Hyperspectral remote sensor cross calibration method based on spectrum matching factor

Abstract

The invention relates to a spectrum imager quantification method, in particular to a hyperspectral remote sensor cross calibration method based on a spectrum matching factor, and aims to solve the technical problem of poor cross calibration precision caused by the fact that an existing hyperspectral remote sensor only depends on a single spectrum channel in the cross calibration process. The method comprises the following steps: 1) obtaining spectrum channel characteristics; 2) calculating the simulated spectral radiance; 3) calculating a spectrum matching factor; 4) obtaining a cross calibration test area; 5) calculating the real spectral radiance of each spectral channel of the reference remote sensor B; 6) obtaining a weight coefficient; the hyperspectral remote sensor cross calibration method based on the spectrum matching factors comprises the steps of (1) obtaining the spectrum matching factors, (2) obtaining the final spectrum matching factors, (3) calculating the real spectrum radiance of each spectrum channel of the to-be-calibrated remote sensor A, and (4) calculating the radiance calibration coefficients of each spectrum channel of the to-be-calibrated remote sensor A. The hyperspectral remote sensor cross calibration method based on the spectrum matching factors reduces errors and improves data accuracy and reliability.

Description

Technical Field

[0001] The invention relates to a quantification method for a spectral imager, and in particular to a cross-calibration method for a hyperspectral remote sensor based on a spectral matching factor. Background Art

[0002] In recent years, hyperspectral remote sensors have been developed and applied as one of the main types of earth observation remote sensing payloads. During the on-orbit operation of remote sensors, the radiation performance of remote sensors changes due to factors such as component aging, platform vibration, and drastic changes in the operating environment. Cross-calibration can timely track and correct the radiation performance attenuation of remote sensors. Cross-calibration is a method of calibrating remote sensors to be calibrated with reference to remote sensors with higher radiation calibration accuracy. Existing hyperspectral payloads such as Fengyun satellites, ocean satellites, and resource satellites have all been cross-calibrated. Cross-calibration has gradually become an important means of on-orbit calibration of hyperspectral payloads.

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

[0004] In the process of spectral matching, the spectral channel of the remote sensor to be calibrated is often selected for matching with the reference remote sensor. The closer the spectral matching factor is to 1, the more accurate the result of cross-calibration is. However, for most remote sensors to be calibrated, it is difficult to find a reference remote sensor that is completely consistent with its spectral channel and spectral response function. When the wavelengths of the spectral channels used for cross-calibration between the reference remote sensor and the remote sensor to be calibrated are far apart, there will be a large error in the spectral matching factor, resulting in limited accuracy of the calibration coefficient. Therefore, inventing a more accurate cross-calibration method based on spectral matching factor has important application prospects. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problem that the existing hyperspectral remote sensor only relies on a single spectral channel during the cross-calibration process, resulting in poor cross-calibration accuracy, and to provide a hyperspectral remote sensor cross-calibration method based on spectral matching factors.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A hyperspectral remote sensor cross-calibration method based on spectral matching factor is characterized in that it includes the following steps:

[0008] 1] Obtaining spectral channel characteristics of the remote sensor A to be calibrated and the reference remote sensor B; the spectral channel characteristics include central wavelength, spectral channel and spectral response function;

[0009] 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 in their respective spectral channels according to the apparent radiance and spectral channel characteristics;

[0010] 3] According to the simulated spectral radiance obtained in step 2], calculate the spectral matching factors of all spectral channels of the remote sensor A to be calibrated;

[0011] 4] Obtain a cross calibration test area where the remote sensor A to be calibrated and the reference remote sensor B are in the cross area;

[0012] 5] Extract the DN values ​​of each spectral channel of the remote sensor A to be calibrated 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 value 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 remote sensor A to be calibrated;

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

[0015] 8] Calculate the true spectral radiance of each spectral channel of the remote sensor A to be calibrated 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];

[0016] 9] According to the DN value of the remote sensor A to be calibrated in the cross-calibration test area in step 5] and the real spectral radiance of the remote sensor A to be calibrated obtained in step 8], the radiation calibration coefficient of each spectral channel of the remote sensor A to be calibrated is calculated to complete the cross-calibration of the hyperspectral remote sensor with spectral matching factor.

[0017] Further, step 2] is specifically as follows:

[0018] 2.1. Input the atmospheric parameters, surface reflectivity, and observation geometry parameters of the uncalibrated remote sensor A and the reference remote sensor B measured synchronously at the site into the atmospheric radiation transfer model; use the atmospheric radiation transfer model to simulate the apparent radiance of the uncalibrated remote sensor A and the reference remote sensor B at the top of the atmosphere, and obtain the apparent radiance LM A (λ) and apparent radiance LM B (λ);

[0019] 2.2. Based on the spectral channel characteristics obtained in step 1] and the apparent radiance LM obtained in step 2.1 A(λ), the simulated spectral radiance LM of the remote sensor A to be calibrated after responding to the i-th spectral channel is calculated by the following formula A (λ i ):

[0020]

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

[0022] 2.3. Based on the spectral channel characteristics obtained in step 1 and the apparent radiance LM obtained in step 2.1 B (λ), the simulated spectral radiance LM of the reference remote sensor B after the kth spectral channel response is calculated by the following formula B (λ k ):

[0023]

[0024] In the formula, λ k is the effective wavelength range of the reference remote sensor B in the kth spectral channel, k = 1, 2, 3, ..., K; S(λ k ) is the spectral response function of the reference remote sensor B in the kth spectral channel; 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 remote sensor B.

[0025] Further, step 3] is specifically as follows:

[0026] 3.1、Select the simulated spectral radiance LM of the central wavelength of the i-th spectral channel of the remote sensor A to be calibrated A (λ i );

[0027] 3.2. Retrieve n spectral channels j, j = 1...n, n≤K, whose central wavelengths of the spectral channels of the reference remote sensor B and the central wavelengths of the i-th spectral channel of the remote sensor A to be calibrated have the smallest difference;

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

[0029]

[0030] Further, step 4] is specifically as follows:

[0031] 4.1. Obtain the same-name image pair of the remote sensor A to be calibrated and the reference remote sensor B in the intersection area;

[0032] 4.2. After the image pairs with the same name are corrected, a uniform, stable and cloud-free research area is selected as the cross-calibration test area.

[0033] Further, step 5] is specifically as follows:

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

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

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

[0037] Where: gain j and offset j are the radiation calibration coefficients of the reference remote sensor B in n spectral channels.

[0038] Further, step 6] is specifically as follows:

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

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

[0041]

[0042] 6.3. Weight w i,j Normalize to get the final weight coefficient w′ of each spectral channel of the remote sensor A to be calibrated i,j .

[0043] Further, step 7] is specifically as follows:

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

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

[0046] Further, step 8] is specifically as follows:

[0047] The real 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 , perform spectral matching on the i-th spectral channel of the remote sensor A to be calibrated, and calculate the spectral radiance L of the i-th spectral channel of the remote sensor A to be calibrated according to the following formula A,i :

[0048]

[0049] Further, step 9] is specifically as follows:

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

[0051] According to 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 radiation calibration coefficient gain of the i-th spectral channel of the remote sensor A to be calibrated is calculated by the above formula: i and offset i , completing the cross-calibration of hyperspectral remote sensors with spectral matching factors.

[0052] Beneficial effects of the present invention:

[0053] 1. The present invention discloses a hyperspectral remote sensor cross-calibration method based on spectral matching factor, which sets a spectral matching factor for a single spectral channel of the remote sensor to be calibrated, and adjusts the size of the spectral matching factor based on a weight coefficient. In the cross-calibration process, the spectral channel of the reference remote sensor is fully utilized, the error caused by a single channel is reduced, and the data accuracy and reliability are improved.

[0054] 2. The present invention provides a hyperspectral remote sensor cross-calibration method based on spectral matching factor, which fully considers the situation that two hyperspectral remote sensors have different spectral channel characteristics during the cross-calibration process, optimizes the spectral matching factor, improves the accuracy and precision of the cross-calibration, and is of great significance to the research on the cross-calibration method.

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

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

[0057] Figure 2 This is a comparison chart of processing hyperspectral remote sensor data using a traditional method (blue) and a hyperspectral remote sensor cross-calibration method based on spectral matching factors (red) of the present invention. DETAILED DESCRIPTION

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

[0059] 1] Obtaining spectral channel characteristics of the remote sensor A to be calibrated and the reference remote sensor B; the spectral channel characteristics include central wavelength, spectral channel and 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 in their respective spectral channels according to the apparent radiance and spectral channel characteristics;

[0061] 2.1. Input the atmospheric parameters, surface reflectivity, and observation geometry parameters of the uncalibrated remote sensor A and the reference remote sensor B measured synchronously at the site into the atmospheric radiation transfer model; use the atmospheric radiation transfer model to simulate the apparent radiance of the uncalibrated remote sensor A and the reference remote sensor B at the top of the atmosphere, and obtain the apparent radiance LM A (λ) and apparent radiance LM B (λ);

[0062] 2.2. Based on the spectral channel characteristics obtained in step 1] and the apparent radiance LM obtained in step 2.1 A (λ), the simulated spectral radiance LM of the remote sensor A to be calibrated after responding to the i-th spectral channel is calculated by the following formula A (λ i ):

[0063]

[0064] In the formula, λ i is the effective wavelength range of the remote sensor A in the i-th spectral channel, i = 1, 2, 3, ..., I; S(λ i ) is the spectral response function of the remote sensor A to be calibrated in the i-th spectral channel; imin is the minimum wavelength of the i-th spectral channel of the remote sensor A to be calibrated; imax is the maximum wavelength of the i-th spectral channel of the remote sensor A to be calibrated;

[0065] 2.3. Based on the spectral channel characteristics obtained in step 1 and the apparent radiance LM obtained in step 2.1 B (λ), the simulated spectral radiance LM of the reference remote sensor B after the response of the kth spectral channel is calculated by the following formula B (λ k ):

[0066]

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

[0068] 3] Calculate the spectral matching factors of all spectral channels of the remote sensor A to be calibrated based on the simulated spectral radiance;

[0069] 3.1、Select the simulated spectral radiance LM of the central wavelength of the i-th spectral channel of the remote sensor A to be calibrated A (λ i );

[0070] 3.2. Retrieve the five spectral channels j whose central wavelengths of the spectral channels of the reference remote sensor B and the central wavelengths of the i-th spectral channel of the remote sensor A to be calibrated have the smallest difference, j = a, b, c, d, e, where a, b, c, d, e are all positive integers;

[0071] 3.3. The simulated spectral radiance LM obtained in step 2.2 A (λ i ) and the simulated spectral radiance obtained in step 2.3 Simulated spectral radiance LM B (λ j ), the simulated spectral radiance LM of the i-th spectral channel of the remote sensor A to be calibrated is calculated by the following formula: A (λ i ) are divided by the simulated spectral radiances corresponding to the five spectral channels j obtained in step 3.2 to obtain five spectral matching factors:

[0072]

[0073] After the above calculation, the calibration sensor A is operated on the i-th spectral channel and the reference sensor B is operated on the j-th spectral channel, and a total of 5 spectral matching factors are obtained: SBAF i,a , SBAF i,b , SBAF i,c , SBAF i,d , SBAF i,e ;

[0074] 4] Obtain a cross calibration test area where the remote sensor A to be calibrated and the reference remote sensor B are in the cross area;

[0075] 4.1. Obtain the same-name image pair of the remote sensor A to be calibrated and the reference remote sensor B in the intersection area;

[0076] 4.2. After the image pairs with the same name are calibrated, a uniform, stable and cloud-free research area is selected as the cross-calibration test area;

[0077] 5] Extract the DN (Digital Number) values ​​of the remote sensor A to be calibrated 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:

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

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

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

[0081] Where: gain j and offset j are the radiation calibration coefficients of the reference remote sensor B in n spectral channels;

[0082] 6] Considering that a single spectral channel i of the remote sensor A to be calibrated is only spectrally matched with a single spectral channel of the reference remote sensor B, the result has a large error. The five spectral channels j of the reference remote sensor B are simultaneously matched with the spectral channel i of the remote sensor A to be calibrated. The weight coefficients are used to assign matching weight coefficients to the five spectral channels of the reference remote sensor B, and the final spectral matching factor is generated by normalizing the weight coefficients.

[0083] Calculate the difference between the central wavelength of the i-th spectral channel of the remote sensor A to be calibrated and the central wavelength of the reference remote sensor B in the a, b, c, d, e spectral channels, calculate the weights of each spectral matching factor obtained in step 3.3, and normalize the weights to obtain the final weight coefficients of each spectral channel of the remote sensor A to be calibrated, specifically:

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

[0085] 6.2. Calculate the weights w of the five spectral matching factors obtained in step 3.3 by the following formula: i,j :

[0086]

[0087] 6.3. The weight w is calculated by the following formula i,j Normalize and get the weight coefficient w′ i,j :

[0088]

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

[0090] 7] According to the weight coefficient w′ obtained in step 6.3 i,j The spectral matching factor SBAF obtained in step 3.3 is calculated by the following formula: i,j After weighting, the final spectral matching factor SBAF of each spectral channel of the remote sensor A to be calibrated is obtained: i ′ ,j :

[0091] SBAF i ′ ,j =SBAF i,j *w′ i,j

[0092] Where SBAF i,j is the initially calculated spectral matching factor, SBAF i ′ ,j is the spectral matching factor after the normalized weight coefficient is assigned. The final spectral matching factor SBAF of the i-th spectral channel of the remote sensor A 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] According to step 5.2, the true spectral radiance L of the reference remote sensor B is obtained B,j and the final spectral matching factor SBAF obtained in step 7 i ′ ,j , perform spectral matching on the i-th spectral channel of the remote sensor A to be calibrated, and calculate the true spectral radiance L of the i-th spectral channel of the remote sensor A to be calibrated according to the following formula A,i :

[0094]

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

[0096] 9] According to 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 radiation calibration coefficient gain of the i-th spectral channel of the remote sensor A to be calibrated is calculated by the above formula: i and offset i , complete the cross-calibration of the hyperspectral remote sensor with spectral matching factor:

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

[0099] During the cross-calibration process, a hyperspectral remote sensor usually has hundreds of spectral channels, and it is difficult to match a high-precision calibration instrument of the same type with completely consistent spectral channel characteristics. According to the central wavelength value of any channel of the remote sensor A to be calibrated, the five spectral channels closest to it in the reference remote sensor B are retrieved to match it; by setting the weight coefficient and normalization processing, a spectral matching factor is generated to achieve cross-calibration of the remote sensor A to be calibrated. The present invention provides a hyperspectral remote sensor cross-calibration method based on spectral matching factor, which solves the problem that the hyperspectral remote sensor only relies on a single spectral channel during the cross-calibration process, resulting in poor cross-calibration accuracy, improves the accuracy of the cross-calibration coefficient (i.e., the radiation calibration coefficient), and can effectively serve the quantitative application of remote sensing data.

[0100] Figure 2 FIG. 1 is a comparison diagram of processing hyperspectral remote sensor data using the original method (blue) and the hyperspectral remote sensor cross-calibration method based on spectral matching factor (red) of the present invention. Figure 2 It can be seen that the traditional cross-calibration method is used to select a single channel of the reference remote sensor B, calculate the spectral matching factor, and compare the uncertainty of the radiation calibration coefficient obtained by this method. The reference remote sensor B is AHSI / ZY1E, and the remote sensor A to be calibrated is HSI / HJ2A. The traditional method only relies on a single reference spectral channel, and the error is large. The method of the present invention makes full use of the characteristics of multiple reference spectral channels to effectively reduce the error. Finally, the uncertainty average of the radiation calibration coefficient of this method in the visible and near-infrared range of HSI / HJ2A is 1.56% (red), which is a significant improvement compared to the traditional method.

Claims

1. A hyperspectral remote sensor cross-calibration method based on spectral matching factor, characterized in that: The following steps are involved: 1] Obtain the spectral channel characteristics of the remote sensor A to be calibrated and the reference remote sensor B; The spectral channel characteristics include central wavelength, spectral channel and spectral response function; 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 in their respective spectral channels according to the apparent radiance and spectral channel characteristics; 3] According to the simulated spectral radiance obtained in step 2], calculate the spectral matching factors of all spectral channels of the remote sensor A to be calibrated; 4] Obtain a cross calibration test area where the remote sensor A to be calibrated and the reference remote sensor B are in the cross area; 5] Extract the DN values ​​of each spectral channel of the remote sensor A to be calibrated 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 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; 7] After weighting each spectral matching factor obtained in step 3] according to the weight coefficient obtained in step 6], the final spectral matching factor of each spectral channel of the remote sensor A to be calibrated is obtained; 8] Calculate the true spectral radiance of each spectral channel of the remote sensor A to be calibrated 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]; 9] According to the DN value of the remote sensor A to be calibrated in the cross-calibration test area in step 5] and the real spectral radiance of the remote sensor A to be calibrated obtained in step 8], the radiation calibration coefficient of each spectral channel of the remote sensor A to be calibrated is calculated to 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] Specifically: 2.

1. Input the atmospheric parameters, surface reflectivity, and observation geometry parameters of the uncalibrated remote sensor A and the reference remote sensor B measured synchronously at the site into the atmospheric radiation transfer model; use the atmospheric radiation transfer model to simulate the apparent radiance of the uncalibrated remote sensor A and the reference remote sensor B at the top of the atmosphere, and obtain the apparent radiance LM A (λ) and apparent radiance LM B (λ); 2.

2. Based on the spectral channel characteristics obtained in step 1] and the apparent radiance LM obtained in step 2.1 A (λ), the simulated spectral radiance LM of the remote sensor A to be calibrated after responding to the i-th spectral channel is calculated by the following formula A (λ i ): In the formula, λ i is the effective wavelength range of the remote sensor A in the i-th spectral channel, i = 1, 2, 3, ..., I; S(λ i ) is the spectral response function of the remote sensor A to be calibrated in the i-th spectral channel; λ imin is the minimum wavelength of the i-th spectral channel of the remote sensor A to be calibrated; λ imax is the maximum wavelength of the i-th spectral channel of the remote sensor A to be calibrated; 2.

3. Based on the spectral channel characteristics obtained in step 1 and the apparent radiance LM obtained in step 2.1 B (λ), the simulated spectral radiance LM of the reference remote sensor B after the kth spectral channel response is calculated by the following formula B (λ k ): In the formula, λ k is the effective wavelength range of the reference remote sensor B in the kth spectral channel, k = 1, 2, 3, ..., K; S(λ k ) is the spectral response function of the reference remote 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 remote sensor B.

3. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 2 is characterized in that: Step 3] Specifically: 3.1、Select the simulated spectral radiance LM of the central wavelength of the i-th spectral channel of the remote sensor A to be calibrated A (λ i ); 3.

2. Retrieve n spectral channels j, j = 1...n, n≤K, whose central wavelengths of the spectral channels of the reference remote sensor B and the central wavelengths of the i-th spectral channel of the remote sensor A to be calibrated have the smallest difference; 3.

3. The simulated spectral radiance LM of the i-th spectral channel of the remote sensor A to be calibrated is calculated by the following formula: A (λ i ) are divided by the simulated spectral radiances corresponding to the n spectral channels j obtained in step 3.2 to obtain n spectral matching factors:

4. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 3 is characterized in that: Step 4] Specifically: 4.

1. Obtain the same-name image pair of the remote sensor A to be calibrated and the reference remote sensor B in the intersection area; 4.

2. After the image pairs with the same name are corrected, a uniform, stable and cloud-free research area is selected as the cross-calibration test area.

5. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 4 is characterized in that: Step 5] Specifically: 5.

1. Extract the DN values ​​of each spectral channel of the remote sensor A to be calibrated and the reference remote sensor B in the cross-calibration test area respectively; 5.

2. Based on the DN of reference sensor B in the cross calibration test area j The true spectral radiance L corresponding to the n spectral channels obtained in step 3.2 of the reference remote sensor B is calculated by the following formula: B,j : L B,j =gain j *DN j +offset j Where: gain j and offset j are the radiation calibration coefficients of the reference remote sensor B in n spectral channels.

6. According to claim 5, a hyperspectral remote sensor cross-calibration method based on spectral matching factor is characterized in that step 6] specifically comprises: 6.

1. Calculate the difference d between the central wavelength of the i-th spectral channel of the remote sensor A to be calibrated and the central wavelength of the reference remote sensor B in the n-th spectral channel j obtained in step 3.

2. i,j ; 6.

2. Calculate the weight w of the n spectral matching factors obtained in step 3.3 by the following formula: i,j : 6.

3. Weight w i,j Normalize to get the final weight coefficient w′ of each spectral channel of the remote sensor A to be calibrated i,j .

7. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 6, characterized in that: Step 7] Specifically: According to the weight coefficient w′ obtained in step 6.3 i,j The spectral matching factor SBAF obtained in step 3.3 is calculated by the following formula: i,j After weighting, the final spectral matching factor SBAF of each spectral channel of the remote sensor A to be calibrated is obtained: i ′ ,j : SBAF i ′ ,j =SBAF i,j *In' i,j 。 8. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 7, characterized in that: Step 8] Specifically: The real 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 , perform spectral matching on the i-th spectral channel of the remote sensor A to be calibrated, and calculate the spectral radiance L of the i-th spectral channel of the remote sensor A to be calibrated according to the following formula A,i :

9. The hyperspectral remote sensor cross-calibration method based on spectral matching factor according to claim 8, characterized in that: Step 9] Specifically: L A,i =gain i *DN i +offset i According to 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 radiation calibration coefficient gain of the i-th spectral channel of the remote sensor A to be calibrated is calculated by the above formula: i and offset i , completing the cross-calibration of hyperspectral remote sensors with spectral matching factors.

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