CN114296061B - Cross calibration method based on multivariate variable detection and different radiation transmission models - Google Patents
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Abstract
The invention discloses a cross calibration method based on multiple variable detection and different radiation transmission models, relates to the technical field of remote sensing application, solves the problems that the prior art uses default modes and experience parameters for the atmospheric modes, the ground surface BRDF characteristics and the like, does not consider influences caused by the change of ground objects at different observation times and uncertainties caused by different radiation transmission models, has larger irrational and uncertainties and the like, acquires real atmospheric profiles, ground surface BRDF and invariable ground objects, considers influences caused by various radiation transmission models, improves the cross calibration for the traditional use of the experience atmospheric modes, does not consider the BRDF characteristics, uses a single radiation transmission model and does not consider the change of ground objects, acquires the parameters of the real atmospheric profiles, BRDF and the like, extracts the invariable ground objects in an experimental area according to a multiple variable detection method, combines MODTRA and 6S radiation transmission models to calculate spectrum matching factors, and further realizes the calculation of cross calibration more scientifically.
Description
Technical Field
The invention relates to the technical field of remote sensing application, in particular to a cross calibration method based on multivariate variable detection and different radiation transmission models.
Background
With the increasing quantization of remote sensing applications, the comprehensive application of multi-time phase and multi-sensor data and the development of quantitative remote sensing technology, high-precision remote sensing sensor radiometric calibration is urgently needed, and the remote sensing sensor radiometric calibration is the basis of high-precision quantitative remote sensing. The remote sensing image can convert an original DN (image numerical value) value into an equivalent energy value at the entrance pupil of the sensor only through radiometric calibration, and remote sensing inversion products such as vegetation, water body, atmosphere and the like are obtained through atmospheric correction and some inversion algorithms, so that large-area multi-time-domain remote sensing image information is provided for the fields such as national defense, agriculture, forestry, disaster protection and the like. The implementation of radiometric calibration depends on conditions such as spectrum of a calibration field, atmospheric parameters and the like when satellites pass through the ground, and strict evaluation and analysis of BRDF (surface bidirectional reflectance distribution function Bidirectional Reflectance Distribution Function) characteristics of the calibration field are also required. The radiation calibration method of the visible near infrared band can be divided into laboratory calibration before emission, on-orbit satellite radiation calibration, on-orbit alternative radiation calibration, cross calibration and the like according to the running state of the sensor.
The sensor needs to be calibrated in a laboratory before the sensor is lifted off, and the radiation performance of the sensor changes due to the fact that the sensor is subjected to vibration and attenuation of different degrees caused by the influence of surrounding environment after the sensor is lifted off, so that the result error calculated through calibration parameters in the laboratory is large, and radiation performance monitoring is needed to be carried out on the sensor in each quarter so as to correct calibration parameters of the sensor to ensure the quantitative precision of remote sensing images. The radiation calibration method which is commonly used at present is field radiation calibration, however, the calibration method consumes higher manpower and material resources, is also influenced by weather when the satellite passes the border, and cannot monitor the radiation performance for a long time. The cross radiometric calibration is to utilize the calibrated reference sensor to realize the calibration of the sensor to be calibrated through the conversion of the radiance, and the cross radiometric calibration is lower in cost, so that the calibration of historical data can be realized and becomes a research hot spot gradually. The invention integrates the experimental cross calibration of the advantages and disadvantages of the two commonly used radiation transmission models at present on the basis of the atmospheric profile, the earth surface multi-angle reflection characteristic, the unchanged ground object points and the like, and is more in line with the actual situation.
Current calculations regarding cross scaling mainly use spectral matching factors to correct for differences in different sensors. The calculation of the spectrum matching factor is to input the observation geometry, the atmospheric condition, the surface parameters and the like of the reference sensor and the sensor to be calibrated into a radiation transmission model to obtain the ratio of the top radiance of the atmosphere layer. Default modes and empirical parameters are used in the radiation transmission model with respect to the atmospheric mode, the surface BRDF characteristics and the like, and influence caused by the change of ground features at different observation times and uncertainty caused by different radiation transmission models are not considered, and the methods have large irrational and uncertainty, so that a cross calibration method based on multiple variable detection and different radiation transmission models is needed.
Aiming at the traditional use of an empirical atmosphere mode, no consideration of BRDF characteristics, the use of a single radiation transmission model and no consideration of feature changes, the invention improves the realization of cross calibration, acquires the parameters of a real atmosphere profile, BRDF and the like, extracts the unchanged features in an experimental area according to a multi-element variable detection method, and simultaneously combines the MODTRA and 6S radiation transmission models to calculate the spectrum matching factors, thereby realizing the calculation of cross calibration more scientifically.
Disclosure of Invention
The invention provides a cross calibration method based on multi-element variable detection and different radiation transmission models, which aims at solving the problems that in the prior art, default modes and experience parameters are used for the characteristics of an atmospheric mode, the BRDF (binary representation function) of the earth surface and the like, influence caused by the change of the earth surface at different observation times and uncertainty caused by different radiation transmission models are not considered, and the like, and has larger irrational degree and uncertainty.
The cross calibration method based on the multivariate variable detection and different radiation transmission models is realized by the following steps:
step one, acquiring atmospheric profile data, atmospheric aerosol data and BRDF model parameters;
step two, respectively inputting the observation geometry, water vapor and spectral response functions of different sensors and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters acquired in the step one to a MODRan and a 6S radiation transmission model and calculating the radiance of different sensors; the specific process is as follows:
step two, inputting the observation geometry, the vapor, the spectral response function and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters obtained in the step one into a 6S radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated;
secondly, inputting the observation geometry, the vapor, the spectral response function and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters obtained in the first step into a MODTRA radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated;
step three, calculating a spectrum matching factor;
step three, selecting an original image to obtain a reference sensor radiance image and a corresponding surface reflectivity image;
step three, the earth surface reflectivity is used as the real reflectivity to be respectively input into an MODTRA radiation transmission model and a 6S radiation transmission model, and the radiance corresponding to the real reflectivity is simulated; and carrying out regression calculation by taking the radiance of the reference sensor as the radiance corresponding to the real radiance and the real reflectivity, wherein the radiance is as follows:
Rad true ＝Rad 6S *a+Rad modtran *b+c
in Rad true For the final calculated radiance, rad 6S And Rad (Rad) modtran The radiance is simulated by a 6S radiation transmission model and an MODTRA radiation transmission model respectively, and a, b and c are coefficients calculated by regression respectively;
thirdly, inputting the radiance of the reference sensor and the to-be-calibrated sensor simulated by the 6S radiation transmission model in the first step and the radiance of the reference sensor and the to-be-calibrated sensor simulated by the MODTRA radiation transmission model in the second step into the regression calculation formula to respectively obtain the final radiance of the reference sensor and the final radiance of the to-be-calibrated sensor;
step three, the final radiance of the sensor to be calibrated obtained in the step three is compared with the final radiance of the reference sensor, and a spectrum matching factor is obtained;
extracting the unchanged ground object of the experimental region by utilizing a multi-variable detection method;
masking the reference sensor radiance image to obtain the radiance of the reference image unchanged ground object;
resampling the image to be calibrated by a bilinear interpolation method, and linearly fitting the resampled image with the radiance of the invariant feature obtained in the fifth step to obtain a correction coefficient; the specific process is as follows:
step six, multiplying the image radiance of the reference sensor by the spectrum matching factor obtained in the step three to obtain the image radiance of the sensor to be calibrated;
resampling the original image of the sensor to be calibrated to the resolution of the original image of the reference sensor by adopting a bilinear interpolation method, and extracting unchanged ground object pixels;
and step six, linearly fitting the original image of the sensor to be calibrated with the unchanged ground object radiance and the gray level DN, and calculating to obtain the gain and bias of the calibration coefficient of the sensor to be calibrated.
The invention has the beneficial effects that: the cross scaling calculation method is an improvement on the conventional cross scaling calculation method. The invention obtains the real atmospheric profile, the ground surface BRDF and the unchanged ground object, considers the influence caused by various radiation transmission models, improves the traditional use experience atmospheric mode, does not consider the BRDF characteristics, uses a single radiation transmission model and does not consider the ground object change to realize cross calibration, obtains the real atmospheric profile, the BRDF and other parameters, extracts the unchanged ground object in an experimental area according to a multiple variable detection method, and combines the MODTRA and the 6S radiation transmission model to calculate the spectrum matching factor, thereby realizing the calculation of the cross calibration more scientifically.
Drawings
FIG. 1 is a schematic block diagram of a cross-scaling method based on multivariate variable detection and different radiation transmission models according to the present invention;
FIG. 2 is a view of the effect of radiation correction field imaging;
FIG. 3 is a graph of NCEP atmospheric broadline data effects;
FIG. 4 is a graph showing the spectral response function effects of a reference sensor and a sensor to be calibrated;
FIG. 5 is a flow chart of a regression experiment of different radiation transmission models;
FIG. 6 is a graph showing the effect of Dunhuang correction field Landsat8 reflectivity and corresponding radiance;
FIG. 7 is a graph showing the effect of the simulation of radiance results and the comparison of real radiance results for MODTRA and 6S models;
FIG. 8 is a graph of invariant feature effects obtained from a multiple variable detection method;
fig. 9 is a graph of the cross-scaled final fit obtained using the method of the present invention.
Detailed Description
The present embodiment will be described with reference to fig. 1 to 9, which are based on a cross scaling method of multivariate variable detection and different radiation transmission models: the method comprises the following steps:
A. acquiring NCEP (national environmental forecast center National Centers forEnvironmental Prediction) atmospheric profile data, MODIS (medium resolution imaging spectrometer-resolution Imaging Spectroradiometer) atmospheric aerosol data and BRDF model parameters;
in the step, firstly, the temperature and the pressure of 17 atmospheres are obtained through NCEP atmospheric profile data, and the elevation is calculated;
wherein H0 is the altitude of a measuring station, hs is the altitude of a standard isobaric surface, P0 is the ground air pressure, ps is the average air column height, R, g are constants, the NCEP re-analysis data set has 144 x 73 grid points, and the data range covers a global isobaric surface layer (total 17 layers), so that the air pressure and temperature values of each layer in four time periods are read according to longitude and latitude information of appointed time when a file is read, and finally linear interpolation is carried out according to observation time to obtain a final result;
aerosol optical thickness (AOD) and moisture were then obtained by MODIS aerosol data. The MCD19A 2V 6 data product can provide daily atmospheric parameters with resolution of 1 km, and a buffer zone of 1 km can be established to obtain average atmospheric parameters if no data exists in an experimental area;
finally, obtaining BRDF parameters of the experimental area through MODIS bidirectional reflectance distribution function model parameters, wherein the data is selected from a Ross-Li nuclear drive model;
wherein:
tanθ v’ ＝P 5 tanθ v (5)
tanθ s’ ＝P 5 tanθ s (6) Middle theta v’ θ s’ The solar zenith angle and the relative azimuth angle are respectively observed; θ v’ ,θ s’ And t is an intermediate variable, P 4 ＝h/b＝2，P 5 ＝b/r＝1,P 1 ,P 2 ,P 3 Respectively, isotropy, geometric parameters and volume parameters can be obtained in the MODIS image.
B. The observation geometry, the atmospheric profile data, the AOD (aerosol optical thickness), the water vapor, BRDF parameters and the spectral response function (the observation geometry is the state of the sensor when shooting images, such as the azimuth and the angular posture of the sensor, etc. when the sensor shoots, the atmospheric parameters are weather conditions, how thick the cloud is, how moist the weather is, etc. when the sensor shoots; the specific process is as follows:
b1: the observation geometry, the atmospheric parameters, the spectral response function, the BRDF parameters and the like of the reference sensor and the sensor to be calibrated are input into a 6S radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated
B2: the observation geometry, the atmospheric parameters, the spectral response function, the BRDF parameters and the like of the reference sensor and the sensor to be calibrated are input into a MODTRA radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated
Step C, calculating a spectrum matching factor;
in the past, a single radiation transmission model is often used in the end of the cross calibration experiment, and the results of different radiation transmission models have large differences and the emphasis of simulation calculation is different. The simulation result of the 6S radiation transmission model in the visible near infrared band is good, but the 6S radiation transmission model has the defects of fewer input parameters and shorter spectrum range; the MODTRA radiation transmission model can simulate and calculate the results of all wavelengths, but the input parameters are more, and the operation is more complicated. Under the condition of the same parameters, the simulation results of the two models have a little deviation, in order to reduce the difference between different radiation transmission models, in the embodiment, the land surface reflectivity image corresponding to the land surface brightness image of the Dunhuang correction field reference sensor Landsat8 is selected, and the land surface reflectivity of Landsat8 is obtained by inputting the original image into LEDAPS software for atmospheric correction so as to realize the conversion from the top brightness of the atmosphere layer to the land surface reflectivity. LEDAPS software requires the input of various atmospheric parameters to construct an atmospheric model, including mainly DEM, output of the 6S radiation transmission model, ozone, ground pressure, temperature and water vapor simulated by the national environmental prediction center (NCEP), etc., which data can be downloaded in Google Earth Engine.
As shown in fig. 6, in this embodiment, the surface reflectivity and the radiance points of different areas of the dunhuang correction field are extracted as experimental data, the surface reflectivity is used as a known real reflectivity and is respectively input into the MODTRAN and the 6S radiation transmission model to obtain corresponding radiance, and the Landsat8 radiance is used as a real radiance and is subjected to regression calculation with the radiance obtained by calculation of the two models, so that the following formula can be obtained:
Rad true ＝Rad 6S *a+Rad modtran *b+c (7)
in Rad true For the final calculated radiance, rad 6S And Rad (Rad) modtran The radiance simulated by 6S and MODTRA respectively, and a, b and c are coefficients calculated by regression respectively.
And finally, inputting the results calculated by different radiation transmission models of the reference sensor and the sensor to be calibrated in the step B into the formula to obtain a final radiance result, and carrying out a ratio of the final radiance of the sensor to be calibrated to the final radiance of the reference sensor to obtain a spectrum matching factor. The method combines the simulation results of the two radiation transmission models, optimizes the experimental flow of calculating the spectrum matching factors in the cross calibration experiment, and further reduces the uncertainty brought by the radiation transmission models;
D. extracting the invariable stable ground object of the experimental area by utilizing a multi-variable detection method;
d1: acquiring two sensor images to be calibrated in different time phases in an experimental area;
d2: extracting unchanged ground objects of each wave band of the reference sensor image in the experimental area according to a multivariate variable detection Method (MAD):
wherein F= [ F ] 1 +F 2 +…+F n ]Sum G= [ G ] 1 +G 2 +…+G n ]Respectively representing two groups of images in different time, n is the number of image wave bands, a T And b T Are constants, minimizing the positive correlation between U and V by linear combination between F and G:
Var(U-V)＝Var(U)+Var(v)-2Cov(U,V)→maximum (9)
where Var is the variance and Cov is the covariance, maximum represents the maximum value U and V satisfying the following constraints, respectively:
from the above equation, it can be further derived that:
wherein ρ is a typical correlation coefficient between U and V, and then subtracting the two sets of linear combinations
From the above we can obtain (U) i -V i ) (U) i -V i ) Variance of (2)The combination is obtained according to the following formula:
Z (j,k) u representing the jth row and kth column of an image i -V i And U i -V i The square sum of the variance ratio can be considered that Z of all pixels in a single band satisfies the chi-square distribution with the degree of freedom of n, and the chi-square distribution with the degree of freedom of n is shown in Z (j,k) The distribution function value at a point can be defined asThe invariant probability for each pixel is expressed as:
finallyWill P r The pixels with (no change) > t are regarded as unchanged object points, wherein t is a set threshold value.
In this embodiment, to ensure that the invariant feature extracted by the multi-variable detection can be used for cross calibration, the threshold t is set to be 0.85, 0.86, … …, 0.94 and 0.95, the number of the invariant feature points required to be selected is more than 10% of the total number of pixels, the correlation coefficient of the pixel values between the invariant feature points of the two-view reference sensor image in different time phases is maximum, and finally the threshold t is set to be 0.93.
E. Masking the reference sensor radiance image to obtain the radiance of the invariant ground feature;
F. resampling the image to be calibrated by a bilinear interpolation method, and performing linear fitting with the radiance of the object point of the reference sensor unchanged to obtain a correction coefficient.
F1: multiplying the radiance of the reference image by a spectrum matching factor to obtain the radiance of the sensor image to be calibrated;
f2: resampling the image of the sensor to be calibrated to the resolution of the image of the reference sensor by using a bilinear interpolation method, and extracting unchanged ground object pixels;
f3: and performing linear fitting calculation on the invariant ground object radiance of the image of the sensor to be calibrated and the DN value to obtain the gain and bias of the calibration coefficient of the sensor to be calibrated.
A second embodiment is described with reference to fig. 1 to 6, which are examples of the cross scaling method based on the multivariate variable detection and different radiation transmission models according to the first embodiment.
In the embodiment, the GF1-B image of 7 months and 17 days in 2018 is taken as an image to be calibrated, the Landsat-8 image of 7 months and 15 days in 2018 is taken as a reference image, and the Dunhuang correction field is taken as an experimental area for cross calibration. According to the method flow chart shown in fig. 1, the method comprises the steps of:
1. the remote sensing image of Dunhuang correction site is obtained as shown in fig. 2; acquiring the atmospheric pressure and the temperature of the Dunhuang correction site through NCEP atmospheric profile data and calculating according to formula (1) to obtain a corresponding elevation, as shown in figure 3; AOD data for the Dunhuang correction field sky were obtained from MODIS AOD images with AOD results of 0.255 and 0.282 for 7 month 17 day and 7 month 15 day, respectively.
2. The BRDF parameters of Dunhuang correction sites are obtained by using the MODIS BRDF coefficient images of the formulas (2) - (6).
3. The observation geometry, the atmospheric parameters, the spectral response function, the BRDF parameters and the like of the reference sensor and the sensor to be calibrated are input into a 6S radiation transmission model and a MODTRA radiation transmission model, and the radiance of the reference sensor and the sensor to be calibrated is obtained according to the formula (7)
4. Obtaining a reference sensor radiance image and a corresponding surface reflectivity image, respectively inputting the surface reflectivity as a real reflectivity into a radiation transmission model A and a radiation transmission model B, and simulating the radiance corresponding to the real reflectivity; and carrying out regression calculation by taking the radiance of the reference sensor as the radiance corresponding to the real radiance and the real reflectivity, wherein the radiance is as follows:
Rad true ＝Rad 6S * a +Rad modtran *b+c
in Rad true For the final calculated radiance, rad 6S And Rad (Rad) modtran The radiance is simulated by a 6S radiation transmission model and an MODTRA radiation transmission model respectively, and a, b and c are coefficients calculated by regression respectively;
5. and finally, inputting the results calculated by different radiation transmission models of the reference sensor and the sensor to be calibrated into the formula to obtain a final radiance result, and carrying out a ratio of the final radiance of the sensor to be calibrated to the final radiance of the reference sensor to obtain a spectrum matching factor. The spectral scaling factors of the blue, green, red and near infrared bands are respectively: 0.999, 1.014, 1.036, 1.658.
6. According to the formula (8) - (14) and the two GF1-BCD images in different time, the invariant ground feature images of Dunhuang correction sites are obtained, and most of the ground features, vegetation water and the like are masked by the multivariate variable detection method shown in the figure 8, and the obtained invariant ground features are stable ground features such as sandy lands and the like.
7. Masking the reference sensor radiance image to obtain the radiance of the invariant ground feature; .
8. And multiplying the radiance of the reference image by a spectrum matching factor to obtain the radiance of the sensor image to be calibrated, resampling the sensor image to be calibrated to the resolution of the reference sensor image by using a bilinear interpolation method, and extracting unchanged ground object pixels.
9. The gain and bias of the scaling coefficient of the sensor to be scaled can be calculated by linear fitting of the invariant ground object radiance of the image of the sensor to be scaled and the DN value, and the result is shown in FIG. 9.
10. From the calculation result, R of each band can be seen 2 All of which are more than 0.95 percent,
the experimental procedure is shown in FIGS. 6 and 7, where the regression coefficient and correlation R for each band 2 The following table shows:
the fact that the blue wave band is greatly influenced by atmospheric scattering, so that the fitting effect is relatively poor, and the fact that the fitting result difference of other wave bands is small indicates that the cross calibration method improves certain cross precision compared with the traditional cross calibration method.
Claims (2)
1. The cross calibration method based on the multivariate variable detection and different radiation transmission models is characterized by comprising the following steps of: the method is realized by the following steps:
step one, acquiring atmospheric profile data, atmospheric aerosol data and BRDF model parameters;
step two, respectively inputting the observation geometry, the water vapor and the spectral response function of different sensors and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters acquired in the step one into MODTRA and 6S radiation transmission models, and calculating the radiance of different sensors; the specific process is as follows:
step two, inputting the observation geometry, the vapor, the spectral response function and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters obtained in the step one into a 6S radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated;
secondly, inputting the observation geometry, the vapor, the spectral response function and the atmospheric profile data, the atmospheric aerosol data and the BRDF parameters obtained in the first step into a MODTRA radiation transmission model to obtain the radiance of the reference sensor and the sensor to be calibrated;
step three, calculating a spectrum matching factor;
step three, obtaining a reference sensor radiance image and a corresponding surface reflectivity image;
step three, the earth surface reflectivity is used as the real reflectivity to be respectively input into an MODTRA radiation transmission model and a 6S radiation transmission model, and the radiance corresponding to the real reflectivity is simulated; and carrying out regression calculation by taking the radiance of the reference sensor as the radiance corresponding to the real radiance and the real reflectivity, wherein the radiance is as follows:
Rad true ＝Rad 6S *a+Rad modtran *b+c
in Rad true For the final calculated radiance, rad 6S And Rad (Rad) modtran The radiance is simulated by a 6S radiation transmission model and an MODTRA radiation transmission model respectively, and a, b and c are coefficients calculated by regression respectively;
thirdly, inputting the radiance of the reference sensor and the to-be-calibrated sensor simulated by the 6S radiation transmission model in the first step and the radiance of the reference sensor and the to-be-calibrated sensor simulated by the MODTRA radiation transmission model in the second step into the regression calculation formula to respectively obtain the final radiance of the reference sensor and the final radiance of the to-be-calibrated sensor;
step three, the final radiance of the sensor to be calibrated obtained in the step three is compared with the final radiance of the reference sensor, and a spectrum matching factor is obtained;
extracting the unchanged ground object of the experimental region by utilizing a multi-variable detection method;
step five, masking the radiation brightness of the image of the reference sensor to obtain the radiation brightness of the invariant feature of the reference image;
resampling the image to be calibrated by a bilinear interpolation method, and linearly fitting the resampled image with the radiance of the invariant feature obtained in the fifth step to obtain a correction coefficient; the specific process is as follows:
step six, multiplying the image radiance of the reference sensor by the spectrum matching factor obtained in the step three to obtain the image radiance of the sensor to be calibrated;
resampling the original image of the sensor to be calibrated to the resolution of the original image of the reference sensor by adopting a bilinear interpolation method, and extracting unchanged ground object pixels;
and step six, linearly fitting the original image invariant ground object radiance of the to-be-calibrated sensor with DN value, and calculating to obtain the gain and bias of the calibration coefficient of the to-be-calibrated sensor.
2. The method of cross-scaling based on multivariate detection and different radiation transmission models of claim 1, wherein: the specific process of the fourth step is as follows:
step four, acquiring two groups of sensor images to be calibrated in different time periods in an experimental area;
step four, extracting unchanged ground objects of each wave band of the reference sensor image in the experimental area according to a multivariate variable detection method:
wherein F= [ F ] 1 +F 2 +…+F n ]Sum G= [ G ] 1 +G 2 +…+G n ]Respectively representing two groups of images in different time, n is the number of image wave bands, a T And b T Are all constant, minimizing the positive correlation between U and V by linear combination between F and G, then:
Var(U-V)＝Var(U)+Var(v)-2Cov(U，V)→maximum
wherein Var is variance, cov is covariance, and maximum represents the maximum value; u and V satisfy the following restrictions respectively:
from the above equation, it can be further derived that:
where ρ is a typical correlation coefficient between U and V, and then subtracting the two sets of linear combinations
Obtaining (U) i -V i ) (U) i -V i ) Variance of (2)The combination is obtained according to the following formula:
Z (j，k) u representing the jth row and kth column of a reference sensor image i -V i And U i -V i The square sum of the variance ratio can be considered that Z of all pixels in a single band satisfies the chi-square distribution with the degree of freedom of n, and the chi-square distribution with the degree of freedom of n is shown in Z (j，k) The distribution function value at a point can be defined asThe invariant probability for each pixel is expressed as:
finally P is arranged r The pixels with (no change) > t are regarded as unchanged object points, wherein t is a set threshold value.
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| CN102279393A (en) * | 2011-07-15 | 2011-12-14 | 北京航空航天大学 | Cross radiometric calibration method of hyper-spectral sensor based on multi-spectral sensor |
| CN104483663A (en) * | 2014-12-25 | 2015-04-01 | 武汉大学 | Atmospheric correction method and system for hyperspectral remote sensing image |
| CN111123382A (en) * | 2019-12-25 | 2020-05-08 | 中国科学院遥感与数字地球研究所 | Aerosol and surface parameter joint inversion method |
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| CN102279393A (en) * | 2011-07-15 | 2011-12-14 | 北京航空航天大学 | Cross radiometric calibration method of hyper-spectral sensor based on multi-spectral sensor |
| CN104483663A (en) * | 2014-12-25 | 2015-04-01 | 武汉大学 | Atmospheric correction method and system for hyperspectral remote sensing image |
| CN111123382A (en) * | 2019-12-25 | 2020-05-08 | 中国科学院遥感与数字地球研究所 | Aerosol and surface parameter joint inversion method |
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| Spectral and radiometric calibrations for mapping satellite-1 camera;Wan Zhi et al.;Optics and Precision Engineering;20150731;全文 * |
| 传感器交叉辐射定标综述;高彩霞;姜小光;马灵玲;霍红元;;干旱区地理;20130115(01);全文 * |
| 积分球光源分布温度对宽波段光学遥感器绝对辐射定标的影响及其校正;万志 等;发光学报;20121231;第33卷(第08期);全文 * |
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