can be covered by a large-area cloud layer, and the multi-temporal infrared radiation normalization precision of the VIRR sensor can be influenced due to the fact that the cloud layer has high reflectivity; according to the analysis of the infrared radiation characteristics, the parameter a is determined as the solar flare reflectivity through the SPSS analysis, and the parameter b is determined as the cloud layer reflectivity; the multi-temporal infrared radiation normalization model of the VIRR sensor is as follows:
y＝ax+b
where y is the normalized value and x is the value before normalization.
When a is 0.9231 and b is 22.525, the best fitting effect of infrared radiation normalization of the VIRR sensor can be achieved; the multi-temporal infrared radiation normalization model of the VIRR sensor is as follows:
y＝0.9231x+22.525
according to the invention, when a multi-temporal infrared radiation normalization model is established, infrared radiation normalization parameters are determined, and the MODIS, AVHRR and VIRR sensor multi-temporal infrared radiation normalization model is established by combining a mathematical model and a sampling method, atmospheric radiation errors are equally divided by applying the normalization model, and radiation differences from different time phases of the infrared band of the same sensor are reduced.
Drawings
FIG. 1 is a cloud detection culling diagram;
FIG. 2 is a flowchart of MODIS infrared radiation normalization;
FIG. 3 is a flowchart of VIRR infrared radiation normalization;
FIG. 4 shows the fitting result of MODIS infrared radiation normalization;
FIG. 5 is a normalized fitting result of AVHRR infrared radiation;
FIG. 6 is a VIRR infrared radiation normalized fit;
FIG. 7 is MODIS sensor multi-temporal infrared radiation normalized forest fire hot spot identification;
FIG. 8 is an AVHRR sensor multi-temporal infrared radiation normalized forest fire hot spot identification;
FIG. 9 illustrates multi-temporal infrared radiation normalized forest fire hot spot identification by a VIRR sensor.
Detailed Description
When forest fire hot spots are monitored by remote sensing, the NOAA sensor has high spatial resolution and wide coverage range, when forest fire hot spot energy is received, the channel 3 has sensitive characteristics and can quickly monitor the forest fire hot spots, but when the saturation temperature is low, the channel 3 is easy to saturate to cause generation of false fire spots, and the judgment is difficult to be carried out again in time. And the MODIS can make up the defects of the AVHRR sensor at higher space-time resolution and spectral resolution, and can uniformly establish forest fire hot spot judgment reference by combining VIRR meteorological satellite data, thereby improving the identification precision of forest fire hot spots. Therefore, a radiation normalization model needs to be constructed, which is mainly divided into two parts: multi-temporal infrared radiation normalization and infrared radiation normalization of different sensors. On multi-temporal infrared radiation normalization, multi-temporal infrared radiation models of three sensors, namely MODIS, AVHRR and VIRR are respectively established, and parameter determination and result precision inspection are carried out on the multi-temporal infrared radiation normalization models of the three sensors. And determining an infrared radiation unified parameter on multi-temporal infrared radiation normalization of different sensors, normalizing the infrared radiation through the maximum and minimum values, constructing an infrared radiation normalization model, and analyzing the result and checking the model precision. The invention researches multi-temporal infrared radiation normalization of different sensors.
Multi-temporal infrared radiation normalization of different sensors
The radiation normalization aims at eliminating the influence of cloud layers, water bodies and illumination on radiation, correcting sensor deviation and improving radiation normalization precision. In the radiation normalization method, cloud influence pixels are removed, and the NDVI value is calculated to extract the vegetation area. And (4) performing radiation calibration on the MODIS, AVHRR and VIRR infrared data to obtain thermal infrared radiation data of the MODIS sensor, and converting the thermal infrared radiation data into a brightness temperature value. And acquiring AVHRR surface temperature data and VIRR brightness temperature data. And setting a threshold value through the reflectivity, identifying the cloud body and the water body, and removing the identified cloud and water pixels. And classifying the ground objects into land, forest vegetation and water bodies through heavy classification. And selecting reference infrared image data and to-be-normalized infrared image data to perform curve fitting, acquiring infrared radiation normalization parameters, determining an infrared radiation normalization model, and forming a new infrared radiation normalization image map.
Cloud water removal
Before multi-temporal infrared radiation normalization of MODIS sensors, clouds need to be detected on the premise that cloud water interference pixels are eliminated, the clouds are distinguished according to the reflectivity of images of the sensors in a visible light band and the brightness temperature of a thermal infrared band, the cloud pixels adopt the relative change rate of images of similar temporal phases to replace the gray value of the cloud pixels, and the continuity of the images is kept. In an MODIS cloud detection multi-spectral cloud detection algorithm, data of a visible light channel 1(0.66 mu m), a near infrared channel 6(1.64 mu m) and a channel 26(1.38 mu m) are selected for calculation, and as the reflectivity of near infrared light is lower than that of visible light, and the cloud has high reflectivity in a visible light wave band, a cloud body is detected according to a vegetation index method. The formula is as follows:
wherein ch (n) is a reflectance value in the n channel, and the cloud detection is determined as follows:
CH(26)>T1
T2<Value<T3and CH (1)>T4
According to the setting of the threshold value range, the reflectivity of the vegetation and the land is less than or equal to zero and less than the reflectivity of visible light, when T is2When the number is 0, the covering picture element is vegetation land. In the reflectivity of the channel 6, since the cloud layer reflects all solar radiation, the snow absorbs the solar radiation directly, so that the emissivity of the snow is much lower than that of the cloud layer, the Value is more than forty percent, and therefore T is set30.4. In the reflectivity of the channel 26, since the cloud is similar to the snow in spectral response, the cloud reflectivity is statistically greater than ten percent, and is higher than that of other objects to eliminate missed detection results, so T10.1. According to the difficulty in distinguishing the water body from the cloud and the low reflectivity of the water body, the T is less than twenty percent4＝0.2。
A multi-channel dynamic threshold cloud detection algorithm is adopted in AVHRR cloud detection, and the method is characterized in that the threshold determination of clear sky and cloud pixels is carried out by taking the maximum extreme point of the second-order difference of the histogram curve on one side of the cloud layer part of the earth surface peak value and the position of the maximum variability of the histogram curve in the histogram curve of a certain channel pixel array. In 5 channels of the AVHRR sensor, ratio calculation and difference calculation of 3, 4 and 5 channels are carried out on the channels 1 and 2, selection of a histogram and a threshold value is carried out on result data, and pixels covered by cloud are judged. In the multi-temporal AVHRR observation data processing, in order to keep the continuous use of data, weighting processing is carried out according to the satellite zenith angle and the dynamic threshold of a channel, and the cloud detection effect is accurate. Meanwhile, the dynamic threshold needs to be smoothed when the discontinuity of the edge data occurs based on the discontinuity of the pixel data.
In the VIRR cloud detection, a multispectral threshold method is adopted for cloud identification, and thresholds are set on visible light and infrared channels by using the difference between the reflectivity and the radiance value of the cloud and other ground objects. And in an infrared window area of 11 mu m, setting low temperature to detect the cloud. Through a large number of experiments and statistical analysis, the brightness temperature value of the image pixel is lower than T <267K, and then the image pixel can be judged as a cloud layer. When T is greater than 273K, the vehicle is judged to be clear sky. Due to the change difference of the reflectivity of the earth surface in the daytime, the difficulty of identifying the cloud by the infrared band is increased, the threshold value is set by combining the reflectivity of visible light, and the cloud detection algorithm is as follows:
wherein R is1Is the visible light channel reflectivity, R2Is the near infrared channel reflectivity. RI is the threshold range.
R3>RIthAnd RIth_Min<RI<RIth_MaxAnd T4<T4th
Wherein, T4Is equivalent black body radiation bright temperature of a thermal infrared channel (10.3-11.3 mu m). RI (Ri)th、T4thAre each R1And T4Of (3) a threshold value, RIth_Min、RIth_MaxRespectively, the upper and lower threshold values of RI. Satisfy RI by data statisticsth<267K，T4th＝237K，RIthmax＝1.1，RIth_minA condition of 0.95 or the like can identify a cloud. Cloud detection culling is shown in fig. 1.
In addition, because the cloud has high sensitivity in the infrared band and the threshold ranges in different seasons have differences, the cloud layer is removed and repaired, and a method for inverting and replacing the cloud layer by adopting the relative change rate of multiple time phases is adopted, and the algorithm is as follows:
setting the image of the cloud as X and the alternate image as Y,
order to
m and n are the number of the image pixels and the alternative pixels, no cloud exists, and the value of the image pixels with the cloud is as follows:
wherein x isi，yi，xmax，xmin，ymax，yminThe values of the image pixel and the alternative image pixel, the maximum value and the minimum value are respectively. When the overlapping position of the two images is not changed greatly, the method can be used for repairing the image of the cloud layer area. On the contrary, the pixel value is greatly changed by using the algorithm.
Data acquisition
Radiation peak wavelength λ according to wien's lawmaxAnd is inversely proportional to the black body temperature T. The higher the temperature, the shorter the wavelength shifts. And selecting the brightness temperature value of the MODIS sensor, the surface temperature of the AVHRR sensor and the brightness temperature value of the VIRR sensor according to the infrared band characteristics of the MODIS sensor, the AVHRR sensor and the VIRR sensor.
Before acquiring multi-temporal infrared radiation normalized data of the MODIS sensor, forest vegetation extraction is required. According to the remote sensing principle, in the wave band setting of the medium-resolution satellite sensor, the infrared wave band cannot distinguish the ground object type, and the reflectivity can reflect the vegetation coverage condition in the visible light wave band. The reflectivity of the forest vegetation in visible light is larger than that in near infrared, and the forest vegetation has the following structure according to a normalization equation of the two wave bands:
wherein, b1Reflectivity in band 1, b2The reflectivity of band 2. Dang Gui has large normalized valueAt zero time, the pixel is identified as a vegetation coverage pixel. On the vegetation data acquisition of the MODIS sensor, the covered pixels are approximately confirmed by an visual inspection method, and when the NDVI is larger than a certain range, the corresponding pixels are confirmed to be forest vegetation pixels.
Based on MODIS sensor multi-temporal infrared radiation normalization, DN value is manually calibrated to radiance, and simplified Planck's law is used to calculate brightness temperature values of 21 and 31 wave bands. And carrying out radiation normalization processing on the brightness temperature value.
On the AVHRR sensor radiation normalization method, a split-split window algorithm is adopted to perform bright temperature linear fitting of the thermal infrared channels 4 and 5 wave bands to obtain the surface temperature, and the multi-time-phase normalization of the AVHRR sensor is performed. The formula is as follows:
T0＝a+bT4+cT5
wherein: a. b and c are constants which mainly depend on the content of moisture in the atmosphere and the ground emissivity. An improved split-split window algorithm was proposed in conjunction with coll as follows:
T0＝T4+[1.34+0.39(T4-T5)(T4-T5)+α(1-)-βΔ+0.56]
α＝ω3-8ω2+17ω+40
β＝150(1-ω/4.5)
wherein: t is0Is the surface temperature in (K), T4And T5Is the brightness temperature value of AVHRR 4 channel and 5 channel thermal infrared channel, omega is the content of atmospheric water and the unit is (g/cm)2) The average emissivity of the 4-channel and 5-channel thermal infrared channels is given, and Δ is the difference between the emissivity of the 4-channel and 5-channel.
When multi-temporal infrared radiation normalized data of the VIRR sensor are obtained, the multi-temporal image normalization is carried out by adopting the brightness temperature value. Firstly, performing on-satellite calibration by using VIRR sensor data, calculating, then performing radiance nonlinear correction by using a linear calibration radiance value, finally calculating an assumed blackbody temperature, and finally calculating the assumed blackbody temperature, wherein the calculation formula is as follows:
analysis of influence factors
The infrared radiation received by the sensor has a brightness value LλIn the infrared radiation transmission process, the infrared radiation transmission device mainly comprises three parts: the atmospheric upward radiation brightness value L ↓, the atmospheric downward radiation brightness value L ↓, the real radiation brightness value on the ground reaches the energy of the satellite sensor after the atmospheric, the radiation transmission equation is:
Lλ＝[*B(Ts)+(1-)L↓]*τ+L↑
wherein, is emissivity, TSThe real temperature of the earth surface, B (Ts) the heat radiation brightness of the black body at Ts calculated by Planck's law, and tau the atmospheric transmittance. The radiation brightness value B (Ts) of the black body at the temperature T in the infrared band is as follows:
B(TS)＝[Lλ-L↑-τ(1-)*L↓]/τ*
in the thermal infrared transmission equation, the visible atmosphere has a great influence on the thermal infrared path, and mainly includes absorption and scattering of the atmosphere. Carbon dioxide, ozone, water vapor, carbon monoxide and suspended matters in the atmosphere have the function of absorbing the atmosphere. The absorption capacity of the water vapor to infrared radiation is most obvious, and the water vapor absorption band occupies a wider waveband and is mainly concentrated in an infrared waveband area. The water vapor in the atmosphere is inconsistent with the change of seasons, time and regions, and has obvious change and larger floating. The higher the moisture content, the greater the absorption of moisture by the infrared radiation band. Secondly, carbon dioxide gas has certain influence on infrared radiation, and although the absorption of solar radiation energy is less, the carbon dioxide gas can absorb ground radiation energy and emit long-wave radiation to the periphery, so that the interference on the acquisition of ground information is caused. Solar radiation is the primary source of surface energy, and radiation has the selective and absorptive effect of infrared radiation as it passes through the atmosphere, so that the atmosphere absorbs some of the radiant energy and converts it into internal energy. Meanwhile, due to the influence of atmospheric molecules and aerosol, energy is converted into heat energy and ionization energy. The cloud layer also has strong absorption capacity in an infrared radiation wave band, and when the sensor detects the real temperature of a ground object, the temperature at the top of the cloud layer can only be reflected due to the covering of the cloud layer, so that the atmospheric air acts on the change of the cloud layer to be enhanced and weakened. The atmosphere has scattering to the effect between infrared radiation wave band and dust, haze, vapour, aerosol, has the refraction again when passing the atmosphere. Different sensors have different atmospheric transmission effects with different wavelengths, inconsistent wave band settings, different imaging time and unstable atmospheric conditions, so that the radiation of the same ground object has different effects.
Model construction
According to the fact that the same ground object of the same sensor has similar gray values, and the difference between the atmosphere and the sensor between the images of the same sensor presents a linear relation, namely the gray values of the same wave band have a linear relation, the radiation normalization is carried out by adopting a unitary linear equation:
yn＝anxn+bn
wherein, ynIs the gray value of n wave bands after the normalization of the experimental image, anAnd bnFor the slope and intercept of the regression equation in the n-band, xnThe gray value of the experimental image in the n-band is shown. Obtaining a by least squaresnAnd bnAnd calculating a normalized image map.
Based on the infrared band characteristics of the MODIS sensor, the infrared radiation normalization is carried out in a linear regression mode, firstly, a threshold value is set through the reflectivity, cloud bodies and water bodies are identified, identified cloud and water pixels are eliminated, reference infrared image data and infrared image data to be normalized are selected, infrared data in an overlapped area are selected, layering is carried out according to the maximum value and the minimum value, random sampling is carried out, and the sampling number meets more than sixty percent of the total number. And finally, determining a normalization equation by the least square method according to the infrared data of the two sampled images. As shown in fig. 2.
The MODIS multi-temporal infrared radiation normalization process is as follows:
(1) and normalizing by using the reflectivity values of 1 and 2 of the visible light band and the near infrared band, setting a threshold value for cloud water detection, and removing cloud water pixels.
(2) And selecting reference infrared radiation data and to-be-normalized infrared radiation data to perform scatter regression, selecting infrared radiation data which meets more than sixty percent, and removing influence pixels.
(3) And determining a normalization equation of the infrared band by using a least square method by taking the unchanged pixel as a target.
(4) And carrying out regression operation on the new infrared image map by using a regression equation to obtain the normalized infrared image map.
Based on AVHRR sensor multi-temporal infrared radiation normalization, firstly, a reflectivity wave band is adopted to set a threshold value to remove part of pixels of cloud and water pixels, and all vegetation covering pixels are selected according to NDVI. And obtaining the surface temperature of the AVHRR infrared image data through a window splitting algorithm. According to the existence of a stronger linear relation among multi-temporal images, the method adopts typical correlation analysis to establish a normalization model. The typical correlation analysis was originally proposed by Hotelling, and the basic idea and principal components are very similar. Two image pictures with n channels x ═ x1……xn]And y ═ y1……yn]Two sets of linear combinations are formed, namely:
a1x1+a2x2+...+anxn＝aTx＝U
b1y1+b2y2+...+bnyn＝bTy＝V
wherein, t1And t2The temporal image can be expressed as x ═ x1,x2,x3...xn]T，y＝[y1,y2,y3...yn]T，a＝[a1,a2,a3...an]T，b＝[b1,b2,b3...bn]TThe correlation coefficient between the typical variables is found to be:
assuming that the correlation between two sets of image data is high, the following conditions are satisfied:
var(u,v)＝aT∑xya＝bT∑xyb
obtaining:
ρ＝aT∑xyb＝max
in order to solve the extreme value problem, a Lagrange multiplier is introduced, and the image X and Y typical variable difference MAD and the variance are calculated to obtain:
MADi＝Ui-Vii＝1,2,3……n，
var＝MADi＝var(ui,vi)＝2(1-ρi)
the typical variable data screening formula is as follows:
where t is a threshold. Assuming that the normalized values of the variance difference and the variance sum of the MAD satisfy the chi-square distribution.
The AVHRR surface temperature value normalization process is as follows:
(1) and normalizing by using the reflectivity values of 1 and 2 of the visible light band and the near infrared band, setting a threshold value for cloud water detection, and removing cloud water pixels to obtain vegetation pixels.
(2) Sixty percent of reference infrared image data and infrared image data to be normalized are selected for scatter regression, and typical variables of the two images are calculated.
(3) And selecting a sample point according to the threshold value t, and setting a rho value.
(4) And selecting sample points according to the threshold value, and performing regression operation on the new infrared image by using a least square method to obtain the normalized infrared radiation image.
Based on the infrared band characteristics of the VIRR sensor, the invention adopts a linear regression mode to carry out multi-temporal infrared radiation normalization, firstly, a reflectivity band is adopted to set a threshold value, part of cloud layer pixels and water body pixels are removed, then, a scatter diagram is formed by using a reference infrared image and the brightness temperature value of the infrared image to be normalized, principal component analysis is carried out, the slope of a principal axis is calculated, sixty percent of infrared radiation data are controlled and selected in an upward and downward range by taking the principal axis as a standard, random sampling is carried out through the maximum and minimum value layering, and a quadratic equation is adopted to determine a regression equation, as shown in figure 3.
The VIRR bright temperature value normalization process is as follows:
(1) and normalizing by using the reflectivity values of 1 and 2 of the visible light band and the near infrared band, setting a threshold value for cloud water detection, and removing cloud water pixels.
(2) Selecting reference infrared data and infrared data to be normalized to perform scatter regression, selecting an infrared data cluster center to perform principal component analysis, determining a slope, selecting sixty percent of infrared radiation data upwards and downwards, and removing influence pixels.
(3) And determining a normalization equation of the infrared band by using a unary linear equation with an unchanged pixel as a target.
(4) And carrying out regression operation on the new infrared image map by using a regression equation to obtain the normalized infrared image map.
Parameter determination
The brightness temperature is generally defined as the temperature corresponding to the radiant energy obtained by the satellite remote sensor, and can reflect the temperature of the ground object. The MODIS sensor uses the luminance temperature value for normalization. When temperature conversion is carried out through the Planck formula, the atmosphere has the effect of absorbing, screening and reducing infrared radiation, and meanwhile, the height of the terrain influences the infrared radiation. Parameters were determined by analysis with SPSS20.0, parameter a being atmospheric factor and parameter b being terrain. When a is-0.1393 and b is 343.17, the infrared radiation effect of the MODIS sensor is better.
The surface temperature refers to the temperature of the ground. After the heat energy of the sun is radiated to the ground, a part of the heat energy is reflected, a part of the heat energy is absorbed by the ground surface, the ground surface is heated, and the temperature obtained by measuring the ground surface temperature is the ground surface temperature. The AVHRR surface temperature has many factors including air temperature, humidity, surface state of ground object, illumination terrain and the like, and the factors influencing the surface temperature mainly include air temperature and humidity. And inputting the data into the SPSS for analysis to determine parameters, wherein the parameter a is air temperature, the parameter b is humidity, and the parameter c is illumination intensity. When a is 0.0049, b is 3.0694, and c is 778.67, the infrared radiation fitting effect of the AVHRR sensor is better.
Solar flare is one of the most severe outbreaks that occur in localized areas of the sun's atmosphere. It can release a large amount of energy in a short time, cause instantaneous heating of local areas, emit various electromagnetic radiations outwards, and accompany with the sudden increase of particle radiation. Solar flare has an influence on the infrared radiation band in the VIRR sensor, and is therefore determined as a parameter to which the VIRR infrared radiation is normalized. Meanwhile, in most of time, the wind cloud meteorological satellite can be covered by a large-area cloud layer, the cloud layer has high reflectivity, and the VIRR sensor multi-temporal infrared radiation normalization precision can be influenced by high temperature. According to the analysis of the infrared radiation characteristics, the parameter a is the solar flare reflectivity, and the parameter b is the cloud layer reflectivity. When a is 0.893 and b is 31.868, the best fitting effect of VIRR infrared radiation normalization can be achieved. The three sensors were summarized for solving for normalized parameters and analyzed using SPSS20.0 software, as shown in table 1:
TABLE 1 normalized fitting parameters
Table 1Normalized fitting parameters
Result accuracy test
In the MODIS sensor multi-temporal infrared radiation normalization, image brightness temperature data of No. 4/month 12 and No. 4/month 13 in 2017 are adopted for curve fitting, cloud water pixels are screened and removed, regression analysis is carried out through a least square method, a unitary one-time regression equation is established, and the result shows that two image data are in a linear correlation relationship, so that the MODIS multi-temporal infrared radiation normalization model used by the invention can meet the normalization requirement and is closer to the color. The fitting effect is substantially close to the reference image map, as shown in fig. 4.
In the multi-temporal infrared radiation normalization of the AVHRR sensor, curve fitting is carried out by adopting surface temperature data of No. 4 and 12 images and surface temperature data of No. 4 and 13 images in 2017, cloud water pixels are removed by screening, and regression analysis is carried out by a least square method, so that a binomial relation is found to be formed between the two image data. Air temperature has a major cause of atmospheric radiation and absorption in the hot infrared tunnel, with surface temperature increasing with increasing air temperature. The humidity and the surface temperature are in a negative correlation relationship within a certain time range, namely, the surface temperature is gradually reduced when the humidity is gradually increased. When the ground is irradiated by the sun, the light energy is accumulated and converted into heat energy to be stored by the ground, and the ground temperature is gradually increased along with the increase of the illumination intensity, namely the illumination intensity and the ground temperature are in a linear positive correlation relationship. The fitted regression curve is shown in fig. 5.
In the multi-temporal radiation normalization of the VIRR sensor, the brightness temperature data extracted from the No. 4/12 image in 2017 and the No. 4/13 image in 2017 are adopted for fitting, cloud water pixels are firstly screened and removed, a unitary quadratic equation is adopted for regression analysis, and the result shows that the two image data are in a linear correlation relationship, so that the VIRR multi-temporal radiation normalization model established by the invention can meet the normalization requirement and is closer to the color. The fitting effect is substantially close to the reference image map, as shown in fig. 6.
In the aspect of radiation normalization, the unitary linear equation adopted by the invention can basically approach a reference image, the radiation precision can be ensured under the condition of eliminating cloud water influence factors, the large change of the temperature of the ground object is met, the radiation normalization error is equally divided, and the multi-temporal infrared radiation normalization of the VIRR sensor is realized. In the analysis of the normalized result, principal components are used for analysis, the purpose of the principal component analysis is to represent the characteristics of the whole sample by using fewer characteristic numbers, the slope and the intercept are obtained through the principal component analysis, and the slope and the intercept are compared with the data before normalization to obtain the radiation normalization coefficient. According to the prior experience, aiming at the result precision of the normalized inversion of the three sensors, the method adopts a decision coefficient R2And mean square error, RMSE, to evaluate the normalized model accuracy, the formula is as follows:
wherein, y0For infrared data after radiation normalization, y1Is original infrared data, and n is the number of pixels. The smaller the RMSE value, the better the fit. The larger the RMSE value, the less effective the fit. Table 2 shows the radiation normalized accuracy test for different sensors.
TABLE 2 radiation normalization accuracy test of different sensors
Table2 Radiation normalized accuracy test for different sensors
As shown in the table 2, the result precision analysis shows that after the radiation normalization, the radiation normalization fitting results of the three sensors are inconsistent, and the ranking is carried out according to the normalization fitting effect with the slope close to 1, so that the radiation normalization fitting effect of the MODIS sensor is superior to that of the VIRR sensor, and the radiation normalization fitting effect of the VIRR sensor is superior to that of the AVHRR sensor.
Forest fire hot spot threshold determination after multi-temporal infrared radiation normalization of different sensors
By carrying out multi-temporal infrared radiation normalization on infrared image data of the MODIS, AVHRR and VIRR sensors, atmospheric errors of unchanged brightness temperature in the infrared images are equally divided, background brightness temperature values are normalized, fire point high temperature pixels are highlighted, influences of factors such as atmosphere, topography and temperature on the temperatures of the MODIS, AVHRR and VIRR sensors for monitoring the ground objects are eliminated, and the accuracy of monitoring the temperatures of the MODIS, AVHRR and VIRR sensors for monitoring the ground objects is improved. And judging forest fire hot spots by setting a threshold range by utilizing the normalized infrared image map. Based on climate and geographical background of Hunan province, the forest fire hot spot discrimination of MODIS, AVHRR and VIRR sensors is carried out by adopting a bright temperature value of an infrared band of 4 microns and a bright temperature difference value between the infrared bands of 4 microns and 11 microns:
(1) the method is characterized in that the brightness temperature value of the 4-micron infrared band of the MODIS sensor is normalized by multi-time-phase infrared radiation, when the brightness temperature value of the 4-micron infrared band is smaller than 317K, the brightness temperature value below 317K is in an unsaturated state, which shows that the fire point intensity is small, the possibility that the pixel is a high-temperature hot spot is excluded, and the judgment formula is as follows:
T4μm＜317K
when the brightness temperature value of the 4-micron infrared band of the MODIS sensor is greater than or equal to 317K, the requirement of a high-temperature point is met, namely the brightness temperature value above 317K is in a saturated state, the fire point intensity is high, the judgment requirement of a high-temperature hot spot is met, the difference value between the brightness temperature value of the 4-micron infrared band and the brightness temperature of the 11-micron infrared band is calculated, when the difference value is greater than or equal to 20.9K, the pixel is judged to be a suspected forest fire hot spot, and the judgment formula is as follows:
T4μm≥317K
T4μm-T11μm≥20.9K
(2) when the brightness temperature value of the 4-micrometer infrared band of the AVHRR sensor is greater than or equal to 317K, the requirement of a high-temperature point is met, namely the brightness temperature value above 317K is in a saturated state, the fire point intensity is high, the judgment requirement of the high-temperature hot spot is met, the difference value between the background brightness temperature values of the 4-micrometer infrared band and the 11-micrometer infrared band is calculated, when the difference value is greater than or equal to 21.85K, the pixel is judged to be a suspected forest fire hot spot, and the judgment formula is as follows:
T4μm≥317K
T4μm-T11μm≥21.85K
(3) the method is characterized in that the brightness temperature value of the 4-micron infrared band of the VIRR sensor is normalized through multi-time-phase infrared radiation, when the brightness temperature value of the 4-micron infrared band is smaller than 310K, namely the brightness temperature value below 310K is in an unsaturated state, the fire point intensity is low, the possibility that the pixel is a high-temperature hot spot is eliminated, and the judgment formula is as follows:
T4μm＜310K
when the brightness temperature value of the 4-micron infrared band of the VIRR sensor is greater than or equal to 310K, the requirement of a high-temperature point is met, namely the brightness temperature value of more than 310K is in a saturated state, the fire point intensity is high, the judgment requirement of the high-temperature hot spot is met, the difference value of the background brightness temperature values of the 4-micron infrared band and the 11-micron infrared band is calculated, when the difference value is greater than or equal to 19.67K, the pixel is judged to be a suspected forest fire hot spot, and the judgment formula is as follows:
T4μm≥310K
T4μm-T11μm≥19.67K
multi-temporal infrared radiation normalization method verification of different sensors
According to the method, MODIS of 13:55:45 in 4/1/2017, AVHRR of 15:47:20 and VIRR of 15:04:32 in clear air and little cloud image data are adopted, three multi-temporal infrared radiation normalization models constructed by the method are applied to normalize infrared radiation image data, and after normalization, a multi-temporal image forest fire hot spot threshold model constructed by the method is adopted to extract forest fire hot spots. As shown in fig. 7, 8, and 9, respectively:
the invention carries out research and discussion on the aspects and carries out deep analysis and discussion on the problem of infrared radiation normalization of remote sensing data of different sensors with medium and low resolution.
According to the method, when a multi-temporal infrared radiation normalization model is established, infrared radiation normalization parameters are determined, and an MODIS, AVHRR and VIRR sensor infrared radiation normalization model is established by combining a mathematical model and a sampling method, atmospheric radiation errors are equally divided by applying the normalization model, and radiation differences from the same sensor in different time phases of an infrared band are reduced.
By the normalization method, the problem of radiation difference of different time phases of infrared bands of the MODIS, AVHRR and VIRR sensors is solved, the radiation standard is established, and infrared radiation normalization models of the MODIS, AVHRR and VIRR sensors are constructed. The infrared radiation normalization model established by the invention can better eliminate the influence of radiation difference, make up for the time difference of the satellite sensors and improve the accuracy of monitoring the ground object change by the infrared radiation of the MODIS, AVHRR and VIRR sensors.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (3)
1. A multi-temporal infrared radiation normalization method for different sensors applied to forest fire hot spot discrimination is characterized by comprising the following steps:
eliminating cloud influence pixels;
calculating an NDVI value, and extracting vegetation area;
carrying out radiation normalization on infrared data of different sensors;
wherein the different sensors include MODIS, AVHRR, and VIRR sensors;
performing radiation normalization on the infrared data of different sensors comprises determining infrared radiation normalization parameters of different sensors;
after infrared radiation normalization parameters of different sensors are obtained, normalization is carried out on infrared data of the different sensors to obtain a new infrared radiation normalization image map;
respectively establishing multi-temporal infrared radiation normalization models based on different sensors by curve fitting and regression analysis of infrared image data referenced by the different sensors and infrared image data to be normalized;
carrying out precision analysis on the multi-temporal infrared radiation normalization models of different sensors;
the precision analysis adopts a decision coefficient and a mean square error (RMSE) to evaluate the precision of the multi-temporal infrared radiation normalization model; the MODIS sensor is normalized by adopting a brightness temperature value and can reflect the temperature condition of the ground objects;
when temperature conversion is carried out through a Planck formula, the atmosphere has the effect of absorbing and attenuating infrared radiation, and meanwhile, the height of the terrain influences the infrared radiation; analyzing and determining parameters through SPSS, wherein the parameter a is an atmospheric factor, the parameter b is a terrain, and a multi-temporal infrared radiation normalization model of the MODIS sensor is as follows:
y=ax+b
wherein y is the value after normalization and x is the value before normalization; a = -0.1393, b =343.17, and the multi-temporal infrared radiation normalization model of the MODIS sensor is:
y = -0.1393x + 343.17; the factors influencing the surface temperature of the AVHRR sensor mainly comprise air temperature and humidity; analyzing and determining parameters through the SPSS, wherein the parameter a is air temperature, the parameter b is humidity, the parameter c is illumination intensity, and a multi-temporal infrared radiation normalization model of the AVHRR sensor is as follows:
y=ax2-bx+c
wherein y is the value after normalization and x is the value before normalization; when a =0.0049, b =3.0694, and c =778.67, the AVHRR sensor infrared radiation fitting curve is closer, and the multi-temporal infrared radiation normalization model of the AVHRR sensor is:
y=0.0049x2-3.0694x+778.67。
2. the method according to claim 1, characterized in that the factor affecting the infrared radiation band in the VIRR sensor is mainly solar flare and is therefore determined as a parameter normalizing the infrared radiation of the VIRR sensor; according to the analysis of the infrared radiation characteristics, the parameter a is determined as the solar flare reflectivity through the SPSS analysis, and the parameter b is determined as the cloud layer reflectivity; the multi-temporal infrared radiation normalization model of the VIRR sensor is as follows:
y=ax+b
where y is the normalized value and x is the value before normalization.
3. The method of claim 2, wherein when a =0.9231, b =22.525, the final fitting effect of the infrared radiation normalization of the VIRR sensor is achieved; the multi-temporal infrared radiation normalization model of the VIRR sensor is as follows: y =0.9231x + 22.525.
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