An imaging parameter optimization method for optical remote sensing satellites based on comprehensive benefits

Abstract

The application relates to a comprehensive benefit-based optical remote sensing satellite imaging parameter optimization method, and belongs to the technical field of remote sensing satellite imaging effect improvement and optimization. The method solves the problem of unclear image quality in the prior art. Specifically, the method comprises the following steps: obtaining an initial image of a remote sensing satellite, obtaining imaging conditions of the initial image, and setting initial imaging parameters before initial image imaging; the parameters include integral order M and gain G; determining the imaging parameters to be optimized in the initial parameters based on the number of saturation points, the average gray value and the dynamic range of the initial image; obtaining first imaging parameters based on the live imaging conditions of the initial image according to an imaging parameter mapping table; obtaining second imaging parameters according to the initial image quality; and calculating the parameter values of the initial imaging parameters to be optimized based on the first and second imaging parameters. The purpose of improving the image quality is achieved.

Description

Technical Field

[0001] This invention relates to the field of remote sensing satellite imaging effect improvement and optimization technology, and in particular to an optical remote sensing satellite imaging parameter optimization method based on comprehensive benefits. Background Technology

[0002] When developing an optical remote sensing satellite observation plan, it is difficult to obtain complete information on the reflectivity of the target and background objects, meteorological conditions, lighting conditions, atmospheric models during imaging, etc., making it impossible to accurately set the sensor integration level and gain, resulting in a lack of imaging quality.

[0003] The integral series and gain obtained in the existing technology may result in image quality loss due to changes in meteorological conditions. Summary of the Invention

[0004] Based on the above analysis, the embodiments of the present invention aim to provide a method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits, in order to solve the problem of unclear image quality in the prior art.

[0005] On one hand, embodiments of the present invention provide a method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits, the method comprising the following steps:

[0006] Acquire initial images from remote sensing satellites, obtain the imaging conditions of the initial images, and the initial imaging parameters set before imaging the initial images; the parameters include: the integration series M and the gain G;

[0007] The imaging parameters to be optimized in the initial parameters are determined based on the number of saturation points, gray mean, and dynamic range of the initial image.

[0008] Based on the imaging conditions of the initial image, the first imaging parameters are obtained according to the real-time imaging parameter mapping table;

[0009] The second imaging parameters are calculated based on the initial image quality.

[0010] The parameter values ​​of the initial imaging parameters to be optimized are calculated based on the first and second imaging parameters.

[0011] Optionally, the calculation of the second parameter based on image quality includes:

[0012] The product of the signal-to-noise ratio of an optical image and its modulation transfer function is used as the evaluation criterion for image quality.

[0013] The integral series at which the evaluation criterion reaches its maximum value is used as the integral series in the second image parameter, while the gain remains unchanged.

[0014] Optionally, the evaluation criterion is SNR×MTF;

[0015] Where SNR is the signal-to-noise ratio of the optical image; MTF is the image modulation transfer function;

[0016] Let k1 be the SNR for a single-stage integral series, then the SNR for an M-stage integral series is: Let k2 be the MTF of the TDICCD space remote sensing camera without image shift. The MTF decrease caused by abnormal image shift along the TDICCD direction is:

[0017]

[0018] When TDICCD operates at an M-order integral series:

[0019]

[0020] Where f is the spatial frequency along the TDI direction corresponding to the camera's focal plane. a is the TDICCD pixel size, and d is the focal length.

[0021] Optionally, determining the initial imaging parameters to be optimized based on the number of saturation points, the average gray level, and the dynamic range of the image includes:

[0022] When the number of image saturation points exceeds a threshold, for anti-diffuse TDICCD:

[0023] If the mean gray value of the image is lower than the lower limit of the image gray value, then keep the integral series unchanged and increase the gain;

[0024] If the average grayscale value of the image is higher than the upper limit of the image grayscale, then keep the integral series unchanged and reduce the gain;

[0025] When the number of image saturation points exceeds the threshold, for TDICCDs that are not resistant to diffusion, the integral series is reduced while the gain remains unchanged.

[0026] Optionally, it also includes:

[0027] When the number of saturated pixels in the image does not exceed the threshold limit:

[0028] If the average gray level of the image is higher than the upper limit of the image gray level, the integral stage is reduced while the gain remains unchanged.

[0029] If the average gray level of the image is lower than the lower limit of the image gray level, the integral stage is reduced while the gain remains unchanged.

[0030] If the number of saturation points in the image does not exceed the threshold limit, and the average gray value of the image is between the lower and upper limits of the image gray value, the initial imaging parameters to be optimized are determined by the dynamic range of the image.

[0031] Optionally, the step of determining the initial imaging parameters to be optimized by judging the dynamic range of the image specifically includes:

[0032] If the dynamic range of the image is less than the dynamic range threshold, then the dynamic range is too narrow. Increasing the integral series will keep the gain unchanged.

[0033] The dynamic range of an image is the difference between the maximum and minimum image brightness.

[0034] Optionally, the calculation of the optimized initial imaging parameters based on the first and second imaging parameters includes:

[0035] Reduce the integral series using the following formula:

[0036]

[0037] Reduce the gain using the following formula:

[0038]

[0039] Where, (η·M) min It is the smaller of η1M1 and η2M2, (η·M) max It is the larger of η1M1 and η2M2, (η·G) min It is the smaller of η1G1 and η2G2, (η·G) max It is the larger of η1G1 and η2G2; M1,G1 and M2,G2 are the integral series and gain in the first and second imaging parameters, respectively; M0,G0 are the integral series and gain in the initial imaging parameters.

[0040] Optionally, it also includes:

[0041] Increase the integral series using the following formula:

[0042]

[0043] Increase the gain using the following formula:

[0044]

[0045] Optionally, the imaging conditions include:

[0046] Imaging conditions include solar elevation angle, reflectance coefficients of target and background features, and meteorological conditions.

[0047] Optionally, the imaging conditions based on the initial image are used to obtain the first parameter according to the real-time imaging parameter mapping table, including:

[0048] Based on the imaging parameter mapping table, the first imaging parameters are obtained according to the illumination conditions, solar elevation angle, reflectance coefficients of target and background objects, and meteorological conditions under the actual conditions at the time of initial image imaging.

[0049] On the other hand, compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0050] 1. Based on the quantitative evaluation of image quality, this invention establishes an adjustment strategy for integral series and gain imaging parameters to determine whether the integral series and gain should be increased or decreased; it records the integral series and gain used based on imaging conditions during imaging, calculates the improved integral series and gain based on the imaging quality evaluation, combines these two types of imaging parameter values, and calculates the final integral series and gain based on the comprehensive benefits, which are used for setting imaging parameters under similar imaging conditions in the next time to improve image quality.

[0051] 2. In this invention, the integral series and gain are calculated based on imaging conditions and image quality after imaging. Considering the decrease in confidence caused by the loss of some information in the above two calculation processes, the integral series and gain are calculated by comprehensively considering imaging conditions and image quality, thereby improving the confidence.

[0052] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0053] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0054] Figure 1 This is a flowchart of the optical remote sensing satellite imaging parameter optimization method based on comprehensive benefits in an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating parameter adjustment in an embodiment of the present invention. Detailed Implementation

[0056] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0057] A specific embodiment of the present invention discloses a method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits, such as... Figure 1 As shown. The method includes the following steps:

[0058] Step S1: Acquire an initial image from a remote sensing satellite, including the imaging conditions and initial imaging parameters set before imaging.

[0059] Specifically, the imaging conditions mainly include the solar elevation angle during imaging, the reflectance coefficients of the target and background features, and meteorological conditions; the parameters include: the integral series M and the gain G.

[0060] The initial parameters, including the integration series M0 and the gain G0, are set empirically based on the imaging conditions during imaging.

[0061] Step S2: Determine the imaging parameters to be optimized in the initial parameters based on the number of saturation points, gray mean, and dynamic range of the image.

[0062] Specifically, Tl is set as the lower limit of grayscale and Th as the upper limit of grayscale as the criteria for judging the brightness of the image. That is, if the grayscale mean P of the image is within the range of [Tl, Th], the image is considered normal. TR is set as the dynamic range threshold of the image. The dynamic range threshold TR is used to determine whether the dynamic range of the image is too narrow. The dynamic range of the image is the difference between the maximum and minimum brightness values ​​of the image. If the dynamic range of the image is greater than the dynamic range threshold, the image is considered to be within the normal dynamic range. If the dynamic range of the image is less than the dynamic range threshold, parameter adjustment is required. TS is set as the criteria for judging whether there are a large number of saturated regions in the image. When the number of saturated points in the image exceeds the TS threshold, it indicates that the image is oversaturated.

[0063] S21: When the number of image saturation points exceeds the threshold, for anti-diffuse TDICCD:

[0064] If the mean gray value of the image is lower than the lower limit of the image gray value, then keep the integral series unchanged and increase the gain;

[0065] If the average grayscale value of the image is higher than the upper limit of the image grayscale, then keep the integral series unchanged and reduce the gain;

[0066] When the number of image saturation points exceeds the threshold, for TDICCDs that are not resistant to diffusion, the integral series is reduced while the gain remains unchanged.

[0067] S22: When the number of image saturation points does not exceed the threshold limit:

[0068] If the average gray level of the image is higher than the upper limit of the image gray level, the integral stage is reduced while the gain remains unchanged.

[0069] If the average gray level of the image is lower than the lower limit of the image gray level, the integral stage is reduced while the gain remains unchanged.

[0070] If the number of saturation points in the image does not exceed the threshold limit, and the average gray value of the image is between the lower and upper limits of the image gray value, the imaging parameters to be optimized are determined by the dynamic range of the image.

[0071] If the dynamic range of the image is less than the dynamic range threshold, then the dynamic range is too narrow. Increasing the integral series will keep the gain unchanged.

[0072] If the image does not fall into either of the two scenarios in steps S31-S32, there is no need to adjust the imaging parameters.

[0073] Step S3: Based on the imaging conditions of the initial image, obtain the first imaging parameters according to the real-time imaging parameter mapping table.

[0074] Based on the imaging parameter mapping table, the first imaging parameters are obtained according to the illumination conditions, solar elevation angle, reflectance coefficients of target and background objects, and meteorological conditions under the actual conditions at the time of initial image imaging.

[0075] The imaging parameter mapping table was provided by the optical remote sensing satellite development department.

[0076] Understandably, the imaging parameter mapping table provided by the optical remote sensing satellite development department is based on imaging parameter values ​​obtained under experimental conditions, and the values ​​are relatively coarse. Before actual imaging, it is necessary to refer to factors such as forecast meteorological data and try to match the imaging parameter mapping table provided by the development department. Since there is a certain gap between meteorological conditions and imaging conditions in the mapping table, the parameters set according to the imaging mapping table may have errors, resulting in poor imaging quality.

[0077] Specifically, the imaging conditions are mainly based on factors such as the solar elevation angle during imaging, the reflectance coefficients of the target and background objects, and meteorological conditions. According to the imaging parameter mapping table provided by the optical remote sensing satellite development department, the corresponding imaging parameters are mapped according to the specific imaging conditions, including the integral series M1 and the gain G1, which are the first imaging parameters.

[0078] Step S4: Calculate the second imaging parameters based on the initial image quality.

[0079] Specifically, after acquiring the initial image from the remote sensing satellite, the second imaging parameters are obtained based on the quality of the initial image.

[0080] Specifically, the product of the signal-to-noise ratio of the optical image and the image modulation transfer function is used as the evaluation standard for image quality; the integral series when the evaluation standard reaches its maximum value is used as the integral series in the second imaging parameter, while the gain remains unchanged.

[0081] The signal-to-noise ratio (SNR) of an optical image reflects its grayscale resolution, while the modulation transfer function (MTF) reflects its spatial resolution. This invention uses SNR×MTF as the comprehensive image quality evaluation standard, and optimizes the setting of imaging parameters based on this standard.

[0082] The evaluation criterion is SNR×MTF;

[0083] Where SNR is the signal-to-noise ratio of the optical image; MTF is the image modulation transfer function;

[0084] Let k1 be the SNR for a single-stage integral series, then the SNR for an M-stage integral series is: Let k2 be the MTF of the TDICCD space remote sensing camera without image shift. The MTF decrease caused by abnormal image shift along the TDICCD direction is:

[0085]

[0086] When TDICCD operates at an M-order integral series:

[0087]

[0088] Where f is the spatial frequency along the TDI direction corresponding to the camera's focal plane. a is the TDICCD pixel size, and d is the focal length.

[0089] The M that maximizes the above formula is the integration series setting for the second imaging parameter. The gain setting does not affect SNR and MTF, so the gain remains unchanged.

[0090] Step S5: Calculate the parameter values ​​of the initial imaging parameters to be optimized based on the first and second imaging parameters.

[0091] Specifically, after acquiring an initial image from an optical remote sensing satellite, the imaging conditions of the initial image and the sensor integration series and gain settings set before imaging are recorded, denoted as M0 and G0. The integration series and gain set according to the imaging conditions based on the initial image, i.e., the first imaging parameters corresponding to the actual imaging conditions, are denoted as M1 and G1. The uncertainty of meteorological conditions is also considered, with a confidence level of η1 (0 < η1 ≤ 1). The second imaging parameters are obtained according to the integration series and gain set based on the quality of the initial image, denoted as M2 and G2, with a confidence level of η2 (0 < η2 ≤ 1). Based on the comprehensive benefits of imaging conditions and image quality, the parameter values ​​of the initial imaging parameters to be optimized are obtained, denoted as M' and G'.

[0092] Reduce the integral series using the following formula:

[0093]

[0094] Reduce the gain using the following formula:

[0095]

[0096] Increase the integral series using the following formula:

[0097]

[0098] Increase the gain using the following formula:

[0099]

[0100] Where, (η·M) min It is the smaller of η1M1 and η2M2, (η·M) max It is the larger of η1M1 and η2M2, (η·G) min It is the smaller of η1G1 and η2G2, (η·G) max It is the larger of η1G1 and η2G2; M1,G1 and M2,G2 are the integral series and gain in the first and second imaging parameters, respectively; M0,G0 are the integral series and gain in the initial imaging parameters.

[0101] When imaging the same target or a target with similar optical reflection characteristics again under similar imaging conditions, the integration stage and gain are set according to M′ and G′.

[0102] This invention provides a method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits. Based on a quantitative assessment of image quality, it establishes an adjustment strategy for integral series and gain imaging parameters, determining whether the integral series and gain should be increased or decreased. It records the integral series and gain used during imaging based on the imaging conditions, calculates improved integral series and gain based on the imaging quality assessment, and combines these two types of imaging parameter values ​​to calculate the final integral series and gain based on comprehensive benefits. This final value is then used for setting imaging parameters under similar imaging conditions in the future, thereby improving image quality.

[0103] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0104] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits, characterized in that, The method includes the following steps: Acquire initial images from remote sensing satellites, obtain the imaging conditions of the initial images, and the initial imaging parameters set before imaging the initial images; the parameters include: the integration series M and the gain G; The imaging parameters to be optimized in the initial parameters are determined based on the number of saturation points, gray mean, and dynamic range of the initial image. Based on the real-time imaging conditions of the initial image, the first imaging parameters are obtained according to the imaging parameter mapping table; The second imaging parameters are calculated based on the initial image quality. The parameter values ​​of the initial imaging parameters to be optimized are calculated based on the first and second imaging parameters, including: Reduce the integral series using the following formula: Reduce the gain using the following formula: Where, (η·M) min It is the smaller of η1M1 and η2M2, (η·M) max It is the larger of η1M1 and η2M2, (η·G) min It is the smaller of η1G1 and η2G2, (η·G) max It is the larger of η1G1 and η2G2; M1,G1 and M2,G2 are the integral series and gain in the first and second imaging parameters, respectively; M0,G0 are the integral series and gain in the initial imaging parameters. Increase the integral series using the following formula: Increase the gain using the following formula:

2. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 1, characterized in that, The second parameter calculated based on image quality includes: The product of the signal-to-noise ratio of an optical image and its modulation transfer function is used as the evaluation criterion for image quality. The integral series at which the evaluation criterion reaches its maximum value is used as the integral series in the second image parameter, while the gain remains unchanged.

3. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 2, characterized in that, The evaluation criterion is SNR×MTF; Where SNR is the signal-to-noise ratio of the optical image; MTF is the image modulation transfer function; Let k1 be the SNR for a single-stage integral series, then the SNR for an M-stage integral series is: Let k2 be the MTF of the TDICCD space remote sensing camera without image shift. The MTF decrease caused by abnormal image shift along the TDICCD direction is: When TDICCD operates at an M-order integral series: Where f is the spatial frequency along the TDI direction corresponding to the camera's focal plane. a is the TDICCD pixel size, and d is the focal length.

4. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 1, characterized in that, The determination of initial imaging parameters to be optimized based on the number of saturation points, average grayscale value, and dynamic range of the image includes: When the number of image saturation points exceeds a threshold, for anti-diffuse TDICCD: If the mean gray value of the image is lower than the lower limit of the image gray value, then keep the integral series unchanged and increase the gain; If the average grayscale value of the image is higher than the upper limit of the image grayscale, then keep the integral series unchanged and reduce the gain; When the number of image saturation points exceeds the threshold, for TDICCDs that are not resistant to diffusion, the integral series is reduced while the gain remains unchanged.

5. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 4, characterized in that, Also includes: When the number of saturated pixels in the image does not exceed the threshold limit: If the average gray level of the image is higher than the upper limit of the image gray level, the integral stage is reduced while the gain remains unchanged. If the average gray level of the image is lower than the lower limit of the image gray level, the integral stage is reduced while the gain remains unchanged. If the number of saturation points in the image does not exceed the threshold limit, and the average gray value of the image is between the lower and upper limits of the image gray value, the initial imaging parameters to be optimized are determined by the dynamic range of the image.

6. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 5, characterized in that, The next step, by determining the dynamic range of the image, is to determine the initial imaging parameters to be optimized, specifically including: If the dynamic range of the image is less than the dynamic range threshold, then the dynamic range is too narrow. Increasing the integral series will keep the gain unchanged. The dynamic range of an image is the difference between the maximum and minimum image brightness.

7. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 1, characterized in that, The imaging conditions include: Imaging conditions include solar elevation angle, reflectance coefficients of target and background features, and meteorological conditions.

8. The method for optimizing optical remote sensing satellite imaging parameters based on comprehensive benefits according to claim 1, characterized in that, The real-time imaging conditions based on the initial image are used to obtain the first parameter according to the imaging parameter mapping table, including: The first imaging parameters are obtained based on the actual lighting conditions, solar elevation angle, reflectance coefficients of the target and background objects, and meteorological conditions during the initial image imaging.

Images