A relative radiation calibration method and device based on heterogeneous yaw data fusion

Abstract

The application discloses a kind of relative radiation calibration method and device based on heterologous yaw data fusion, the method includes: according to the yaw imaging model generated by satellite probe element information to generate yaw data;Utilize imaging time length model to generate yaw data imaging time, and judge whether yaw data imaging time satisfies the requirement of pre-set threshold value, if satisfy;Utilize gray detection model to generate yaw data gray range, and judge whether yaw data gray range satisfies the requirement of pre-set threshold value, if satisfy, then carry out straight line detection and regulation processing to generate each probe element statistical data;Based on each probe element statistical data to generate yaw coefficient.The application is analyzed by yaw imaging characteristics, and proposes the mathematical model of the relative radiation calibration coefficient generation of multi-section yaw data fusion, not only determines the shortest time length of effective yaw data, also solves the problem that single-section yaw data covers incomplete gray scale range, and high-efficiency visible light yaw relative radiation calibration can be realized.

Description

Technical Field

[0001] This invention relates to the field of yaw imaging technology, and in particular to a relative radiometric calibration method and apparatus based on heterogeneous yaw data fusion. Background Technology

[0002] Yaw-based relative radiometric calibration is a classic algorithm for improving the radiometric quality of remote sensing images. It utilizes the radiometric response characteristics of satellite detectors to ensure the uniformity of radiometric brightness across the entire field of view. Yaw-based calibration is a relatively rigorous relative radiometric calibration method, capable of high-frequency, high-precision relative radiometric calibration and acquiring the real-time response status of the detector, providing valuable insights into the radiometric state of monitoring satellites in orbit. However, due to factors such as the limited maneuverability of satellites and their small swath width, which cannot cover the entire grayscale range, single-segment yaw data is often insufficient for yaw calibration requirements. Therefore, it is necessary to combine the characteristics of multiple yaw data segments to generate relative radiometric calibration coefficients. Furthermore, to ensure that all detectors can image the same ground features, thereby acquiring the response differences of detectors to the same entrance pupil brightness, and to ensure the grayscale coverage of the yaw data, it is crucial to utilize the actual response status as much as possible to generate radiometric calibration coefficients. Therefore, imaging duration becomes critical to ensuring the effectiveness of yaw data. Summary of the Invention

[0003] The present invention aims to at least partially solve one of the technical problems in the related art.

[0004] Therefore, the purpose of this invention is to propose a relative radiometric calibration method based on heterogeneous yaw data fusion, which determines the shortest duration of effective yaw data and solves the problem of incomplete grayscale coverage of single-segment yaw data, thereby achieving efficient visible light yaw relative radiometric calibration.

[0005] Another objective of this invention is to propose a relative radiometric calibration device based on heterogeneous yaw data fusion.

[0006] To achieve the above objectives, this invention proposes a relative radiometric calibration method based on heterogeneous yaw data fusion, comprising:

[0007] Yaw data is generated based on a yaw imaging model constructed from satellite probe information;

[0008] The imaging time of yaw data is generated using an imaging duration model, and it is determined whether the imaging time of the yaw data meets the requirements of a preset threshold. If it does, the imaging time is determined.

[0009] The grayscale detection model is used to generate the grayscale range of yaw data, and it is determined whether the grayscale range of yaw data meets the requirements of a preset threshold. If it does, the straight line detection and standardization processing of the yaw data are performed to generate statistical data of each probe element.

[0010] The yaw coefficient is generated based on the statistical data of each probe.

[0011] The relative radiometric calibration method based on heterogeneous yaw data fusion according to embodiments of the present invention may also have the following additional technical features:

[0012] Furthermore, the process of performing straight-line detection and specification processing of yaw data to generate statistical data for each detector element includes:

[0013] The yaw data is subjected to straight-line detection to obtain angle detection results. Based on the angle detection results, the yaw data is subjected to standardization processing to obtain statistical data for each probe element.

[0014] DN[m+n×width]=DN[m+(n+windth / K1-m×K2)×width] (1)

[0015] K1 = tanθ, K2 = tan(90-θ)

[0016] Wherein, DN is the grayscale data of the one-dimensional yaw calibration image stored by image row, DN[m+n×width] represents the grayscale value of the image at the m-th row and n-th column of the yaw data image, width is the width of the yaw radiometric calibration image, i.e. the number of linear CCD detector elements, and θ is the time angle of the yaw image.

[0017] Further, the grayscale response curve of each probe element is obtained:

[0018]

[0019] Among them, M jk Let n be the total number of pixels in the j-th element of the yaw data whose gray level is equal to k, and n be the number of pixels in the j-th element of the yaw data whose gray level is k.

[0020] Furthermore, the probability density function of the comprehensive histogram and the probability density function of the expected histogram for each probe are calculated. Assuming the gray level of each pixel in the histogram is k, the corresponding probability density function is:

[0021] P k =M jk / M (3)

[0022] Among them, M jk Let k be the number of pixels with gray level k, and M be the total number of pixels in the desired histogram. Then the cumulative probability density function corresponding to gray level k is:

[0023]

[0024] The cumulative probability density function of the expected histogram with gray level l is V lThe cumulative probability density function of the probe with gray level k is S. k Satisfying the relation:

[0025] V l ≤S k ≤V l+1 (5)

[0026] When ||V l -S k |-|V l+1 -S k When |≤0, use gray level l to replace gray level k; otherwise, use gray level l+1 to replace gray level k.

[0027] Furthermore, relative radiometric calibration coefficients are generated using the statistical parameters of the grayscale values ​​of each detector element:

[0028]

[0029] Where y is the original grayscale value of the image, y' is the grayscale value after relative radiometric correction, and m tar It is the expected gray mean of all probes, m i It is the gray mean of the i-th detector, v tar It is the standard deviation of the expected value of all probes, v i It is the gray standard deviation of the i-th probe.

[0030] Further, the imaging time of the yaw data is calculated:

[0031] T = [(D total -D overlap )+BitRange*ratio*Frequency]*t integrate (7)

[0032] Where T is the ideal yaw data imaging time, and D... total It is the total number of probes, D overlap This represents the total number of detectors within the overlap area, BitRange is the grayscale dynamic range, ratio is the percentage of the grayscale dynamic range that needs to be covered, Frequency is the frequency of each grayscale level, and t... integrate It is the integration time during sensor yaw imaging.

[0033] Furthermore, the grayscale values ​​of each element in the multiple yaw data are statistically merged. If the grayscale range of each element satisfies formula (8) > 0.8 * BitRange, then the yaw coefficient is generated based on the merged yaw data.

[0034]

[0035] Where, m kThe flag represents the frequency of gray level k in the detector histogram. k This is an identifier indicating whether grayscale k is available.

[0036] Furthermore, the effective range of the entire grayscale interval of the probe is statistically analyzed to determine whether the merged yaw data meets the requirements:

[0037]

[0038] Where Sum is the sum of all identifiers that meet the usage requirements, and BitRange is the maximum gray level of the satellite image. If Sum is greater than or equal to 0.8 * BitRange, then the requirements are met.

[0039] To achieve the above objectives, another aspect of the present invention proposes a relative radiometric calibration device based on heterogeneous yaw data fusion, comprising:

[0040] The yaw data generation module is used to generate yaw data based on the yaw imaging model constructed from satellite probe information;

[0041] The imaging time determination module is used to generate the yaw data imaging time using the imaging duration model, and to determine whether the yaw data imaging time meets the requirements of a preset threshold. If it does, the module determines the yaw data imaging time.

[0042] The grayscale range judgment module is used to generate the grayscale range of yaw data using the grayscale detection model, and to determine whether the grayscale range of the yaw data meets the requirements of a preset threshold. If it does, the module performs straight line detection and standardization processing of the yaw data to generate statistical data of each probe element.

[0043] The yaw coefficient generation module is used to generate the yaw coefficient based on the statistical data of each probe element.

[0044] The relative radiometric calibration method and apparatus based on heterogeneous yaw data fusion of this invention proposes a mathematical model for generating relative radiometric calibration coefficients by analyzing the characteristics of yaw imaging and fusing multiple yaw data segments. This model not only determines the shortest duration of effective yaw data but also solves the problem of incomplete grayscale coverage by single-segment yaw data, achieving efficient visible light yaw relative radiometric calibration.

[0045] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0046] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0047] Figure 1This is a flowchart of a relative radiometric calibration method based on heterogeneous yaw data fusion according to an embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of yaw data slant correction according to an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of histogram matching processing according to an embodiment of the present invention;

[0050] Figure 4 These are schematic diagrams illustrating different pushbroom imaging modes according to embodiments of the present invention;

[0051] Figure 5 This is a schematic diagram illustrating the use of yaw imaging data according to an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram of grayscale detection of yaw data according to an embodiment of the present invention;

[0053] Figure 7 This is a ground logic diagram of a relative radiometric calibration method based on heterogeneous yaw data fusion according to an embodiment of the present invention;

[0054] Figure 8 This is a schematic diagram of a relative radiometric calibration device based on heterogeneous yaw data fusion according to an embodiment of the present invention. Detailed Implementation

[0055] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0056] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0057] The relative radiometric calibration method and apparatus based on heterogeneous yaw data fusion proposed according to embodiments of the present invention are described below with reference to the accompanying drawings.

[0058] Figure 1 This is a flowchart of a relative radiometric calibration method based on heterogeneous yaw data fusion according to an embodiment of the present invention.

[0059] like Figure 1 As shown, the method includes, but is not limited to, the following steps:

[0060] S1, yaw data is generated based on the yaw imaging model constructed from satellite probe information;

[0061] S2, use the imaging duration model to generate the yaw data imaging time, and determine whether the yaw data imaging time meets the requirements of the preset threshold. If it does;

[0062] S3, use the grayscale detection model to generate the grayscale range of yaw data, and determine whether the grayscale range of yaw data meets the requirements of the preset threshold. If it does, perform straight line detection and standardization processing of the yaw data to generate statistical data of each probe element.

[0063] S4 generates the yaw coefficient based on statistical data of each probe.

[0064] The relative radiometric calibration method based on heterogeneous yaw data fusion according to embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0065] It is understandable that yaw imaging enables different sensors to image the same ground feature with minimal time difference. Yaw imaging rotates the satellite or camera by 90 degrees so that all sensors image the ground feature sequentially. Therefore, yaw data appears as diagonal lines of about 45 degrees on the image, with the diagonal lines representing the same ground feature. Figure 2 As shown in (a) of the image. Although the same ground feature was captured by the probes at the beginning and end of the imaging phases, the information about these features was not at the same level. Therefore, oblique line correction was required for the yaw strips. After standardized correction, the ground feature information captured by each probe was completely identical, as shown in (a). Figure 2 As shown in (b) of the diagram.

[0066] As some embodiments of the present invention, the standardization correction process is as follows: based on the angle detection results of the yaw radiation calibration data, the yaw radiation calibration data is standardized using formula (1) to ensure that each row of data in the image is the imaging data of all detector elements of the sensor for the same ground object.

[0067] DN[m+n×width]=DN[m+(n+windth / K1-m×K2)×width] (1)

[0068] K1 = tanθ, K2 = tan(90-θ)

[0069] In the formula, DN is the grayscale data of the one-dimensional yaw calibration image stored by image row; DN[m+n×width] represents the grayscale value of the image at the m-th row and n-th column of the yaw data image; width is the width of the yaw radiometric calibration image, i.e., the number of linear CCD detector elements; θ is the time angle of the yaw image.

[0070] Furthermore, based on the statistical results of the gray values ​​of each of the above probes, linear extrapolation coefficients and nonlinear coefficients can be generated.

[0071] Among them, the histogram matching method for nonlinear coefficients is used. After oblique line specification correction, each row of the image can be regarded as the same ground feature. With the accumulation of hundreds of thousands of rows of data, the gray-scale response curve of each detector element can be obtained, specifically expressed as follows:

[0072]

[0073] In the formula, M jk Let n be the total number of pixels in the j-th element of the yaw data whose gray level is equal to k, and n be the number of pixels in the j-th element of the yaw data whose gray level is k. The statistics of the gray values ​​of each element are the mathematical basis for generating the relative radiometric correction coefficient based on the yaw data.

[0074] In some embodiments of the present invention, the nonlinear coefficients are typically generated using a histogram matching method. For the histogram matching algorithm, the combined histogram of all probes is the expected histogram. The principle of establishing the lookup table is to ensure that the probability density function of the combined histogram of each probe after matching is the same as the probability density function of the expected histogram. Therefore, the probability density function of the combined histogram and the probability density function of the expected histogram of each probe are first calculated. Let the gray level of the pixel in the histogram be k, then the corresponding probability density function is:

[0075] P k =M jk / M (3)

[0076] In the above formula, M jk Let k be the number of pixels with gray levels equal to k, and M be the total number of pixels in the desired histogram. Then the cumulative probability density function corresponding to histogram k is:

[0077]

[0078] Let the cumulative probability density function of the expected histogram with gray level l be V. l The cumulative probability density function of a certain probe with gray level k is S. k If the following relationship is satisfied:

[0079] V l ≤S k ≤V l+1 (5)

[0080] When |V l -S k |-|V l+1 -S kWhen |≤0, replace gray level k with gray level l; otherwise, replace gray level k with gray level l+1. Processing all detectors in this way yields the corresponding low-brightness region lookup table. Finally, lookup table values ​​exceeding the transition point range during the statistical process are discarded. A schematic diagram of histogram matching processing is shown below. Figure 3 As shown, Figure 3 The top left corner shows the grayscale histogram of a certain probe element i, and the top right corner shows the expected histogram of all probe elements. According to the principle of formula (5), a new grayscale value mapping relationship for probe element i is generated, namely the grayscale lookup table, as shown in [reference]. Figure 3 In the lower left corner, make the gray-level distribution of probe i conform to the expected histogram distribution.

[0081] As some embodiments of the present invention, the statistical parameter method for linear coefficients is employed. The linear coefficients are generated using statistical parameters to produce relative radiometric calibration coefficients, specifically as follows:

[0082]

[0083] In the formula, y is the original grayscale value of the image, y' is the grayscale value after relative radiometric correction, and m tar It is the expected gray mean of all probes, m i It is the gray mean of the i-th detector, v tar It is the standard deviation of the expected value of all probes, v i The gray standard deviation of the i-th detector element can be transformed to obtain the gain of the linear model of this detector element as v. tar / v i The bias is m tar -m i *(v tar / v i ).

[0084] Furthermore, a mathematical model is generated by fusing relative radiometric calibration coefficients from multiple yaw data segments.

[0085] Understandably, linear pushbroom optical sensors suffer from unique response characteristics for each element due to inhomogeneities in response and bias between individual elements, inherent noise and dark current inconsistencies in each element, and differences in the characteristics of the sensor's peripheral circuitry. This results in variations in the image quality of each element, manifesting as various random and systematic image noises. Yaw imaging rotates the satellite or camera by 90 degrees, allowing all elements to image the ground features sequentially. The biggest difference between yaw imaging and conventional pushbroom imaging is that yaw imaging can achieve imagery of the same ground features by different elements with minimal temporal differences. For example... Figure 4 As shown, Figure 4 (a) in the image is a normal push-broom image. Figure 4(b) in the figure is yaw pushbroom imaging. The output image on the right is the grayscale response map of the probe after yaw, which is the data information obtained by all probes covering the same ground feature in one yaw.

[0086] In addition, the minimum requirements for usable yaw data include two parts: first, all elements must cover the same ground feature; second, the strips required to cover the same ground feature should not be too short, that is, to obtain an effective grayscale response, the frequency of each grayscale level must meet a certain number of times. For example... Figure 5 The rightmost available area represents the minimum usage conditions for a single yaw data test.

[0087] As some embodiments of the present invention, assuming that the frequency of each gray level is 500, the yaw imaging time required under ideal conditions can be calculated based on the above information:

[0088] T = [(D total -D overlap )+BitRange*ratio*Frequency]*t integrate (7)

[0089] In the formula, T is the yaw imaging duration under ideal conditions, and D total It is the total number of probes, D overlap This represents the total number of detectors within the overlap area, BitRange is the grayscale dynamic range, ratio is the percentage of the grayscale dynamic range that needs to be covered, Frequency is the frequency of each grayscale level, and t... integrate This is the integration time during yaw imaging by the sensor. Ideally, the angle between the yaw imaging data and the data is 45 degrees, therefore D total -D overlap This represents the number of valid probes. Based on the geometric relationship of a 45-degree angle, the height of both invalid data points at the beginning and end is D. total -D overlap When the yaw angle and flight speed are mismatched, the included angle of the yaw imaging data may differ, thus limiting the theoretically calculated yaw imaging duration. The main influencing factors include the mismatch between the yaw angle and flight speed, the grayscale range of the yaw data, and the linearity of the detector element. Alternatively, the yaw data imaging duration can be calculated based on the yaw data metadata and satellite design parameters.

[0090] Understandably, the imaging duration of yaw data is fundamental to ensuring its validity and is one of the necessary conditions for its full usability. The grayscale range of the yaw data is another key factor in its validity; only with sufficiently wide grayscale coverage will the yaw data be effective and the generated coefficients usable. Yaw photography typically covers different terrain features (ocean, land, clouds, etc.) at varying levels to obtain different grayscale values. The imaging center is usually located on the coastline to maximize data availability.

[0091] To accurately determine the grayscale range of the yaw data, the grayscale response values ​​of all elements in the entire data segment were first calculated, with the grayscale values ​​of elements not captured being set to 0. The specific statistical process is as follows:

[0092] As some embodiments of the present invention, taking a single detector element as an example, according to the yaw design principle, the grayscale histogram of the detector element is statistically analyzed, such as... Figure 4 As shown in the figure. The red box in the figure represents the grayscale value of a certain ground feature captured by a certain detector element in the non-optical halosing region. The grayscale histogram of this detector element is calculated as follows. Figure 6 The rightmost element is used to calculate the grayscale distribution of that element. This process is repeated for other elements until the grayscale distribution of all elements is calculated.

[0093] By analyzing the grayscale histograms of all probes, the grayscale range of yaw data can be detected, and the grayscale distribution can be understood.

[0094] Furthermore, based on the above, this invention concludes that the usability of yaw data consists of two factors: the yaw shooting time and the grayscale range of the yaw data. The yaw shooting time must be greater than the minimum satellite shooting time threshold, and the grayscale range of the yaw data must also be greater than the imaging grayscale range threshold. Only yaw data that meets these conditions is effective for relative radiometric correction of the image. However, it is difficult for single-track yaw data to simultaneously meet both conditions. When the yaw shooting time exceeds the threshold, the grayscale response range is often very narrow, failing to cover the low, medium, and high brightness ranges. To address these issues, this invention proposes a method of generating relative radiometric correction by merging multi-track yaw data.

[0095] As some embodiments of the present invention, when the single-track yaw data is detected by grayscale range detection and it is found that some grayscale ranges are not covered or the covered grayscale statistical values ​​are too few, the type of ground feature to be photographed in the next yaw imaging is given according to the grayscale ranges that do not meet the conditions of the yaw data. Based on the theoretical model of yaw imaging duration, the time of each yaw imaging must satisfy > formula (7). The grayscale values ​​of each probe element in multiple yaw data are statistically merged. If the grayscale range of each probe element satisfies formula (8) > 0.8 * BitRange, then the merged yaw data is valid yaw data and can be processed for radiometric coefficient generation.

[0096]

[0097] In the formula, m k The frequency of gray level k in the probe histogram. When the gray level frequency is greater than or equal to the empirical value of 500, the gray level meets the usage requirements. k This is an identifier indicating whether grayscale k is available.

[0098] Analyze the effective range of the entire grayscale interval of the statistical probe to determine whether the data segment meets the requirements:

[0099]

[0100] In the formula, Sum is the sum of all identifiers that meet the usage requirements, BitRange is the maximum gray level of the satellite image, and when Sum is greater than or equal to 0.8 * BitRange, the yaw data segment meets the usage requirements.

[0101] As some embodiments of the present invention, the specific process for yaw data merging to generate coefficients is as follows: Figure 7 As shown, the single-track yaw data generates a yaw imaging model based on satellite parameter information. Then, it determines whether the imaging time exceeds the time threshold based on the imaging duration model. If the time meets the minimum requirement, it determines whether the grayscale range meets the requirement based on the grayscale detection model. If the grayscale range is greater than 0.8*BitRange, it performs straight-line detection and standardization processing on the yaw data to generate statistical data for each detector element. Finally, it generates the yaw coefficient.

[0102] If the minimum shooting time requirement is not met, this invention can also use a grayscale detection model to determine whether the grayscale range meets the requirements. In cases with a low probability, the grayscale dynamic range will still be greater than 0.8 * BitRange. In this case, yaw data straight-line detection and standardization processing are performed to generate statistical data for each detector element; finally, the yaw coefficient is generated. However, usually, if the shooting time requirement is not met, the grayscale range will not be greater than 0.8 * BitRange. In this case, typical ground feature information for the next yaw imaging is provided, and shooting is repeated.

[0103] If neither the shooting time nor the grayscale range meets the requirements, typical ground feature information for the next yaw imaging is provided, and the shooting is repeated. The data from several yaw imaging sessions are merged until the grayscale dynamic range of all elements is greater than 0.8*BitRange. Finally, straight-line detection and standardization processing of the yaw data are performed to generate statistical data for each element; finally, the yaw coefficient is generated.

[0104] According to the relative radiometric calibration method based on heterogeneous yaw data fusion according to the embodiments of the present invention, the shortest duration of effective yaw data is determined, and the problem of incomplete grayscale coverage of single-segment yaw data is solved, thereby achieving efficient visible light yaw relative radiometric calibration.

[0105] To achieve the above embodiments, such as Figure 8 As shown, this embodiment also provides a relative radiometric calibration device 10 based on heterogeneous yaw data fusion. The device 10 includes: a yaw data generation module 100, an imaging time determination module 200, a grayscale range determination module 300, and a yaw coefficient generation module 400.

[0106] Yaw data generation module 100 is used to generate yaw data based on the yaw imaging model constructed from satellite probe information;

[0107] The imaging time determination module 200 is used to generate the yaw data imaging time using the imaging duration model, and to determine whether the yaw data imaging time meets the requirements of a preset threshold. If it does, the module determines the yaw data imaging time.

[0108] The grayscale range judgment module 300 is used to generate the grayscale range of yaw data using the grayscale detection model, and to determine whether the grayscale range of yaw data meets the requirements of the preset threshold. If it does, the linear detection and standardization processing of the yaw data are performed to generate statistical data of each probe element.

[0109] Yaw coefficient generation module 400 is used to generate yaw coefficients based on statistical data of each probe element.

[0110] The relative radiometric calibration device based on heterogeneous yaw data fusion according to embodiments of the present invention determines the shortest duration of effective yaw data and solves the problem of incomplete grayscale coverage of single-segment yaw data, thereby achieving efficient visible light yaw relative radiometric calibration.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0113] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A relative radiometric calibration method based on heterogeneous yaw data fusion, characterized in that, Includes the following steps: Yaw data is generated based on a yaw imaging model constructed from satellite probe information; The imaging time of yaw data is generated using an imaging duration model, and it is determined whether the imaging time of the yaw data meets the requirements of a preset threshold. If it does, the imaging time is determined. The grayscale detection model is used to generate the grayscale range of yaw data, and it is determined whether the grayscale range of yaw data meets the requirements of a preset threshold. If it does, the straight line detection and standardization processing of the yaw data are performed to generate statistical data of each probe element. A yaw coefficient is generated based on the statistical data of each probe element. Calculate the probability density function of the comprehensive histogram and the probability density function of the expected histogram for each probe, with the preset gray levels of pixels in the histogram as follows: The corresponding probability density function is: (3) in, gray level equals The number of pixels, If the total number of pixels in the desired histogram is , then the grayscale level is... The corresponding cumulative probability density function is: (4) The expected gray levels of the histogram are The cumulative probability density function is The gray level of the probe is The cumulative probability density function is Satisfying the relation: (5) when When using grayscale Replace grayscale Otherwise, use grayscale. Replace grayscale .

2. The method according to claim 1, characterized in that, The process of performing straight-line detection and standardization of yaw data to generate statistical data for each detector element includes: The yaw data is subjected to straight-line detection to obtain angle detection results. Based on the angle detection results, the yaw data is subjected to standardization processing to obtain statistical data for each probe element. (1) , in, This refers to the one-dimensional yaw calibration image grayscale data stored row by row. Indicates the first yaw data image OK Image grayscale values ​​in column, The width of the yaw radiation calibration image is determined by the number of linear CCD elements. The time angle of the yaw image.

3. The method according to claim 2, characterized in that, Obtain the grayscale response curve for each probe element: (2) in, For yaw data The gray level of each detector is equal to The total number of pixels, For yaw data The gray level of each probe is The number of.

4. The method according to claim 3, characterized in that, Relative radiometric calibration coefficients are generated using statistical parameters of the grayscale values ​​of each detector element. (6) in, It is the original grayscale value of the image. It is the gray value after relative radiometric correction. It is the expected gray mean of all probes. It is the first The average gray value of each probe element It is the standard deviation of the expected value of all probes. It is the first The gray standard deviation of each probe element.

5. The method according to claim 4, characterized in that, Calculate the imaging time of the yaw data: (7) in, This is the ideal yaw data imaging time. It is the total number of probes. It represents the total number of probes within the overlapping area. It refers to the grayscale dynamic range. It is the ratio that the grayscale dynamic range needs to cover. It is the frequency of each gray level. It is the integration time during sensor yaw imaging.

6. The method according to claim 5, characterized in that, The grayscale values ​​of each element in multiple yaw data are statistically combined. If the grayscale range of each element satisfies formula (8) > The yaw coefficient is then generated based on the merged yaw data. (8) in, The gray level in the probe histogram is frequency, grayscale Whether it is available.

7. The method according to claim 6, characterized in that, By analyzing the effective range of the entire grayscale interval of the statistical probe, it is determined whether the merged yaw data meets the requirements: (9) in, It is the sum of all identifiers that meet the usage requirements. The maximum gray level of the satellite image, if Greater than or equal to Then the requirement is met.

8. A relative radiometric calibration device based on heterogeneous yaw data fusion using the method described in claim 1, characterized in that, include: The yaw data generation module is used to generate yaw data based on the yaw imaging model constructed from satellite probe information; The imaging time determination module is used to generate the yaw data imaging time using the imaging duration model, and to determine whether the yaw data imaging time meets the requirements of a preset threshold. If it does, the module determines the yaw data imaging time. The grayscale range judgment module is used to generate the grayscale range of yaw data using the grayscale detection model, and to determine whether the grayscale range of the yaw data meets the requirements of a preset threshold. If it does, the module performs straight line detection and standardization processing of the yaw data to generate statistical data of each probe element. The yaw coefficient generation module is used to generate the yaw coefficient based on the statistical data of each probe element.

Images