A hyperspectral remote sensor cross-calibration method based on spectral matching factor

Abstract

The present application relates to a kind of quantitative methods of spectral imager, specifically, a kind of high spectral remote sensor cross calibration method based on spectral matching factor, existing high spectral remote sensor only relies on single spectral channel in cross calibration process, leading to the technical problem of poor cross calibration precision.This method includes the following steps:1】Obtain spectral channel characteristics;2】Calculate simulated spectral radiance;3】Calculate spectral matching factor;4】Obtain cross calibration test area;5】Calculate the real spectral radiance of each spectral channel of reference remote sensor B;6】Obtain weight coefficient;7】Obtain final spectral matching factor;8】Calculate the real spectral radiance of each spectral channel of to-be-calibrated remote sensor A;9】Calculate the radiation calibration coefficient of each spectral channel of to-be-calibrated remote sensor A, complete the cross calibration of high spectral remote sensor of spectral matching factor.This method reduces error, improves data accuracy and reliability.

Description

TECHNICAL FIELD

[0001] The application relates to a quantitative method of a spectral imager, in particular to a hyperspectral remote sensor cross-calibration method based on a spectral matching factor. BACKGROUND

[0002] In recent years, a hyperspectral remote sensor has been developed and applied as one of main types of earth observation remote sensing loads. During on-orbit operation of the remote sensor, the radiation performance of the remote sensor changes due to factors such as aging of parts, platform vibration and dramatic change of operation environment. The radiation performance decay of the remote sensor can be tracked and corrected in time through cross-calibration. The cross-calibration is a method of calibrating a remote sensor to be calibrated by taking a remote sensor with high radiation calibration accuracy as a reference. The existing hyperspectral loads of Fengyun satellites, marine satellites and resource satellites are cross-calibrated. The cross-calibration has gradually become an important means for on-orbit calibration of the hyperspectral load.

[0003] The spectral matching factor is an important input item of a cross-calibration calculation model, is a ratio of radiance or apparent reflectivity of corresponding spectral channels of the remote sensor to be calibrated and the reference remote sensor, reflects differences between spectral channels and response functions of the remote sensors, and is a main error source of the cross-calibration.

[0004] In the spectral matching process, the spectral channels of the remote sensor to be calibrated and the reference remote sensor are usually selected to be close to each other for matching. The closer the spectral matching factor is to 1, the more accurate the cross-calibration result is. However, for most remote sensors to be calibrated, it is difficult to find a reference remote sensor with completely consistent spectral channels and spectral response functions. When the spectral channels of the reference remote sensor and the remote sensor to be calibrated for cross-calibration are far apart, the spectral matching factor has a large error, and the accuracy of the calibration coefficient is limited. Therefore, it is of important application prospect to invent a cross-calibration method based on the spectral matching factor with higher accuracy. SUMMARY

[0005] The application aims at solving the technical problem that the existing hyperspectral remote sensor only depends on a single spectral channel in the cross-calibration process, and the cross-calibration accuracy is poor, and provides a hyperspectral remote sensor cross-calibration method based on a spectral matching factor.

[0006] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0007] A hyperspectral remote sensor cross-calibration method based on a spectral matching factor, which is characterized by comprising the following steps:

[0008] 1】Obtain spectral channel characteristics of a remote sensor A to be calibrated and a reference remote sensor B. The spectral channel characteristics include a center wavelength, a spectral channel and a spectral response function;

[0009] 2】Simulate the apparent radiance of the to-be-calibrated remote sensor A and the reference remote sensor B respectively, and calculate the simulated spectral radiance of the to-be-calibrated remote sensor A and the reference remote sensor B after responding to the respective spectral channels according to the apparent radiance and the spectral channel characteristics;

[0010] 3】Calculate the spectral matching factors of all spectral channels of the to-be-calibrated remote sensor A according to the simulated spectral radiance obtained in step 2】;

[0011] 4】Obtain the cross-calibration test area of the to-be-calibrated remote sensor A and the reference remote sensor B in the intersection area;

[0012] 5】Extract the DN values of each spectral channel of the to-be-calibrated remote sensor A and the reference remote sensor B in the cross-calibration test area respectively, and calculate the true spectral radiance of each spectral channel of the reference remote sensor B according to the DN values of the reference remote sensor B in the cross-calibration test area:

[0013] 6】Calculate the weights of each spectral matching factor obtained in step 3】 and normalize the weights to obtain the weight coefficients of each spectral channel of the to-be-calibrated remote sensor A;

[0014] 7】After weighting each spectral matching factor obtained in step 3】 according to the weight coefficients obtained in step 6】 to obtain the final spectral matching factor of each spectral channel of the to-be-calibrated remote sensor A;

[0015] 8】Calculate the true spectral radiance of each spectral channel of the to-be-calibrated remote sensor A according to the true spectral radiance of the reference remote sensor B obtained in step 5】 and the final spectral matching factor obtained in step 7】;

[0016] 9】Calculate the radiometric calibration coefficient of each spectral channel of the to-be-calibrated remote sensor A according to the DN values of the to-be-calibrated remote sensor A in the cross-calibration test area in step 5】 and the true spectral radiance of the to-be-calibrated remote sensor A obtained in step 8】, and complete the cross-calibration of the spectral matching factor of the hyperspectral remote sensor.

[0017] Further, step 2】 is specifically:

[0018] 2.1、Input the atmospheric parameters, ground reflectivity, and observation geometry parameters of the to-be-calibrated remote sensor A and the reference remote sensor B measured synchronously on the site into the atmospheric radiation transfer model; simulate the apparent radiance of the to-be-calibrated remote sensor A and the reference remote sensor B at the top of the atmosphere layer by using the atmospheric radiation transfer model, and obtain the apparent radiance LM A (λ) and the apparent radiance LM B (λ);

[0019] 2.2、According to the spectral channel characteristics obtained in step 1】 and the apparent radiance LM A(λ), the simulated spectral radiance LM A (λ i ) after the response of the i-th spectral channel of the to-be-calibrated remote sensor A is calculated by the following formula

[0020]

[0021] wherein λ i is the effective wavelength range of the i-th spectral channel of the to-be-calibrated remote sensor A, i = 1, 2, 3, …, I; S(λ i ) is the spectral response function of the i-th spectral channel of the to-be-calibrated remote sensor A; λ imin is the minimum wavelength of the i-th spectral channel of the to-be-calibrated remote sensor A; λ imax is the maximum wavelength of the i-th spectral channel of the to-be-calibrated remote sensor A.

[0022] 2.3, the spectral channel characteristics obtained in step 1】and the apparent radiance LM B (λ) after the response of the k-th spectral channel of the reference remote sensor B is calculated by the following formula B (λ k ):

[0023]

[0024] wherein λ k is the effective wavelength range of the k-th spectral channel of the reference remote sensor B, k = 1, 2, 3, …, K; S(λ k ) is the spectral response function of the k-th spectral channel of the reference remote sensor B; λ kmin is the minimum wavelength of the j-th spectral channel of the reference remote sensor B, λ kmax is the maximum wavelength of the k-th spectral channel of the reference remote sensor B.

[0025] Further, step 3】is specifically:

[0026] 3.1, the simulated spectral radiance LM A (λ i ) of the center wavelength of the i-th spectral channel of the to-be-calibrated remote sensor A is taken;

[0027] 3.2, the n spectral channels j, j = 1…n, n ≤ K, having the minimum difference between the center wavelength of each spectral channel of the reference remote sensor B and the center wavelength of the i-th spectral channel of the to-be-calibrated remote sensor A are searched;

[0028] 3.3, the simulated spectral radiance LM A (λ i ) of the i-th spectral channel of the to-be-calibrated remote sensor A is divided by the simulated spectral radiance corresponding to the n spectral channels j obtained in step 3.2, respectively, to obtain n spectral matching factors:

[0029]

[0030] Furthermore, step 4 specifically involves:

[0031] 4.1 Acquire the image pairs of the same name between the remote sensor to be calibrated A and the reference remote sensor B in the intersection area;

[0032] 4.2 After correcting the image pairs with the same name, a uniform, stable, and cloud-free study area was selected as the cross-calibration test area.

[0033] Furthermore, step 5 specifically involves:

[0034] 5.1 Extract the DN values ​​of each spectral channel of the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross-calibration test area;

[0035] 5.2. Based on the reference remote sensor B in the cross-calibration test area DN j The value is calculated using the following formula: the true spectral radiance L corresponding to the n spectral channels obtained by the reference remote sensor B in step 3.2. B,j :

[0036] L B,j =gain j *DN j +offset j

[0037] In the formula: gain j and offset j All of these are the radiometric calibration coefficients of the reference remote sensor B in n spectral channels.

[0038] Furthermore, step 6 specifically involves:

[0039] 6.1 Calculate the difference d between the center wavelength of the i-th spectral channel of the remote sensor to be calibrated A and the center wavelength of the reference remote sensor B in the n spectral channels j obtained in step 3.2. i,j ;

[0040] 6.2 Calculate the weights w of the n spectral matching factors obtained in step 3.3 using the following formula. i,j :

[0041]

[0042] 6.3. Regarding the weight w i,j Normalization yields the final weighting coefficients w′ for each spectral channel of the remote sensor A to be calibrated. i,j .

[0043] Furthermore, step 7 specifically involves:

[0044] The weight coefficient w' obtained according to step 6.3 i,j The spectral matching factor SBAF obtained in step 3.3 is matched by the following formula i,j After weighting, the final spectral matching factor SBAF of each spectral channel of the to-be-calibrated remote sensor A is obtained i ′ ,j :

[0045] SBAF i ′ ,j =SBAF i,j *w' i,j .

[0046] Further, step 8 is specifically:

[0047] The true spectral radiance L of the reference remote sensor B obtained according to step 5.2 B,j And the final spectral matching factor SBAF obtained in step 7 i ′ ,j The spectral matching of the i-th spectral channel of the to-be-calibrated remote sensor A is carried out, and the spectral radiance L of the i-th spectral channel of the to-be-calibrated remote sensor A is calculated according to the following formula A,i :

[0048]

[0049] Further, step 9 is specifically:

[0050] L A,i =gain i *DN i +offset i

[0051] According to the DN value of the to-be-calibrated remote sensor A obtained in step 5.1 i And the true spectral radiance L obtained in step 8 A,i , using the least square method, the radiation calibration coefficient gain i And offset i of the i-th spectral channel of the to-be-calibrated remote sensor A are calculated by the above formula, and the hyperspectral remote sensor cross calibration of the spectral matching factor is completed.

[0052] The beneficial effects of the present application are:

[0053] 1. The hyperspectral remote sensor cross-calibration method based on a spectral matching factor, which sets a spectral matching factor for a single spectral channel of a remote sensor to be calibrated, adjusts the size of the spectral matching factor based on a weight coefficient, fully utilizes the spectral channels of a reference remote sensor in the cross-calibration process, reduces the error caused by a single channel, and improves the data accuracy and reliability.

[0054] 2. The hyperspectral remote sensor cross-calibration method based on a spectral matching factor, which fully considers the different spectral channel characteristics of two hyperspectral remote sensors in the cross-calibration process, optimizes the spectral matching factor, improves the accuracy and precision of cross-calibration, and has important significance for the research on the cross-calibration method.

[0055] 3. The hyperspectral remote sensor cross-calibration method based on a spectral matching factor has great application prospects in the quantitative field of hyperspectral remote sensors, especially in the cross-calibration of hyperspectral remote sensors. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is a flowchart of an embodiment of the hyperspectral remote sensor cross-calibration method based on a spectral matching factor;

[0057] Figure 2 is a comparison chart of hyperspectral remote sensor data processed by a traditional method (blue) and the hyperspectral remote sensor cross-calibration method based on a spectral matching factor (red) of the present application. DETAILED DESCRIPTION

[0058] As shown in Figure 1 , the hyperspectral remote sensor cross-calibration method based on a spectral matching factor includes the following steps:

[0059] 1. Obtain the spectral channel characteristics of a remote sensor A to be calibrated and a reference remote sensor B; the spectral channel characteristics include a center wavelength, a spectral channel, and a spectral response function;

[0060] 2. Simulate the apparent radiance of the remote sensor A to be calibrated and the reference remote sensor B, respectively, and calculate the simulated spectral radiance of the remote sensor A to be calibrated and the reference remote sensor B after responding to the respective spectral channels based on the apparent radiance and the spectral channel characteristics;

[0061] 2.1. Input the atmospheric parameters, surface reflectivity, and observation geometry parameters of the remote sensor A to be calibrated and the reference remote sensor B into an atmospheric radiation transfer model; simulate the apparent radiance of the remote sensor A to be calibrated and the reference remote sensor B at the top of the atmosphere layer using the atmospheric radiation transfer model, and obtain the apparent radiance LM A (λ) and the apparent radiance LM B (λ);

[0062] 2.2, Calculate the simulated spectral radiance LM (λ) of the reference sensor B in the kth spectral channel after response, according to the spectral channel characteristics obtained in step 1 and the apparent radiance LM (λ) obtained in step 2.1, by the following formula: A A i

[0063]

[0064] In the formula, λ i is the effective wavelength range of the reference sensor B in the kth spectral channel, k = 1, 2, 3, …, K; S(λ i ) is the spectral response function of the reference sensor B in the kth spectral channel; λ imin is the minimum wavelength of the jth spectral channel of the reference sensor B, λ imax is the maximum wavelength of the kth spectral channel of the reference sensor B;

[0065] 2.3, Calculate the simulated spectral radiance LM (λ) of the reference sensor B in the kth spectral channel after response, according to the spectral channel characteristics obtained in step 1 and the apparent radiance LM (λ) obtained in step 2.1, by the following formula: B B k

[0066]

[0067] In the formula, λ k is the effective wavelength range of the reference sensor B in the kth spectral channel, k = 1, 2, 3, …, K; S(λ k ) is the spectral response function of the reference sensor B in the kth spectral channel; λ kmin is the minimum wavelength of the jth spectral channel of the reference sensor B, λ kmax is the maximum wavelength of the kth spectral channel of the reference sensor B;

[0068] 3, Calculate the spectral matching factor of all spectral channels of the sensor to be calibrated according to the simulated spectral radiance;

[0069] 3.1, Take the simulated spectral radiance LM (λ) of the center wavelength of the ith spectral channel of the sensor to be calibrated; A i

[0070] 3.2, Retrieve the 5 spectral channels j with the minimum difference between the center wavelength of each spectral channel of the reference sensor B and the center wavelength of the ith spectral channel of the sensor to be calibrated, j = a, b, c, d, e, where a, b, c, d, e are all positive integers;

[0071] ​​​​​​​​3.3. Based on the simulated spectral radiance LM obtained in step 2.2 A (λ i The simulated spectral radiance LM obtained in step 2.3 and step 2.3 B (λ j The simulated spectral radiance LM of the i-th spectral channel of the remote sensor A to be calibrated is expressed by the following formula. A (λ i Divide each of the five spectral channels j obtained in step 3.2 by the simulated spectral radiance corresponding to the five spectral channels j to obtain five spectral matching factors:

[0072]

[0073] After the above calculation, the remote sensor A to be calibrated performs calculations with the reference remote sensor B in the i-th spectral channel and obtains a total of 5 spectral matching factors: SBAF i,a SBAF i,b SBAF i,c SBAF i,d SBAF i,e ;

[0074] 4) Obtain the cross-calibration test area of ​​the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross region;

[0075] 4.1 Acquire the image pairs of the same name between the remote sensor to be calibrated A and the reference remote sensor B in the intersection area;

[0076] 4.2 After correcting the image pairs with the same name, a uniform, stable, and cloud-free study area was selected as the cross-calibration test area.

[0077] 5. Extract the DN (Digital Number) values ​​of the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross-calibration test area, and calculate the true spectral radiance of each spectral channel of the reference remote sensor (B):

[0078] 5.1 Extract the DN values ​​of each spectral channel of the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross-calibration test area;

[0079] 5.2. Based on the reference remote sensor B in the cross-calibration test area DN j The value is calculated using the following formula: the true spectral radiance L corresponding to the n spectral channels obtained by the reference remote sensor B in step 3.2. B,j :

[0080] L B,j =gain j *DN j +offset j

[0081] wherein: gain j and offset j are the radiometric calibration coefficients of the reference sensor B at n spectral channels;

[0082] 6】Considering that the single spectral channel i of the to-be-calibrated sensor A is only spectrally matched with the single spectral channel of the reference sensor B, there is a large error in the result, the five spectral channels j of the reference sensor B are simultaneously matched with the spectral channels i of the to-be-calibrated sensor A, the weight coefficients are given to the five spectral channels of the reference sensor B to generate the final spectral matching factor through weight coefficient normalization.

[0083] The difference between the center wavelength of the i-th spectral channel of the to-be-calibrated sensor A and the center wavelengths of the a-th, b-th, c-th, d-th and e-th spectral channels of the reference sensor B is calculated, the weight of each spectral matching factor obtained in step 3.3 is calculated, and the weight is normalized to obtain the final weight coefficient of each spectral channel of the to-be-calibrated sensor A, which is specifically:

[0084] 6.1、The present application adopts the basic method of inverse square of distance, calculates the difference d i,j between the center wavelength of the i-th spectral channel of the to-be-calibrated sensor A and the center wavelengths of the a-th, b-th, c-th, d-th and e-th spectral channels of the reference sensor B obtained in step 3.2.

[0085] 6.2、The weight w i,j of the five spectral matching factors obtained in step 3.3 is calculated by the following formula:

[0086]

[0087] 6.3、The weight w i,j is normalized by the following formula to obtain the weight coefficient w′ i,j :

[0088]

[0089] According to the above formula, the weight coefficients w′ i,a , w′ i,b , w′ i,c , w′ i,d , w′ i,e can be obtained.

[0090] 7】According to the weight coefficient w′ i,j obtained in step 6.3, the spectral matching factor SBAF i,j obtained in step 3.3 is weighted by the following formula to obtain the final spectral matching factor SBAF i of each spectral channel of the to-be-calibrated sensor A. ′ ,j :

[0091] SBAF i ′ ,j =SBAF i,j *w′ i,j

[0092] In the formula, SBAF i,j SBAF is the initially calculated spectral matching factor. i ′ ,j This is the spectral matching factor after assigning normalized weighting coefficients. The final spectral matching factor SBAF for the i-th spectral channel of the remote sensor to be calibrated can be calculated from the above formula. i ′ ,a , SBAF i ′ ,b , SBAF i ′ ,c , SBAF i ′ ,d , SBAF i ′ ,e .

[0093] 8. Based on the true spectral radiance L of the reference remote sensor B obtained in step 5.2 B,j And the final spectral matching factor SBAF obtained in step 7 i ′ ,j Spectral matching is performed on the i-th spectral channel of the remote sensor A to be calibrated, and the true spectral radiance L of the remote sensor A in the i-th spectral channel is calculated according to the following formula. A,i :

[0094]

[0095] In the formula, L B,j The reference is the true spectral radiance of the a, b, c, d, and e spectral channels of remote sensor B;

[0096] 9】Based on the DN of the remote sensor A to be calibrated obtained in step 5.1 i The value is the same as the true spectral radiance L obtained in step 8. A,i Using the least squares method, the radiometric calibration coefficient gain of the i-th spectral channel of the remote sensor A to be calibrated is calculated using the above formula. i and offset i Complete the cross-calibration of the hyperspectral remote sensor for spectral matching factors:

[0097] L A,i =gaini *DN i +offset i

[0098] In the cross-calibration process, the hyperspectral remote sensor usually has hundreds of spectral channels, and it is difficult to match the same type of high-precision calibration instrument with completely consistent spectral channel characteristics. According to any channel center wavelength value of the to-be-calibrated remote sensor A, the closest 5 spectral channels in the reference remote sensor B are retrieved and matched; by setting the weight coefficient and normalization processing, the spectral matching factor is generated, and the cross-calibration of the to-be-calibrated remote sensor A is realized. The present application is a kind of cross-calibration method of hyperspectral remote sensor based on spectral matching factor, solves the problem that the hyperspectral remote sensor only relies on a single spectral channel in the cross-calibration process, resulting in poor cross-calibration accuracy, improves the accuracy of cross-calibration coefficient (i.e. radiometric calibration coefficient), and effectively serves the quantitative application of remote sensing data.

[0099] Figure 2 A comparison chart of processing hyperspectral remote sensor data by using the original method (blue) and the present application is shown in the figure, from which Figure 2 It can be seen that the traditional cross-calibration method selects a single channel of the reference remote sensor B, calculates the spectral matching factor, and obtains the radiometric calibration coefficient, and the uncertainty of the radiometric calibration coefficient obtained by the present method is compared. The reference remote sensor B is AHSI / ZY1E, and the to-be-calibrated remote sensor A is HSI / HJ2A. The traditional method only relies on a single reference spectral channel, and the error is large. The present application method fully utilizes the characteristics of multiple reference spectral channels, and effectively reduces the error. Finally, the radiometric calibration coefficient of the present method has an average uncertainty of 1.56% (red) in the visible and near-infrared range of HSI / HJ2A, which is greatly improved compared with the traditional method.

Claims

1. A cross-calibration method for hyperspectral remote sensors based on spectral matching factors, characterized in that, Includes the following steps: 1) Obtain the spectral channel characteristics of the remote sensor to be calibrated, A, and the reference remote sensor, B; The spectral channel characteristics include the center wavelength, spectral channel, and spectral response function; 2】Simulate the apparent radiance of the remote sensor to be calibrated A and the reference remote sensor B respectively, and calculate the simulated spectral radiance of the remote sensor to be calibrated A and the reference remote sensor B after their respective spectral channel responses based on the apparent radiance and spectral channel characteristics.

3. Based on the simulated spectral radiance obtained in step 2, calculate the spectral matching factor for all spectral channels of the remote sensor A to be calibrated; 4) Obtain the cross-calibration test area of ​​the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross region; 5. Extract the DN values ​​of each spectral channel of the remote sensor to be calibrated A and the reference remote sensor B in the cross-calibration test area, and calculate the true spectral radiance of each spectral channel of the reference remote sensor B based on the DN value of the reference remote sensor B in the cross-calibration test area.

6. Calculate the weights of each spectral matching factor obtained in step 3, and normalize the weights to obtain the weight coefficients of each spectral channel of the remote sensor A to be calibrated: 6.1 Calculate the first value of the remote sensor A to be calibrated. The center wavelength of the spectral channels and the reference remote sensor B in n spectral channels The difference in center wavelength ; 6.2 Calculate the weights of the n spectral matching factors using the following formula. : ； 6.

3. Weighting Normalization yields the final weighting coefficients for each spectral channel of the remote sensor A to be calibrated. ; 7】After weighting each spectral matching factor obtained in step 3】 based on the weighting coefficients obtained in step 6】, the final spectral matching factor of each spectral channel of the remote sensor A to be calibrated is obtained.

8. Based on the true spectral radiance of the reference remote sensor B obtained in step 5 and the final spectral matching factor obtained in step 7, calculate the true spectral radiance of each spectral channel of the remote sensor A to be calibrated. 9】Based on the DN value of the remote sensor to be calibrated A in the cross-calibration test area obtained in step 5】 and the true spectral radiance of the remote sensor to be calibrated A obtained in step 8】, calculate the radiometric calibration coefficients of each spectral channel of the remote sensor to be calibrated A, and complete the cross-calibration of the hyperspectral remote sensor with spectral matching factor.

2. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 1, characterized in that, Step 2 is as follows: 2.1 Input the atmospheric parameters, surface reflectance, and observation geometry parameters of the remote sensor to be calibrated (A) and the reference remote sensor (B) obtained from synchronous site measurements into the atmospheric radiative transfer model; use the atmospheric radiative transfer model to simulate the apparent radiance of the remote sensor to be calibrated (A) and the reference remote sensor (B) at the top of the atmosphere, respectively, to obtain the apparent radiance. With apparent radiance ; 2.2 Based on the spectral channel characteristics obtained in step 1] and the apparent radiance obtained in step 2.1 The following formula is used to calculate the value of the remote sensor A to be calibrated in the first... Simulated spectral radiance after spectral channel response : ； In the formula, The remote sensor A to be calibrated is in the first The effective wavelength range of the spectral channel =1,2,3,…,I; The remote sensor A to be calibrated is in the first Spectral response function of the spectral channel; It is the remote sensor A to be calibrated. The minimum wavelength of the spectral channel; It is the remote sensor A to be calibrated. Maximum wavelength of the spectral channel; 2.3 Based on the spectral channel characteristics obtained in step 1] and the apparent radiance obtained in step 2.1 The reference remote sensor B is calculated using the following formula at the 1st... Simulated spectral radiance after spectral channel response : ； In the formula, Reference remote sensor B is in the 1st The effective wavelength range of the spectral channel ; Reference remote sensor B is in the 1st Spectral response function of the spectral channel; It is the reference remote sensor B. The minimum wavelength of the spectral channel, It is the reference remote sensor B. The maximum wavelength of the spectral channel.

3. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 2, characterized in that, Step 3 is: 3.1 Randomly select remote sensor A to be calibrated Simulated spectral radiance at the center wavelength of the spectral channel ; 3.

2. The center wavelengths of each spectral channel of the reference remote sensor B and the wavelengths of the remote sensor to be calibrated, are compared with those of the sensor A. The n spectral channels with the smallest center wavelength difference , A positive integer less than or equal to n, where n ≤ ; 3.

3. The following formula is used to calibrate the remote sensor A to be calibrated. Simulated spectral radiance of spectral channels Divide each of the n spectral channels obtained in step 3.2 by the results. Based on the corresponding simulated spectral radiance, n spectral matching factors are obtained: 。 4. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 3, characterized in that, Step 4 is: 4.1 Acquire the image pairs of the same name between the remote sensor to be calibrated A and the reference remote sensor B in the intersection area; 4.2 After correcting the image pairs with the same name, a uniform, stable, and cloud-free study area was selected as the cross-calibration test area.

5. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 4, characterized in that, Step 5 is: 5.1 Extract the DN values ​​of each spectral channel of the remote sensor to be calibrated (A) and the reference remote sensor (B) in the cross-calibration test area; 5.

2. Based on the reference remote sensor B in the cross-calibration test area The value is calculated using the following formula, which represents the true spectral radiance corresponding to the n spectral channels obtained by reference remote sensor B in step 3.

2. : ； In the formula: and All of these are the radiometric calibration coefficients of the reference remote sensor B in n spectral channels.

6. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 5, characterized in that, Step 7 is: Based on the weighting coefficients obtained in step 6.3 The spectral matching factor obtained in step 3.3 is calculated using the following formula. After weighting, the final spectral matching factor for each spectral channel of the remote sensor A to be calibrated is obtained. : 。 7. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 6, characterized in that, Step 8 is: Based on the true spectral radiance of reference remote sensor B obtained in step 5.2 And the final spectral matching factor obtained in step 7 The first calibration of remote sensor A Spectral matching is performed on the spectral channels, and the spectral data of the remote sensor A to be calibrated is calculated according to the following formula. Spectral radiance of spectral channels : 。 8. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 7, characterized in that, Step 9 is: ； Based on the results of step 5.1, the remote sensor A to be calibrated... The value is the same as the true spectral radiance obtained in step 8. Using the least squares method, the first step of the calibration of the remote sensor A to be calibrated is calculated using the above formula. Radiative scaling factors of spectral channels and The hyperspectral remote sensor cross-calibration of the spectral matching factor was completed.

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