CN112150555A - In-orbit relative radiation calibration method for geosynchronous orbit area-array camera - Google Patents
In-orbit relative radiation calibration method for geosynchronous orbit area-array camera Download PDFInfo
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Abstract
The invention relates to an on-orbit relative radiation calibration method for a geosynchronous orbit area-array camera, and S1, the on-orbit relative radiation calibration of the geosynchronous orbit area-array camera takes each half year as a period; s2, dividing the image into 6 months evenly in a period, selecting images in the order from low cloud cover to high cloud cover every month, and taking the images as statistical samples to participate in the calculation of the scaling coefficient; s3, counting the gray distribution condition of each pixel, and eliminating the influence of a 0 value and a saturated gray value; s4, taking the overall gray distribution of all pixels as the expected value of the gray distribution of each pixel, and drawing an expected gray distribution curve; s5, confirming the actual ranges of the high value area, the middle value area and the low value area; and S6, fitting the gray distribution of each pixel with expected gray distribution data according to the sequence of the high value area, the medium value area and the low value area to obtain the relative radiometric calibration coefficient of the high value area, the medium value area and the low value area of each pixel, wherein the relative radiometric calibration coefficient is used for the relative radiometric correction of the on-orbit data.
Description
Technical Field
The invention belongs to the field of performance test and evaluation of remote sensing satellites, and relates to an in-orbit relative radiation calibration method for a high-resolution four-satellite area-array camera.
Background
The relative radiometric calibration is to calibrate the error of the imaging system by using a high-precision radiometric calibration standard and determine the response relationship between each probe element and the probe element, so that the accuracy of the radiometric calibration standard directly influences the relative radiometric calibration precision. The existing remote sensing relative radiometric calibration method comprises the following steps: the laboratory calibration method using an integrating sphere before satellite transmission is only used in the early stage of orbit because the radiation characteristics of the satellite after transmission change. The infrared remote sensing is generally provided with an on-satellite calibration device, on-satellite calibration can be carried out based on an on-satellite calibration lamp or a diffuse reflection plate after emission, and relative radiometric calibration can be realized by on-orbit field calibration based on ground uniform field, 90-degree yaw calibration or satellite on-orbit statistical calibration without the on-satellite calibration device. The methods are applicable to linear array imaging satellites in synchronous sun orbits.
In-orbit relative radiometric calibration may be different if geosynchronous orbit area array imaging satellites are involved. For example, the high-resolution four-numbered satellite transmitted at the end of 2015 is the geosynchronous orbit satellite with the highest resolution in the world at that time, the resolution is 50m, the imaging is carried out in an area array remote sensing mode, and the number of area array pixels is 10240 × 10240. Laboratory scaling factors were used early in the orbit and have not been updated for more than 4 years to date due to the lack of an effective alternative to in-orbit relative radiometric scaling. Because the on-orbit state of the remote sensor can be gradually deviated from the laboratory state before launching, the longer the time is, the larger the difference is possibly, and therefore the requirement for researching a reasonable on-orbit relative radiometric calibration coefficient to replace the laboratory calibration coefficient is more and more urgent.
According to the experience of the sun synchronous orbit satellite, the calibration method based on the on-orbit data statistics needs more than one hundred thousand and even millions of sampling records for each pixel, so that more comprehensive surface feature representativeness is obtained. The linear array imaging of the sun synchronous track can easily meet the requirement, and taking a high-resolution second panchromatic channel as an example, each scene image has 2.7 ten thousand lines, and 4 scenes of images can meet the quantity requirement of sampling for hundreds of thousands of times. However, the geosynchronous orbit satellite such as the high-grade four satellite cannot push and sweep, and all pixels are imaged simultaneously every time, so that the sampling times are equal to the scenes of the images.
Disclosure of Invention
The technical problem solved by the invention is as follows: the method solves the problems that the geosynchronous orbit satellite such as a high-resolution four-signal geosynchronous orbit satellite lacks an in-orbit relative radiation calibration coefficient generation means and depends on laboratory calibration data before transmission for a long time.
The technical scheme of the invention is as follows: an on-orbit relative radiation calibration method for a geosynchronous orbit area-array camera is realized by the following modes:
s1, dividing the on-orbit relative radiation calibration of the geosynchronous orbit area-array camera into two statistical time intervals by taking each half year as a cycle according to the change rule of the radiation intensity reflected by the earth surface within one year;
s2, dividing the image into 6 months evenly in a period, selecting images in the order from low cloud cover to high cloud cover every month, using the images as statistical samples to participate in the calculation of a scaling coefficient, and only counting 1 scene when a gazing imaging sequence has multiple scenes; the number of the selected images is determined according to the number of gray level samples preset by each pixel;
s3, carrying out gray level statistics on the on-orbit image samples of the geosynchronous orbit area-array camera pixel by pixel, counting the gray level distribution condition of each pixel, and eliminating the influence of a 0 value and a saturated gray level value;
s4, taking the overall gray distribution of all pixels as the expected value of the gray distribution of each pixel, and drawing an expected gray distribution curve;
s5, according to the quantization digit of the image, three gray scales are designated as initial values of the centers of a high-value area, a medium-value area and a low-value area respectively for clustering, and after clustering, the actual ranges of the high-value area, the medium-value area and the low-value area are confirmed;
and S6, fitting the gray distribution of each pixel with expected gray distribution data according to the sequence of the high value area, the medium value area and the low value area to obtain the relative radiometric calibration coefficient of the high value area, the medium value area and the low value area of each pixel, wherein the relative radiometric calibration coefficient is used for the relative radiometric correction of the on-orbit data.
Preferably, the on-orbit image samples related to statistics in S1 are divided into two half-year-long statistical periods with the winter solstice and summer solstice of each year as the boundary.
Preferably, the on-orbit relative radiometric calibration frequency is satisfied twice a year.
Preferably, the number of preset gray scale samples is not less than 6 ten thousand.
Preferably, the image data participating in the single statistics is processed in a distributed and parallel manner.
Preferably, the designated high, middle and low gray scales correspond to 10% -15%, 50% and 85-90% of the saturation value respectively.
Preferably, the median region scaling factor is preferably a least squares model fit.
Preferably, after the fitting of S6, the image element gradation data in the transition region between the high value region, the median region, and the low value region is smoothed.
Preferably, the transition region is 5 adjacent gray scales up and down with a gray scale boundary point between a high value region and a middle value region or between a low value region and the middle value region as a center.
Compared with the prior art, the invention has the beneficial effects that:
the invention can regularly utilize the on-orbit data to generate the relative radiometric calibration coefficient, the relative radiometric calibration coefficient is updated once every half year, and the new coefficient can reflect the current property of the system better than the laboratory calibration coefficient before satellite emission, thereby improving the effect of the relative radiometric calibration.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of probability of single pixel gray scale distribution according to an embodiment of the present invention;
FIG. 3 is a graph of the overall distribution probability of an embodiment of the present invention;
FIG. 4 shows the fitting results of the low value region according to the embodiment of the present invention;
FIG. 5 shows the fitting results of the value regions in the example of the present invention;
FIG. 6 shows the fitting result of the high-value region according to the embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following examples.
An in-orbit relative radiation calibration method for a geosynchronous orbit area-array camera is shown in fig. 1 and comprises the following steps:
(1) determining statistical time periods
Taking the high-resolution four-satellite as an example, the last calibration period is from winter in 2019 to summer in 2020. For convenience, data from 1 month to 6 months of 2020 will be used for illustration as only about one week of time difference in time.
(2) Determining statistical samples
In the first half of 2020, satellite data high score four distributes 212,737 views to users, meaning that each probe obtains 212,737 samples, of which 47306 times in 1-2 months, 31937 times in 3 months, 51740 times in 4 months, 45732 times in 5 months, and 36022 times in 6 months. The case of temporarily adjusting the imaging gain is eliminated first, and the samples using the same gain are retained. Secondly, in order to eliminate the statistical deviation caused by scenes, time phases and clouds as far as possible, only 1 scene is reserved under the condition of staring at multiple scenes of an imaging sequence, and 1 ten thousand scenes are selected from each month according to the sequence of the cloud cover from low to high and are used as statistical samples to participate in the calculation of the scaling coefficient.
(3) Carrying out gray level statistics on pixel by pixel and eliminating the influence of 0 value and saturated gray level value of each pixel
And carrying out gray level statistics on pixel by pixel. The full-color spectrum of the high-resolution fourth satellite is quantized by 10 bits, the quantized value is from 0 to 1023, 0 represents no signal, 1023 represents signal saturation, a large number of images are actually saturated at the gray value of 1022, the quantization information is inaccurate at the moment, the magnitude of the pixel corresponding to the three gray levels jumps remarkably, and therefore, the three gray level statistical values of 0, 1022 and 1023 of each pixel are set to zero together. The total number of the single-pixel samples after the three gray scales are removed is the number of effective samples. The percentage of each gray level is calculated by dividing the number of pixels of each gray level by the number of effective samples, and a gray level distribution probability map of each pixel is drawn, taking the pixels in the first row and the first column as an example, as shown in fig. 2.
(4) Statistical sample population gray distribution
Similarly, the percentage of each gray scale of the whole 10240 × 10240 pixels on the area array is obtained, and a whole gray scale distribution probability map is drawn as shown in fig. 3.
(5) Determining a segmentation threshold
Assigning 870, 511 and 154 which are respectively positioned at 85%, 50% and 15% of saturated gray as initial values of a clustering center to perform clustering calculation, and forming high, medium and low response intervals after clustering, namely high-value region response intervals [904 and 1023], medium-value region response intervals [213 and 903], and low-value region response intervals [0 and 212 ].
(6) Piecewise radiometric scaling coefficient generation
In each response interval of high, medium and low, according to each pixel and the overall gray scale distribution data, a piecewise linear model is adopted to fit the radiation characteristics of the pixels and generate relative radiation scaling coefficients of each corresponding interval, the result is shown in fig. 4, 5 and 6, and the fitting result y in the low-value area is 0.9971x, R is20.996; median region fitting result y-1.0028 x, R20.9857; high value region fitting result y-1.0023 x, R20.9731. It should be noted that, in the actual business statistics, because the luminance interval in the median region has the highest linearity of the pixel response, the least square fitting process should be performed to obtain the radiation correction coefficient. The relative radiometric calibration coefficients may be used for relative radiometric correction of the in-track data.
(7) In the example, because the data consistency is high, the gray scale of the two transition regions is still monotonously changed after the two transition regions are corrected by the scaling coefficients, and the smooth processing is not needed.
The invention has not been described in detail in part in the common general knowledge of a person skilled in the art.
Claims (9)
1. An on-orbit relative radiation calibration method for a geosynchronous orbit area-array camera is characterized by being realized in the following way:
s1, dividing the on-orbit relative radiation calibration of the geosynchronous orbit area-array camera into two statistical time intervals by taking each half year as a cycle according to the change rule of the radiation intensity reflected by the earth surface within one year;
s2, dividing the image into 6 months evenly in a period, selecting images in the order from low cloud cover to high cloud cover every month, using the images as statistical samples to participate in the calculation of a scaling coefficient, and only counting 1 scene when a gazing imaging sequence has multiple scenes; the number of the selected images is determined according to the number of gray level samples preset by each pixel;
s3, carrying out gray level statistics on the on-orbit image samples of the geosynchronous orbit area-array camera pixel by pixel, counting the gray level distribution condition of each pixel, and eliminating the influence of a 0 value and a saturated gray level value;
s4, taking the overall gray distribution of all pixels as the expected value of the gray distribution of each pixel, and drawing an expected gray distribution curve;
s5, according to the quantization digit of the image, three gray scales are designated as initial values of the centers of a high-value area, a medium-value area and a low-value area respectively for clustering, and after clustering, the actual ranges of the high-value area, the medium-value area and the low-value area are confirmed;
and S6, fitting the gray distribution of each pixel with expected gray distribution data according to the sequence of the high value area, the medium value area and the low value area to obtain the relative radiometric calibration coefficient of the high value area, the medium value area and the low value area of each pixel, wherein the relative radiometric calibration coefficient is used for the relative radiometric correction of the on-orbit data.
2. The method of claim 1, wherein: the on-orbit image samples involved in statistics in S1 are divided into two half-year-long statistical time periods with the winter solstice and summer solstice of each year as the boundary.
3. The method of claim 1, wherein: the on-orbit relative radiation calibration frequency is satisfied twice a year.
4. The method of claim 1, wherein: the number of the preset gray level samples is not less than 6 ten thousand.
5. The method of claim 1, wherein: and the image data participating in single statistics adopts distributed parallel processing.
6. The method of claim 1, wherein: the designated high, middle and low gray levels respectively correspond to 10% -15%, 50% and 85-90% of the saturation value.
7. The method of claim 1, wherein: and the median region scaling coefficient is preferably fitted by a least square model.
8. The method of claim 1, wherein: after the fitting of S6, the pixel gray data in the transition region between the high value region, the median region, and the low value region is smoothed.
9. The method of claim 8, wherein: the transition area is 5 adjacent gray scales up and down with the gray scale boundary point of the high value area and the middle value area or the low value area and the middle value area as the center.
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