Satellite remote sensing instrument radiation calibration method, device and equipment and storage medium

Abstract

Embodiments of the present application provide a satellite remote sensing instrument radiation calibration method, device, equipment and storage medium. Including: obtaining a first radiation calibration coefficient set; the first radiation calibration coefficient set is a set of radiation calibration coefficients obtained based on a preset radiation source arranged at the front end of the optical path of the satellite remote sensing instrument; obtaining a second radiation calibration coefficient set and a non-radiation transfer component temperature value; the second radiation calibration coefficient set is a set of radiation calibration coefficients obtained based on a preset black body arranged between internal optical path components of the satellite remote sensing instrument; the non-radiation transfer component temperature value is the temperature value of a non-radiation transfer optical path component inside the satellite remote sensing instrument that does not participate in radiation transfer when observing the preset black body; using the second radiation calibration coefficient set and the non-radiation transfer component temperature value as training samples, and using the first radiation calibration coefficient set as a training label to perform model training, to obtain a calibration coefficient conversion model. The present scheme can improve the accuracy of radiation calibration.

Description

Technical Field

[0001] This application relates to the field of aerospace technology, and in particular to a method, apparatus, electronic device, and computer storage medium for radiometric calibration of satellite remote sensing instruments. Background Technology

[0002] Satellite remote sensing instruments, mounted on satellites, can observe the Earth, receiving radiation (such as infrared or microwave radiation) from objects on Earth and outputting a dimensionless digital value without actual physical meaning. This output value is then calibrated to achieve quantitative remote sensing. In other words, based on calibration coefficients that characterize the quantitative relationship between the amount of radiation entering the satellite remote sensing instrument and the output value, the digital output value is converted into an observation parameter with definite physical dimensions. Therefore, before using satellite remote sensing instruments, radiometric calibration is necessary to obtain these calibration coefficients.

[0003] Existing radiometric calibration methods have low performance reliability. Practical applications have shown that using calibration coefficients obtained from existing methods for remote sensing measurements can lead to significant errors or even measurement failures.

[0004] Therefore, there is an urgent need for a radiation calibration scheme with clear physical meaning and reliable performance in order to obtain accurate calibration coefficients. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, apparatus, electronic device, and computer storage medium for radiometric calibration of satellite remote sensing instruments to solve some or all of the above-mentioned problems.

[0006] According to a first aspect of the embodiments of this application, a radiometric calibration method for a satellite remote sensing instrument is provided, comprising:

[0007] Obtain a first set of radiometric calibration coefficients; the first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset radiation source set at the front end of the optical path of the satellite remote sensing instrument; obtain a second set of radiometric calibration coefficients and temperature values ​​of non-radiative transfer components; the second set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset blackbody set between optical path components inside the satellite remote sensing instrument; the temperature values ​​of non-radiative transfer optical path components inside the satellite remote sensing instrument that do not participate in radiative transfer when observing the preset blackbody; use the second set of radiometric calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and use the first set of radiometric calibration coefficients as training labels to train the model, thereby obtaining a trained calibration coefficient conversion model, and obtaining the set of radiometric calibration coefficients of the satellite remote sensing instrument based on the calibration coefficient conversion model.

[0008] According to a second aspect of the embodiments of this application, a radiometric calibration device for a satellite remote sensing instrument is provided, comprising:

[0009] The first acquisition module is used to acquire a first set of radiometric calibration coefficients; the first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometric calibration of the satellite remote sensing instrument based on a preset radiation source set at the front end of the optical path of the satellite remote sensing instrument; the second acquisition module is used to acquire a second set of radiometric calibration coefficients and temperature values ​​of non-radiative transfer components; the second set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometric calibration of the satellite remote sensing instrument based on a preset blackbody set between optical path components inside the satellite remote sensing instrument; the temperature values ​​of the non-radiative transfer components are the temperature values ​​of the non-radiative transfer optical path components inside the satellite remote sensing instrument that do not participate in radiative transfer when observing the preset blackbody; the training module is used to train the model using the second set of radiometric calibration coefficients and the temperature values ​​of the non-radiative transfer components as training samples and the first set of radiometric calibration coefficients as training labels to obtain a trained calibration coefficient conversion model, and to obtain the set of radiometric calibration coefficients of the satellite remote sensing instrument based on the calibration coefficient conversion model.

[0010] According to a third aspect of the present application, an electronic device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, which causes the processor to perform an operation corresponding to the method of the first aspect.

[0011] According to a fourth aspect of the embodiments of this application, a computer storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the method of the first aspect.

[0012] According to the satellite remote sensing instrument radiometric calibration scheme provided in the embodiments of this application, a first set of radiometric calibration coefficients is obtained by performing radiometric calibration using a preset radiation source located at the front end of the optical path of the satellite remote sensing instrument, and a second set of radiometric calibration coefficients is obtained by performing radiometric calibration using a preset blackbody located between optical path components inside the satellite remote sensing instrument. Temperature values ​​of non-radiative transfer optical path components that do not participate in radiative transfer when the second set of radiometric calibration coefficients is obtained, but participate in radiative transfer when the first set of radiometric calibration coefficients is obtained, are also obtained. Then, using the second set of radiometric calibration coefficients and the temperature values ​​of the non-radiative transfer components as training samples, and using the first set of radiometric calibration coefficients as training labels, a calibration coefficient conversion model capable of accurately predicting differences in radiometric calibration coefficients caused by differences in the radiative optical path is trained.

[0013] In practical applications, after obtaining the actual radiometric calibration coefficients obtained by radiometric calibration using a blackbody located between optical path components inside the satellite remote sensing instrument, the actual radiometric calibration coefficients can be converted based on the aforementioned calibration coefficient conversion model to obtain more accurate radiometric calibration coefficients that match the actual application scenario. In other words, the embodiments of this application can effectively improve the accuracy of radiometric calibration. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0015] Figure 1 A schematic diagram comparing the position of the blackbody in a radiation calibration scenario with the position of the target object in an actual measurement scenario;

[0016] Figure 2 This is a flowchart illustrating the steps of a satellite remote sensing instrument radiometric calibration method according to an embodiment of this application.

[0017] Figure 3 This is a schematic diagram of a radiation calibration scenario according to an embodiment of this application;

[0018] Figure 4 This is a schematic diagram of another radiation calibration scenario according to an embodiment of this application;

[0019] Figure 5 This is a structural block diagram of a satellite remote sensing instrument radiometric calibration device according to an embodiment of this application;

[0020] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in the embodiments of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of this application.

[0022] General Overview of the Embodiments in this Application

[0023] Before using satellite remote sensing instruments, they need to be radiometrically calibrated to obtain calibration coefficients that can characterize the quantitative relationship between the amount of radiation entering the satellite remote sensing instrument and the output value.

[0024] Satellite remote sensing instruments typically contain multiple optical path components. Indicatively, these may include: primary optical components (such as primary mirrors, secondary mirrors, folding mirrors, etc.) for collecting and focusing beams from radiation sources, and secondary optical path components (such as filters, dichroic mirrors, gratings, etc.) for beam splitting, modulation, and secondary imaging. Radiation emitted from external objects can enter the detector through the optical path composed of these components.

[0025] At present, on-board blackbody radiation calibration is usually performed. The radiation emitted by the blackbody enters the detector through the optical path formed by the optical path components, so that the detector outputs the corresponding output value. By observing the actual energy of the blackbody radiation and the corresponding instrument output value through multiple calibration observations, the calibration coefficient under the current calibration state can be obtained by curve fitting.

[0026] Specifically, for infrared satellite remote sensing instruments, the equation for calibration using on-board blackbody radiation can be expressed as the following formula (1):

[0027]

[0028] in, It is the radiation calibration coefficient. The radiometric calibration coefficient is the electronic output count value (Digital Number, DN) of a satellite remote sensing instrument, which is the dimensionless count value of the electronic output converted into the target radiation quantity. The bridge, R is the energy of the radiation source observed by the instrument, and N is the total number of radiation calibration coefficients. When an infrared / microwave remote sensing instrument performs radiation calibration, the instrument observes radiation sources with known energy, let... The energy of the radiation source observed during the secondary calibration was , Corresponding output Count value , Then, curve fitting method is used to... By fitting the data, the radiation calibration coefficients in formula (1) can be obtained. .

[0029] See Figure 1 As shown in Figure a, due to factors such as satellite space, weight, and structure, radiometric calibration often necessitates placing the calibration blackbody between various optical path components inside the instrument. However, see... Figure 1As shown in Figure b, in actual measurement scenarios, the target object to be measured (such as a target object on Earth) is located at the front end of the optical path of the satellite remote sensing instrument (in this embodiment of the application, for the optical path formed by all optical path components, the end furthest from the detector is called the optical path front end), that is, the front end of all the aforementioned optical path components.

[0030] Because the optical path traversed by blackbody radiation during radiation calibration differs from the optical path traversed by the target object's radiation in the actual measurement scenario, the calibration coefficients obtained through the above scheme can only characterize the quantitative relationship between the radiation quantity and output value corresponding to the optical path between the blackbody and the detector, and are not equivalent to the quantitative relationship between the radiation quantity and output value corresponding to the complete optical path. If actual remote sensing measurements are performed based on the above calibration results, it will lead to significant errors or even measurement errors.

[0031] The above situation will be explained from a theoretical perspective below:

[0032] For on-orbit satellite remote sensing instruments, when using a blackbody located at the front end of the instrument's optical path for radiometric calibration, the following formula (2) is satisfied:

[0033]

[0034]

[0035]

[0036]

[0037] in, It is the total radiant energy received by the detector when observing a blackbody. It is the blackbody emissivity. The Planck function is used to calculate temperature. A blackbody at wavelength The detector receives both blackbody radiation and blackbody reflected radiation emitted from the surface. It will also receive spontaneous radiation from the instrument, in order to express; It is the total radiant energy received by the detector when observing cold space; Therefore The instrument spectral response function is defined as the independent variable and the instrument's relative conversion efficiency as the dependent variable. This function is used to describe the instrument's response characteristics to all wavelengths. It is the electronic output count value of the satellite remote sensing instrument when observing a blackbody; It is the electronic output count value of satellite remote sensing instruments when observing cold space.

[0038] When observing cold air, the detector can still receive spontaneous emissions from the instrument. Therefore, the scaling factors are calculated. hour, It is differentiated, and at this time the calculation in formula (2) is performed. The formula only contains information about the energy of the calibration source blackbody, therefore the radiation calibration coefficient... It can be calculated.

[0039] However, when a blackbody located between the optical path components inside the instrument is used for radiation calibration, the above formula (2) becomes the following formula (3):

[0040]

[0041]

[0042]

[0043]

[0044] Because the optical paths for observing a blackbody and observing cold space are different, the spontaneous emission of the instrument in formula (3) is no longer the same for observing a blackbody and observing cold space, and is therefore expressed as follows: and It is said that, due to Therefore, the scaling factors are calculated. The time cannot be differencing, meaning the scaling coefficients cannot be calculated.

[0045] To address the aforementioned issues, this application provides a radiometric calibration scheme for satellite remote sensing instruments. Specifically, it acquires a first set of radiometric calibration coefficients obtained by performing radiometric calibration using a preset radiation source located at the front end of the optical path of the satellite remote sensing instrument, and a second set of radiometric calibration coefficients obtained by performing radiometric calibration using a preset blackbody located between optical path components inside the satellite remote sensing instrument. It also acquires the temperature values ​​of non-radiative transfer optical path components that do not participate in radiative transfer when the second set of radiometric calibration coefficients is obtained, but participate in radiative transfer when the first set of radiometric calibration coefficients is obtained. Then, using the second set of radiometric calibration coefficients and the temperature values ​​of the non-radiative transfer components as training samples, and using the first set of radiometric calibration coefficients as training labels, a calibration coefficient conversion model capable of accurately predicting differences in radiometric calibration coefficients caused by differences in the radiative optical path is trained.

[0046] In practical applications, after obtaining the actual radiometric calibration coefficients obtained by radiometric calibration using a blackbody located between optical path components inside the satellite remote sensing instrument, the actual radiometric calibration coefficients can be converted based on the above calibration coefficient conversion model to obtain more accurate radiometric calibration coefficients that match the actual application scenario.

[0047] The scheme provided in this application uses a set of radiation calibration coefficients under different calibration conditions and the temperature values ​​of the corresponding non-radiative transfer optical path components as training data to train a calibration coefficient transformation model, thereby enabling the model to possess prior knowledge of unknown factors. Furthermore, for remote sensing instruments, their internal radiation transfer processes involve nonlinear equations such as Planck's formula and temperature conduction formula, with the temperature values ​​of optical components as variables. The model possessing prior knowledge of unknown factors exhibits a stronger nonlinear fitting capability. Therefore, this application embodiment utilizes the above model to address the issue of... and The differences in calibration coefficients caused by the differences between them were accurately predicted, thus making the obtained radiation calibration coefficients more accurate.

[0048] Detailed implementation process of the embodiments of this application

[0049] Reference Figure 2 , Figure 2 This is a flowchart illustrating the steps of a satellite remote sensing instrument radiometric calibration method according to an embodiment of this application. The satellite remote sensing instrument radiometric calibration method provided in this application includes the following steps:

[0050] Step 202: Obtain the first set of radiometric calibration coefficients; the first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometric calibration of the satellite remote sensing instrument based on a preset radiation source set at the front end of the optical path of the satellite remote sensing instrument.

[0051] Schematic, see Figure 3 The first set of radiometric calibration coefficients can be calibration coefficients obtained by radiometric calibration using a preset radiation source (any radiation source with known energy, such as a preset blackbody, sea surface, etc.) set at the front end of the optical path of a satellite remote sensing instrument. The set consisting of all the components. In this embodiment, the front end of the optical path of the satellite remote sensing instrument can refer to the end furthest from the detector (i.e., the detector is located at the rear end of the optical path) in relation to the optical path formed by all the optical path components. The preset radiation source can be located inside or outside the satellite remote sensing instrument; this embodiment does not limit this.

[0052] Specifically, a preset radiation source with known energy can be set outside the instrument, and the instrument observes this preset radiation source. The energy of the preset radiation source for the second calibration observation is... , The corresponding output is... Count value , Then, based on the above formula (1), curve fitting is used to... By fitting the data pairs, the first set of radiation calibration coefficients in the embodiments of this application can be obtained. Where K is greater than or equal to The natural number.

[0053] In this embodiment, the number (m+1) of the first radiometric calibration coefficients is not limited and can be customized according to actual conditions. For example, when using a first-order polynomial for data fitting, two first radiometric calibration coefficients can be obtained; when using a second-order polynomial for data fitting, three first radiometric calibration coefficients can be obtained, and so on.

[0054] Step 204: Obtain the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components; the second set of radiation calibration coefficients is the set of radiation calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset blackbody set between the optical path components inside the satellite remote sensing instrument; the temperature values ​​of the non-radiative transfer components are the temperature values ​​of the non-radiative transfer optical path components inside the satellite remote sensing instrument that do not participate in radiation transfer when observing the preset blackbody.

[0055] Schematic, see Figure 4 The second set of radiometric calibration coefficients can be calibration coefficients obtained by radiometric calibration using a preset blackbody placed between two optical path components inside a satellite remote sensing instrument. The set that constitutes the collection.

[0056] Specifically, a preset blackbody with known energy can be placed between two optical path components inside the instrument. Then, similar to step 202, the instrument observes this preset blackbody and sets... The energy of the blackbody is preset for the second calibration observation. , The corresponding output is... Count value , Then based on the formula By fitting curves, the second set of radiation calibration coefficients in the embodiments of this application can be obtained. ,in, For n greater than or equal to The natural number.

[0057] Similarly, in this embodiment, the number (n+1) of the second radiometric calibration coefficients is not limited and can be customized according to actual conditions. For example, when using a first-order polynomial for data fitting, two second radiometric calibration coefficients can be obtained; when using a second-order polynomial for data fitting, three second radiometric calibration coefficients can be obtained, and so on.

[0058] In addition, the number of second radiation calibration coefficients can be the same as or different from the number of first radiation calibration coefficients. This application embodiment does not limit this, and can be customized according to actual conditions.

[0059] The temperature value of the non-radiative transfer component can refer to the temperature value of the non-radiative transfer optical path component inside the satellite remote sensing instrument under the radiometric calibration state of acquiring the aforementioned second set of radiometric calibration coefficients. Specifically, the non-radiative transfer optical path component is the optical path component inside the instrument that does not participate in radiation transfer when observing the aforementioned preset blackbody.

[0060] Step 206: Using the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model. The set of radiation calibration coefficients for satellite remote sensing instruments is then obtained based on the calibration coefficient conversion model.

[0061] Schematic illustration: The scaling coefficient conversion model in this application embodiment can be an artificial intelligence model, such as a neural network model. This application embodiment does not limit the specific architecture of the scaling coefficient conversion model or the specific values ​​of the model parameters. For example, the scaling coefficient conversion model can be a single deep feedforward network that can output all first radiation scaling coefficients at once. For instance, the network topology may include, in sequence: an input layer, a batch normalization layer (BN), a fully connected layer FC1, an activation layer (ReLU), a fully connected layer FC2, an activation layer (ReLU), a fully connected layer FC3, and an output layer (linear). The above network topology is merely an illustrative description of the scaling coefficient conversion model's network topology and does not constitute a limitation on the specific network topology of the scaling coefficient conversion model. The training process may include: inputting the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components into the input layer, then processing them through a batch normalization layer (BN), a fully connected layer FC1, an activation layer (ReLU), a fully connected layer FC2, an activation layer (ReLU), and a fully connected layer FC3, and finally outputting the predicted first set of radiation calibration coefficients through the output layer; calculating the loss value based on the predicted first set of radiation calibration coefficients and the first set of radiation calibration coefficients obtained in step 102 (e.g., the loss value can be obtained based on the weighted mean square error); adjusting the adjustable parameters in each layer of the model based on the loss value, thereby obtaining the trained calibration coefficient conversion model.

[0062] After obtaining the trained scaling coefficient transformation model through step 206 above, the following operations can be performed:

[0063] Obtain the third set of radiometric calibration coefficients and the actual component temperature values; wherein, the third set of radiometric calibration coefficients is the set of radiometric calibration coefficients obtained under the actual calibration state based on the third blackbody set between the optical path components inside the satellite remote sensing instrument; the actual component temperature values ​​are the temperature values ​​of the aforementioned non-radiative transmission optical path components under the actual calibration state; input the third set of radiometric calibration coefficients and the aforementioned actual component temperature values ​​into the trained calibration coefficient conversion model to obtain the actual radiometric calibration coefficients of the satellite remote sensing instrument under the actual calibration state.

[0064] According to the satellite remote sensing instrument radiometric calibration scheme provided in the embodiments of this application, a first set of radiometric calibration coefficients is obtained by performing radiometric calibration using a preset radiation source located at the front end of the optical path of the satellite remote sensing instrument, and a second set of radiometric calibration coefficients is obtained by performing radiometric calibration using a preset blackbody located between optical path components inside the satellite remote sensing instrument. Temperature values ​​of non-radiative transfer optical path components that do not participate in radiative transfer when the second set of radiometric calibration coefficients is obtained, but participate in radiative transfer when the first set of radiometric calibration coefficients is obtained, are also obtained. Then, using the second set of radiometric calibration coefficients and the temperature values ​​of the non-radiative transfer components as training samples, and using the first set of radiometric calibration coefficients as training labels, a calibration coefficient conversion model capable of accurately predicting differences in radiometric calibration coefficients caused by differences in the radiative optical path is trained.

[0065] In practical applications, after obtaining the actual radiometric calibration coefficients obtained by radiometric calibration using a blackbody located between optical path components inside the satellite remote sensing instrument, the actual radiometric calibration coefficients can be converted based on the aforementioned calibration coefficient conversion model to obtain more accurate radiometric calibration coefficients that match the actual application scenario. In other words, the embodiments of this application can effectively improve the accuracy of radiometric calibration.

[0066] Optionally, in some embodiments, the first set of radiation calibration coefficients includes multiple sets of first radiation calibration coefficients; the second set of radiation calibration coefficients includes multiple sets of second radiation calibration coefficients; a single set of first radiation calibration coefficients corresponds one-to-one with a single set of second radiation calibration coefficients, and the single set of first radiation calibration coefficients and the single set of second radiation calibration coefficients with the corresponding relationship correspond to the same calibration state.

[0067] The process of training the model using the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and using the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, can include:

[0068] The model is trained using the second radiation calibration coefficients and non-radiative transfer component temperature values ​​from the second radiation calibration coefficient set as training samples, and the first radiation calibration coefficients corresponding to each set of second radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0069] To illustrate, during the training of the calibration coefficient conversion model, the model learns the mapping relationship between the radiation calibration coefficients obtained by calibration using a preset radiation source located at the front end of the device's optical path and the radiation calibration coefficients obtained by calibration using a preset blackbody located between optical path components inside the device. The specific values ​​of the calibration coefficients are related to the calibration state (such as the external ambient temperature), and the values ​​of the radiation calibration coefficients may be different under different calibration states.

[0070] Based on the above considerations, the embodiments of this application obtain multiple sets of radiometric calibration coefficients when constructing training data, wherein different sets of radiometric calibration coefficients correspond to different calibration states. That is, multiple different calibration states are set, and for each calibration state, a first set of radiometric calibration coefficients and a second set of radiometric calibration coefficients are obtained. Then, model training is performed based on the radiometric calibration coefficients corresponding to each calibration state. In this way, the conversion performance of the calibration coefficient conversion model obtained by the final training can be effectively improved, so that the model can more accurately learn the mapping relationship between the radiometric calibration coefficients obtained by calibration using a preset radiation source located at the front end of the device's optical path and the radiometric calibration coefficients obtained by calibration using a preset blackbody located between optical path components inside the device, thereby improving the accuracy of radiometric calibration.

[0071] Optionally, in some embodiments, the process of obtaining a single set of first radiation calibration coefficients may include:

[0072] Under the preset calibration state, the energy values ​​of the preset radiation source and the first output values ​​of the satellite remote sensing instrument are obtained at multiple first measurement times. Based on the energy values ​​of the preset radiation source and the first output values ​​of the satellite remote sensing instrument at multiple first measurement times, a single set of first radiation calibration coefficients under the preset calibration state is obtained.

[0073] Correspondingly, the process of obtaining the second set of radiometric calibration coefficients corresponding to the first set of radiometric calibration coefficients can include:

[0074] Under a preset calibration state, the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument are acquired at multiple second measurement times. Based on the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument at multiple second measurement times, a single set of second radiometric calibration coefficients corresponding to a single set of first radiometric calibration coefficients are obtained under the preset calibration state. The time interval between the first measurement time and the second measurement time is less than a preset interval threshold.

[0075] To illustrate and facilitate understanding, the above process will be explained using examples below:

[0076] Assuming the preset radiation source is a first blackbody, under preset calibration conditions, starting from 00:00, the energy value and the first output value are measured every hour for a total of 4 times. The first measurement times are 00:00, 01:00, 02:00, and 03:00. After obtaining the energy value of the first blackbody and the first output value of the satellite remote sensing instrument at the first measurement times, curve fitting can be performed using the above formula 1 to obtain a single set of first radiation calibration coefficients under preset calibration conditions. .

[0077] Correspondingly, for the second blackbody located between the optical path components inside the satellite remote sensing instrument, the energy value and the first output value can be measured every hour starting from 00:01, for a total of 4 measurements. The second measurement times are 00:08, 01:08, 02:08, and 03:08. After obtaining the energy value of the second blackbody and the second output value of the satellite remote sensing instrument at the second measurement times, the above formula can be used. Curve fitting is performed to obtain a single set of second radiation calibration coefficients under the preset calibration conditions. .

[0078] In the example above, the time interval between the first measurement time and the second measurement time is 8 seconds, which is less than the preset interval threshold of 10 seconds.

[0079] Because the radiation state of satellite remote sensing instruments may change over time, and generally, the greater the time interval between two different moments, the greater the difference in the radiation state of the satellite remote sensing instrument. Therefore, in the above embodiments of this application, when performing radiometric calibration using a first blackbody and a second blackbody respectively, the time interval between measurement moments is limited to avoid excessive differences in the radiation state of the instrument when calibrated with the first blackbody compared to when calibrated with the second blackbody due to an excessively large time interval between the first and second measurement moments. In other words, it avoids situations where the calibration state when calibrated with the first blackbody differs from that when calibrated with the second blackbody, ensuring the consistency of the calibration state when calibrated using two different methods. Subsequently, using the radiometric calibration coefficients corresponding to the two calibration methods under the same calibration state as training data for the model can improve the accuracy of model prediction and further effectively improve the accuracy of radiometric calibration.

[0080] Optionally, in some embodiments, the number of non-radiative transmission optical path components is multiple, and the satellite remote sensing instrument radiometric calibration method may further include:

[0081] By combining the temperature values ​​of each non-radiative transfer component in pairs, multiple combined results are obtained.

[0082] For a single combination result, calculate the product of the temperature values ​​of the two non-radiative transfer components contained in the single combination result to obtain the product of the temperature values ​​corresponding to the single combination result.

[0083] Correspondingly, the process described above, which uses the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and the first set of radiation calibration coefficients as training labels to train the model and obtain the trained calibration coefficient conversion model, can include:

[0084] The model is trained using the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the product of the temperature values ​​corresponding to each combination result as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0085] Schematic illustration: Assume there are three non-radiative transfer components, namely component 1, component 2, and component 3. The temperature value of component 1 is T1, the temperature value of component 2 is T2, and the temperature value of component 3 is T3. These three temperature values ​​can be combined in pairs to obtain three combinations: (T1, T2), (T1, T3), and (T2, T3). Furthermore, the product of the temperature values ​​corresponding to each combination is obtained: the product of (T1, T2) is T1. T2, the product of the temperatures corresponding to (T1, T3) T1 T3, the product of the temperatures (T2, T3) and T2 T3.

[0086] Subsequently, during model training, in addition to inputting the second set of radiation calibration coefficients, T1, T2, and T3 as input data into the model, T1 was also... T2, T1 T3 and T2 T3 is also used as input data into the model. This allows the model to be trained, resulting in a trained scaling coefficient transformation model.

[0087] For satellite remote sensing instruments, when multiple non-radiative transfer optical path components exist, the temperature of these components primarily injects spontaneous emission differences into the detector through two physical pathways (i.e., the spontaneous emission of the satellite remote sensing instrument when observing a blackbody located between the internal optical path components). When observing the spontaneous emission of the instrument in the cold air outside the instrument, Differences between them):

[0088] (1) Planck radiation term Where λ is the wavelength, T is the temperature of the non-radiative optical transmission component, B is the radiance, h is Planck's constant, c is the speed of light in vacuum, and k is Boltzmann's constant. From the above formula, it can be seen that there is a non-linear relationship between the radiance and the temperature of the non-radiative optical transmission component.

[0089] (2) Heat transfer, that is, the temperature change of each non-radiative optical path component will be coupled to the detector through thermal radiation or thermal conduction, resulting in spontaneous emission differences. This can be approximated using the following formula: ΔL = F ( - ), where ΔL represents the differential radiation caused by the non-radiative transfer components. This differential radiation is ultimately superimposed on the main optical path signal, which is the reason why the second radiation calibration coefficient cannot be directly used to perform radiation conversion on target objects located outside the instrument; Indicates the first Emissivity of the surface of a non-radiative transfer component; For the first The effective radiation area of ​​each non-radiative transmission component; F The perspective factor represents the viewpoint from the component. The surface emits, by the component The received radiation share; and They represent the first The temperature value of the first non-radiative transfer component and the first The temperature value of the non-radiative transfer component. The above formula for calculating ΔL is obtained by Taylor expansion near the nominal temperature T0, and the resulting expansion contains a second-order term: K Δ Δ .That In the middle, Δ Indicates the first The temperature change of each non-radiative transmission component; Δ Indicates the first The temperature change of each non-radiative transmission component; K This represents the second-order coupling coefficient obtained after Taylor expansion.

[0090] Analysis of the above physical pathways (especially the heat transfer pathway) reveals that each non-radiative optical path component generates radiation in the form of a "temperature value product". This will result in the mapping relationship between the second radiation calibration coefficient and the first radiation calibration coefficient including higher-order interaction terms such as the "temperature value product".

[0091] Based on the above analysis, if only non-radiative optical transmission components are used as model inputs, the model needs to consume several additional network layers to synthesize the "temperature value product". The above synthesis process is located in the high curvature region of the high-dimensional parameter space, and the convergence rate is limited by gradient vanishing and combinatorial explosion. Therefore, it is time-consuming.

[0092] Therefore, in this embodiment, the temperature values ​​of each non-radiative transfer component are combined in pairs to obtain multiple combination results; the product of the temperature values ​​corresponding to each combination result is calculated, and then the product of the above temperature values ​​is explicitly introduced as an extended feature of the neural network model. This is equivalent to completing "physical prior feature engineering" at the input end of the model, fixing the nonlinear coupling terms that originally required additional model processing into linear readable coordinates. The above processing still follows the physical fact that "the Planck function, temperature conduction, and other processes have nonlinear temperature relationships" within the instrument. Therefore, while ensuring model performance, the convergence efficiency of the model can be improved.

[0093] Optionally, in some embodiments, the satellite remote sensing instrument radiometric calibration method may further include:

[0094] The temperature values ​​of each non-radiative transfer component are squared to obtain the squared result for each non-radiative transfer component.

[0095] Correspondingly, the model is trained using the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the product of the temperature values ​​corresponding to each combination result as training samples, and the first set of radiation calibration coefficients as training labels. This results in a trained calibration coefficient conversion model, which may include:

[0096] Using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, and the square operation results corresponding to each non-radiative transfer component as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

[0097] Schematic, as described above, for satellite remote sensing instruments, when there are multiple non-radiative transfer optical path components, the temperature of the non-radiative transfer optical path components mainly injects spontaneous emission differences into the detector through two physical pathways: Planck radiation and heat transfer.

[0098] Regarding the Planck radiation term, performing a first-order Taylor expansion on it near the nominal temperature T0 yields: B( )=C0+C1 Δ +6C1 (Δ ) 2 +…; where the quadratic term (Δ) ) 2 The coefficient 6C1 and Proportional.

[0099] In terms of heat transfer, for ΔL= F ( - After performing a Taylor expansion, we will get K ( Δ ) 2 .

[0100] Therefore, both of the above physical pathways also generate radiation in the form of "temperature squared", which will make the mapping relationship between the second radiation calibration coefficient and the first radiation calibration coefficient include "temperature squared".

[0101] Based on the above analysis, this embodiment of the application also adopts an explicit introduction method to use the square operation results corresponding to each non-radiative transfer component as an extended feature of the scaling coefficient transformation model, fixing the nonlinear coupling terms that originally required additional model processing into linear readable coordinates. The above processing still follows the physical fact that "the Planck function, temperature conduction and other processes have nonlinear temperature relationships" within the instrument. Therefore, while ensuring model performance, the convergence efficiency of the model can be further improved.

[0102] Optionally, in some embodiments, the satellite remote sensing instrument radiometric calibration method may further include:

[0103] Obtain the operating time information of non-radiative optical transmission path components.

[0104] Correspondingly, using the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and the first set of radiation calibration coefficients as training labels, the model is trained to obtain the calibration coefficient conversion model, which can include:

[0105] Using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, and the operating time information as training samples, and the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

[0106] Using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, and the square operation results corresponding to each non-radiative transfer component as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model, which can include:

[0107] Using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, the square operation results corresponding to each non-radiative transfer component, and the working time information of the non-radiative transfer optical path component as training samples, and the first set of radiation calibration coefficients as training labels, the model is trained to obtain the calibration coefficient conversion model.

[0108] To illustrate, during the actual operation of a satellite remote sensing instrument, its internal optical components experience irreversible degradation of reflectivity / emissivity due to space radiation and atomic oxygen erosion. In other words, as operating time increases, optical components continuously age: surfaces blacken, screws loosen, and molecular contamination occurs. These factors cause a gradual change in the thermal emissivity and thermal conductivity of the optical components. For non-radiative transfer components, these changes will also affect the mapping relationship between the first and second radiometric calibration coefficients. That is, under otherwise constant conditions, different degrees of aging (or degradation) of non-radiative transfer components will result in different mapping relationships between the first and second radiometric calibration coefficients.

[0109] In the embodiments described above, the operating duration information of the non-radiative transmission optical path component is separately added to the model input data. This is equivalent to extracting the aging curve of the non-radiative transmission component as a correction factor for its radiation performance. In this way, through model training, the model can automatically learn the decay law of the radiation performance of the non-radiative transmission component over time. Therefore, during inference, the model does not need to estimate or "guess" the changes in the aforementioned mapping relationship caused by the aging of the non-radiative transmission optical path component. Thus, it can effectively improve the efficiency of model training, enhance the conversion performance of the trained model, and improve the accuracy of radiometric calibration.

[0110] Optionally, in some embodiments, the satellite remote sensing instrument radiometric calibration method may further include:

[0111] Obtain the temperature gradient information of the non-radiative transfer component over time.

[0112] Correspondingly, the process of training the model using the second set of radiation calibration coefficients and the temperature value of the non-radiative transfer component as training samples, and using the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, may include:

[0113] Using the second set of radiation calibration coefficients, the temperature value of the non-radiative transfer component, and the temperature gradient information of the non-radiative transfer component over time as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

[0114] or,

[0115] Obtain historical temperature sequence information of non-radiative transfer components; historical temperature sequence information includes the temperature values ​​of non-radiative transfer components at different historical moments.

[0116] Correspondingly, the process of training the model using the second set of radiation calibration coefficients and the temperature value of the non-radiative transfer component as training samples, and using the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, may include:

[0117] Using the second set of radiation calibration coefficients, the temperature values ​​of the non-radiative transfer components, and the historical temperature sequence information of the non-radiative transfer components as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

[0118] Using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, the square operation results corresponding to each non-radiative transfer component, and the on-orbit duration information of the non-radiative transfer optical path components as training samples, and the first set of radiation calibration coefficients as training labels, the model is trained to obtain the trained calibration coefficient conversion model, which may include:

[0119] The model is trained using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, the square operation result corresponding to each non-radiative transfer component, the on-orbit duration information of the non-radiative transfer optical path component, and the temperature change gradient information of the non-radiative transfer component over time as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0120] or,

[0121] The model is trained using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, the square operation result corresponding to each non-radiative transfer component, the on-orbit duration information of non-radiative transfer optical path components, and the historical temperature sequence information of non-radiative transfer components as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0122] To illustrate, when a satellite remote sensing instrument enters or exits a shadow area, there is a thermal conduction delay (thermal hysteresis) between the surface and interior of its optical components. Considering the above, in this embodiment, the temperature gradient information over time of non-radiative transfer components or historical temperature sequence information is introduced as a temporal feature for model training. This allows the trained model to identify the dynamic impact of temperature change trends on calibration coefficients, further improving the accuracy of radiometric calibration.

[0123] Optionally, in some embodiments, the first radiation calibration coefficient set includes multiple first radiation calibration coefficients and multiple calibration coefficient conversion models; and the calibration coefficient conversion models correspond one-to-one with the first radiation calibration coefficients.

[0124] The process of training the model using the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and using the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, can include:

[0125] Using the second set of radiation calibration coefficients and the temperature of the non-radiative transfer components as training samples, the model is trained using each of the first radiation calibration coefficients in the first set of radiation calibration coefficients as labels, resulting in calibration coefficient conversion models corresponding to each of the first radiation calibration coefficients.

[0126] Schematic, the first radiation calibration factor can be That is, the number of the first radiation calibration coefficients can be m+1>1, and the number of the second radiation calibration coefficients can be... That is, the number of second radiation calibration coefficients can be n+1>1.

[0127] In the above embodiments of this application, m+1 calibration coefficient conversion models can be set, wherein one calibration coefficient conversion model is used to obtain a corresponding first radiation calibration coefficient.

[0128] Analyzing the above formula (1), taking formula (1) as a first-order polynomial as an example, the first set of radiation calibration coefficients can include the intercept. With slope The dynamic ranges of the two differ by several orders of magnitude. If the same model is used to predict all the first radiometric calibration coefficients in the first radiometric calibration coefficient set, then during model training, the loss function will be dominated by the large gradient coefficients, while the small gradient coefficients will converge and lag.

[0129] Based on the above considerations, in this embodiment of the application, an independent model is set for each first radiometric calibration coefficient. Each calibration coefficient conversion model is trained and deployed separately. In the process of model training, loss weights and convergence thresholds can be set separately for each coefficient, so that each calibration residual can reach the optimal at the same time. Therefore, the prediction performance of the model finally trained is better, which can improve the accuracy of radiometric calibration.

[0130] In the above embodiments of this application, the scaling coefficient transformation model can be a deep feedforward network with "one model per coefficient," and each scaling coefficient transformation model is trained and deployed separately. This application does not limit the specific architecture of the scaling coefficient transformation model or the specific values ​​of the model parameters; any model capable of implementing the scaling coefficient transformation function in this application's embodiments falls within the protection scope of this application. For example, the network topology of a single scaling coefficient transformation model may also include the following sequentially connected layers: input layer, batch normalization layer (BN), fully connected layer FC1, activation layer (ReLU), fully connected layer FC2, activation layer (ReLU), fully connected layer FC3, and output layer (linear). The above network topology is merely an illustrative description of the scaling coefficient transformation model's network topology and does not constitute a limitation on the specific network topology of the scaling coefficient transformation model. The model training process may include: inputting the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components into the input layer, then processing them through a batch normalization layer (BN), a fully connected layer FC1, an activation layer (ReLU), a fully connected layer FC2, an activation layer (ReLU), and a fully connected layer FC3, and finally outputting the predicted single first radiation calibration coefficient through the output layer; calculating the loss value based on the predicted single first radiation calibration coefficient and the corresponding first radiation calibration coefficient obtained in step 102; and adjusting the adjustable parameters in each layer of the model based on the loss value to obtain the trained calibration coefficient conversion model.

[0131] Figure 5 This is a structural block diagram of a satellite remote sensing instrument radiometric calibration device according to an embodiment of this application. The satellite remote sensing instrument radiometric calibration device provided in this application embodiment may include:

[0132] The first acquisition module 502 is used to acquire a first set of radiometric calibration coefficients; the first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometric calibration of the satellite remote sensing instrument based on a preset radiation source set in front of the optical path of the satellite remote sensing instrument.

[0133] The second acquisition module 504 is used to acquire the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components. The second set of radiation calibration coefficients is a set of radiation calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset blackbody set between the optical path components inside the satellite remote sensing instrument. The temperature values ​​of the non-radiative transfer components are the temperature values ​​of the non-radiative transfer optical path components inside the satellite remote sensing instrument that do not participate in radiation transfer when observing the preset blackbody.

[0134] Training module 506 is used to train the model using the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, and to obtain the set of radiation calibration coefficients of satellite remote sensing instruments based on the calibration coefficient conversion model.

[0135] Optionally, in some embodiments, the first set of radiation calibration coefficients includes multiple sets of first radiation calibration coefficients; the second set of radiation calibration coefficients includes multiple sets of second radiation calibration coefficients; a single set of first radiation calibration coefficients corresponds one-to-one with a single set of second radiation calibration coefficients, and the single set of first radiation calibration coefficients and the single set of second radiation calibration coefficients with the corresponding relationship correspond to the same calibration state.

[0136] Training module 506 is specifically used to: train the model using each group of second radiation calibration coefficients and non-radiative transfer component temperature values ​​in the second radiation calibration coefficient set as training samples, and use each group of first radiation calibration coefficients corresponding to each group of second radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0137] Optionally, in some embodiments, the process of obtaining a single set of first radiation calibration coefficients includes:

[0138] Under the preset calibration state, the energy values ​​of the preset radiation source and the first output values ​​of the satellite remote sensing instrument are acquired at multiple first measurement times.

[0139] Based on the energy values ​​of the preset radiation source and the first output values ​​of the satellite remote sensing instrument at multiple first measurement moments, a single set of first radiation calibration coefficients under the preset calibration state is obtained;

[0140] The process of obtaining the second set of radiometric calibration coefficients corresponding to the first set of radiometric calibration coefficients includes:

[0141] Under the preset calibration state, the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument are acquired at multiple second measurement times respectively;

[0142] Based on the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument at multiple second measurement times, a single set of second radiometric calibration coefficients corresponding to a single set of first radiometric calibration coefficients under the preset calibration state are obtained.

[0143] The time interval between the first measurement time and the second measurement time is less than a preset interval threshold.

[0144] Optionally, in some embodiments, the number of non-radiative optical path components is multiple.

[0145] The second acquisition module 504 is also used to: combine the temperature values ​​of each non-radiative transfer component in pairs to obtain multiple combination results; and for a single combination result, calculate the product of the temperature values ​​of the two non-radiative transfer components contained in the single combination result to obtain the temperature value product corresponding to the single combination result.

[0146] Training module 506 is specifically used to: train the model using the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the product of the temperature values ​​corresponding to each combination result as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0147] Optionally, in some embodiments, the second acquisition module 504 is further configured to: perform a square operation on the temperature values ​​of each non-radiative transfer component to obtain the square operation result corresponding to each non-radiative transfer component;

[0148] Training module 506 is specifically used to: use the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, and the square operation result corresponding to each non-radiative transfer component as training samples, and use the first set of radiation calibration coefficients as training labels to train the model and obtain the trained calibration coefficient conversion model.

[0149] Optionally, in some embodiments, the second acquisition module 504 is further configured to: acquire on-orbit duration information of the non-radiative transmission optical path component;

[0150] Training module 506 is specifically used to: train the model using the second set of radiation calibration coefficients, the temperature values ​​of non-radiative transfer components, and on-orbit duration information as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

[0151] Optionally, in some embodiments, the first radiation calibration coefficient set includes multiple first radiation calibration coefficients and multiple calibration coefficient conversion models; and the calibration coefficient conversion models correspond one-to-one with the first radiation calibration coefficients.

[0152] The training module 506 is specifically used to: use the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components as training samples, and use each of the first radiation calibration coefficients in the first set of radiation calibration coefficients as labels to train the model, so as to obtain the calibration coefficient conversion model corresponding to each of the first radiation calibration coefficients.

[0153] The satellite remote sensing instrument radiometric calibration device of this embodiment is used to implement the corresponding satellite remote sensing instrument radiometric calibration method in the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here. Furthermore, the functional implementation of each module in the satellite remote sensing instrument radiometric calibration device of this embodiment can be referred to the description of the corresponding part in the foregoing method embodiments, which will also not be repeated here.

[0154] Reference Figure 6 This document illustrates a schematic diagram of an electronic device according to an embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device.

[0155] like Figure 6 As shown, the electronic device may include: a processor 602, a communications interface 604, a memory 606, and a communications bus 608.

[0156] in:

[0157] The processor 602, communication interface 604, and memory 606 communicate with each other via communication bus 608.

[0158] Communication interface 604 is used for communication with other electronic devices.

[0159] The processor 602 is used to execute program 610, specifically to perform the relevant steps in the above method embodiments.

[0160] Specifically, program 610 may include program code that includes computer operation instructions.

[0161] The processor 602 may be a CPU, an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The smart device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0162] Memory 606 is used to store program 610. Memory 606 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0163] Program 610 may include multiple computer instructions. Specifically, program 610 may use multiple computer instructions to cause processor 602 to perform the operation corresponding to any of the methods described in the foregoing multiple method embodiments.

[0164] The specific implementation of each step in program 610 can be found in the corresponding steps and units described in the above method embodiments, and has corresponding beneficial effects, which will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0165] This application also provides a computer storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in any of the foregoing method embodiments. The computer storage medium includes, but is not limited to, compact disc read-only memory (CD-ROM), random access memory (RAM), floppy disk, hard disk, or magneto-optical disk.

[0166] This application also provides a computer program product, including computer instructions that instruct a computing device to perform an operation corresponding to any of the methods in the above-described multiple method embodiments.

[0167] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0168] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium. Thus, the methods described herein can be stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an Application Specific Integrated Circuit (ASIC) or a Field Programmable Gate Array (FPGA)). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., Random Access Memory (RAM), Read-Only Memory (ROM), Flash Memory, etc.) capable of storing or receiving software or computer code, implementing the methods described herein when the software or computer code is accessed and executed by the computer, processor, or hardware. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0169] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this application.

[0170] The above embodiments are only used to illustrate the embodiments of this application, and are not intended to limit the embodiments of this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of this application. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of this application, and the patent protection scope of the embodiments of this application should be defined by the claims.

Claims

1. A radiometric calibration method for satellite remote sensing instruments, characterized in that, include: Obtain the first set of radiation calibration coefficients; The first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset radiation source set at the front end of the optical path of the satellite remote sensing instrument. Obtain the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components; The second set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset blackbody placed between the optical path components inside the satellite remote sensing instrument. The temperature value of the non-radiative transfer component is the temperature value of the non-radiative transfer optical path component inside the satellite remote sensing instrument that does not participate in radiative transfer when observing the preset blackbody; the number of the non-radiative transfer optical path components is multiple. The temperature values ​​of each non-radiative transfer component are combined in pairs to obtain multiple combination results; for a single combination result, the product of the temperature values ​​of the two non-radiative transfer components contained in the single combination result is calculated to obtain the temperature value product corresponding to the single combination result. Using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model, and the set of radiation calibration coefficients for satellite remote sensing instruments is obtained based on the calibration coefficient conversion model.

2. The method according to claim 1, characterized in that, The first set of radiation calibration coefficients contains multiple sets of first radiation calibration coefficients; the second set of radiation calibration coefficients contains multiple sets of second radiation calibration coefficients. There is a one-to-one correspondence between the first set of radiation calibration coefficients and the second set of radiation calibration coefficients, and the first set of radiation calibration coefficients and the second set of radiation calibration coefficients with the corresponding relationship all correspond to the same calibration state. The process of training the model using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using the first set of radiation calibration coefficients as training labels, yields a trained calibration coefficient conversion model, including: The model is trained by using the product of each set of second radiation calibration coefficients in the second radiation calibration coefficient set, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using each set of first radiation calibration coefficients corresponding to each set of second radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model.

3. The method according to claim 2, characterized in that, The process of obtaining the first set of radiation calibration coefficients includes: Under a preset calibration state, the energy values ​​of the preset radiation source and the first output values ​​of the satellite remote sensing instrument are acquired at multiple first measurement times. Based on the energy value of the preset radiation source and the first output value of the satellite remote sensing instrument at the multiple first measurement times, a single set of first radiation calibration coefficients under the preset calibration state is obtained; The process of obtaining the single set of second radiometric calibration coefficients corresponding to the single set of first radiometric calibration coefficients includes: Under the preset calibration state, the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument are acquired at multiple second measurement times respectively; Based on the energy value of the preset blackbody and the second output value of the satellite remote sensing instrument at the multiple second measurement times, a single set of second radiometric calibration coefficients corresponding to the single set of first radiometric calibration coefficients under the preset calibration state are obtained; Wherein, the time interval between the first measurement time and the second measurement time is less than a preset interval threshold.

4. The method according to claim 1, characterized in that, The method further includes: The temperature values ​​of each non-radiative transfer component are squared to obtain the squared results for each non-radiative transfer component. The process of training the model using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using the first set of radiation calibration coefficients as training labels, yields a trained calibration coefficient conversion model, including: Using the second set of radiation calibration coefficients, the temperature values ​​of the non-radiative transfer components, the product of the temperature values ​​corresponding to each combination result, and the square operation results corresponding to each non-radiative transfer component as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the operating time information of the non-radiative transmission optical path component; The process of training the model using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using the first set of radiation calibration coefficients as training labels, yields a trained calibration coefficient conversion model, including: Using the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, the product of the temperature values ​​corresponding to each combination result, and the working time information as training samples, and using the first set of radiation calibration coefficients as training labels, the model is trained to obtain the completed calibration coefficient conversion model.

6. The method according to claim 1, characterized in that, The first set of radiation calibration coefficients contains multiple first radiation calibration coefficients, and the number of calibration coefficient conversion models is also multiple; furthermore, each calibration coefficient conversion model corresponds one-to-one with a first radiation calibration coefficient. The process of training the model using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and using the first set of radiation calibration coefficients as training labels, yields a trained calibration coefficient conversion model, including: Using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, the model is trained using each first radiation calibration coefficient in the first set of radiation calibration coefficients as labels, to obtain the calibration coefficient conversion model corresponding to each first radiation calibration coefficient.

7. A radiometric calibration device for a satellite remote sensing instrument, characterized in that, include: The first acquisition module is used to acquire the first set of radiation calibration coefficients; The first set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset radiation source set at the front end of the optical path of the satellite remote sensing instrument. The second acquisition module is used to acquire the second set of radiation calibration coefficients and the temperature values ​​of non-radiative transfer components. The second set of radiometric calibration coefficients is a set of radiometric calibration coefficients obtained by radiometrically calibrating the satellite remote sensing instrument based on a preset blackbody placed between the optical path components inside the satellite remote sensing instrument. The temperature value of the non-radiative transfer component is the temperature value of the non-radiative transfer optical path component inside the satellite remote sensing instrument that does not participate in radiative transfer when observing the preset blackbody; there are multiple non-radiative transfer optical path components; the temperature values ​​of each non-radiative transfer component are combined in pairs to obtain multiple combination results; for a single combination result, the product of the temperature values ​​of the two non-radiative transfer components contained in the single combination result is calculated to obtain the temperature value product corresponding to the single combination result; The training module is used to train the model using the product of the second set of radiation calibration coefficients, the temperature values ​​of each non-radiative transfer component, and the temperature values ​​corresponding to each combination result as training samples, and the first set of radiation calibration coefficients as training labels, to obtain the trained calibration coefficient conversion model, and to obtain the set of radiation calibration coefficients of the satellite remote sensing instrument based on the calibration coefficient conversion model.

8. An electronic device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method as described in any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in any one of claims 1-6.

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