CN112150555B - On-orbit relative radiation calibration method for geosynchronous orbit area array camera - Google Patents
On-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 of a geosynchronous orbit area array camera, which comprises the following steps of S1, the on-orbit relative radiation calibration of the geosynchronous orbit area array camera takes one cycle per half year; s2, dividing the average of the images into 6 months in one period, and selecting the images from low cloud cover to high in order each month to serve as a statistical sample to participate in calculation of a 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 integral gray distribution of all pixels as the expected value of 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 gray distribution of each pixel with expected gray distribution data according to the sequence of the high value region, the middle value region and the low value region to obtain relative radiation calibration coefficients of the high value region, the middle value region and the low value region of each pixel, wherein the relative radiation calibration coefficients are used for relative radiation correction of on-orbit data.
Description
Technical Field
The invention belongs to the field of remote sensing satellite performance test and evaluation, 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 utilizing a high-precision radiometric calibration reference, and determine the response relation between each probe element and each probe element, so that the accuracy of the radiometric calibration reference directly influences the relative radiometric calibration precision. The existing remote sensing relative radiation calibration method comprises the following steps: the laboratory calibration method using an integrating sphere before satellite transmission is only adopted in the early stage of orbit because the radiation characteristics of the satellite after transmission can change. The infrared remote sensing is generally provided with an on-board calibration device, after the infrared remote sensing is transmitted, on-board calibration can be carried out based on-board calibration lamps or diffuse reflection plates, on-board calibration is not carried out, and relative radiation calibration can be realized through on-orbit field calibration based on a ground uniform field, 90-degree yaw calibration or on-orbit statistics calibration by using satellites. The methods described above are applicable to linear array imaging satellites in solar synchronous orbit.
In-orbit relative radiometric calibration may be different if geosynchronous orbit area array imaging satellites are involved. For example, the high-resolution satellite No. four emitted by 2015 is the geosynchronous orbit satellite with highest resolution in the world at the moment, the resolution is 50m, the imaging is performed by adopting an area array remote sensing mode, and the number of area array pixels is 10240 by 10240. Laboratory scaling factors were employed early in the orbit and have not been updated by more than 4 years to date due to the lack of an efficient on-orbit relative radiometric scaling alternative. Because the remote sensor on-orbit state can deviate from the laboratory state before emission gradually, the longer the time is, the larger the difference is possible, so the demand of researching reasonable on-orbit relative radiation calibration coefficients to replace laboratory calibration coefficients is more and more urgent.
According to the experience of the solar synchronous orbit satellite, the calibration method based on the on-orbit data statistics needs to have a sampling record of more than one hundred thousand or even more than one million pixels, so that a comprehensive ground feature representation is obtained. The linear array imaging of the sun synchronous orbit meets the requirement easily, taking a high-resolution two-number panchromatic channel as an example, each scene image has 2.7 ten thousand lines, and 4 scene images can meet the requirement of the quantity of hundred thousand times of sampling. However, the geosynchronous orbit satellite like the fourth high-resolution satellite cannot push and sweep, and all pixels are imaged at the same time, so that the sampling frequency is equal to the number of scenes of the image.
Disclosure of Invention
The invention solves the technical problems that: the method solves the problem that the class of high-resolution fourth geosynchronous orbit satellites lack in-orbit relative radiation scaling factor generation means and are dependent on laboratory scaling data before transmission for a long time.
The solution of the invention is as follows: an on-orbit relative radiation calibration method of a geosynchronous orbit area array camera is realized by the following steps:
s1, according to the change rule of radiation intensity reflected by the earth surface within one year, the on-orbit relative radiation calibration of the geosynchronous orbit area array camera is divided into two statistical time periods with one period per half year;
s2, dividing the average in a period into 6 months, selecting images in the order from low cloud cover to high monthly, taking the images as statistical samples to participate in the calculation of scaling coefficients, and counting only 1 scene when staring at multiple scenes of an imaging sequence; the number of the selected images is determined according to the number of gray level samples preset for each pixel;
s3, carrying out gray statistics on the on-orbit image sample of the geosynchronous orbit area array camera, counting the gray distribution condition of each pixel, and eliminating the influence of a 0 value and a saturated gray value;
s4, taking the integral gray distribution of all pixels as the expected value of gray distribution of each pixel, and drawing an expected gray distribution curve;
s5, according to the quantization bit number of the image, three gray scales are designated as initial values of centers of a high-value region, a medium-value region and a low-value region respectively for clustering, and after clustering, the actual ranges of the high-value region, the medium-value region and the low-value region are confirmed;
and S6, fitting gray distribution of each pixel with expected gray distribution data according to the sequence of the high value region, the middle value region and the low value region to obtain relative radiation calibration coefficients of the high value region, the middle value region and the low value region of each pixel, wherein the relative radiation calibration coefficients are used for relative radiation correction of on-orbit data.
Preferably, the on-orbit image sample participating in statistics in S1 is divided into two statistical time periods of half a year in length, bounded by winter and summer each year.
Preferably, the on-orbit relative radiation calibration frequency is satisfied twice a year.
Preferably, the preset gray scale sampling number is not less than 6 ten thousand times.
Preferably, the image data participating in the single statistics is processed in distributed parallel.
Preferably, the designated three gray scales of high, medium and low respectively correspond to 10% -15%, 50% and 85% -90% of the saturation value.
Preferably, the median region scaling factor is preferably a least squares model fit.
Preferably, after the S6 fitting, the pixel gradation data in the transition region among the high value region, the median region, and the low value region is smoothed.
Preferably, the transition area is 5 gray scales which are adjacent to each other up and down by taking the gray scale demarcation points of the high value area and the median area or the low value area and the median area as the center.
Compared with the prior art, the invention has the beneficial effects that:
the invention can periodically generate the relative radiometric calibration coefficient by utilizing the on-orbit data, and update the relative radiometric calibration coefficient once every half year, and the new coefficient can reflect the current character of the system more than the laboratory calibration coefficient before satellite transmission, thereby improving the effect of relative radiometric calibration.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the gray scale distribution probability of a single pixel according to an example of the present invention;
FIG. 3 is an overall distribution probability curve for an example of the present invention;
FIG. 4 is a graph showing the result of fitting a low value region according to an embodiment of the present invention;
FIG. 5 is a median region fitting result in an embodiment of the present invention;
FIG. 6 is a graph showing the result of fitting the high value regions according to the embodiment of the present invention.
Detailed Description
The invention is further illustrated below with reference to examples.
An on-orbit relative radiation calibration method of a geosynchronous orbit area array camera is shown in fig. 1, and comprises the following steps:
(1) Determining a statistical period of time
Taking the high-resolution satellite number four as an example, the last calibration period is 2019 winter to 2020 summer. Since the time varies by only about one week, this is illustrated by the data from 1 month to 6 months in 2020 for convenience.
(2) Determining statistical samples
In the last half of 2020, satellite data are distributed 212,737 shots to users in a total way, meaning that each probe element obtains 212,737 samples, wherein 1-2 months 47306 times, 3 months 31937 times, 4 months 51740 times, 5 months 45732 times, 6 months distribute 36022 times. The condition of temporarily adjusting the imaging gain is first removed, and samples using the same gain are retained. Secondly, in order to eliminate statistical deviation caused by scenes, time phases and clouds as much as possible, only 1 scene is reserved under the condition of staring at multiple scenes of an imaging sequence, 1 ten thousand scenes are selected from low to high in cloud cover in each month, and the scenes are used as statistical samples to participate in calculation of scaling coefficients.
(3) Carrying out gray statistics on pixel by pixel, and eliminating the influence of the 0 value and the saturated gray value of each pixel
And carrying out gray statistics pixel by pixel. The full-color spectrum of the high-resolution four-number satellite adopts 10-bit quantization, the quantized value is from 0 to 1023,0 to represent no signal, 1023 represents signal saturation, a large number of images are actually saturated at the gray value 1022, the quantized information at the moment is inaccurate, the pixel order jump corresponding to the three gray scales is obvious, and therefore three gray scale statistical values of 0, 1022 and 1023 of each pixel are set to zero. The total number of single pixel samples after the three gray scales are removed is the effective sample number. The gray scale distribution probability map of each pixel is drawn by dividing the number of each gray scale pixel by the number of effective samples, and the first row and first column pixels are taken as an example, as shown in fig. 2.
(4) Statistics of sample population gray scale distribution
As above, the percentage of each gray level of the whole 10240×10240 pixels on the area array is obtained, and the whole gray level distribution probability map is drawn as shown in fig. 3.
(5) Determining segmentation threshold
The 870, 511 and 154 respectively positioned at 85%, 50% and 15% of the saturated gray scale are designated as initial values of a clustering center to perform clustering calculation, and three response intervals of high, medium and low are formed after clustering, namely a high value area response interval [904, 1023], a median area response interval [213, 903] and a low value area response interval [0, 212].
(6) Segmented radiation scaling coefficient generation
Fitting pixel radiation characteristics in each response interval of high, medium and low by adopting a piecewise linear model according to gray distribution data of each pixel and the whole, generating relative radiation calibration coefficients of the corresponding intervals, and as shown in figures 4, 5 and 6, fitting results y=0.9971 x and R in the low-value region 2 =0.996; median region fitting result y=1.0028x，R 2 = 0.9857; high value region fitting result y=1.0023x, r 2 = 0.9731. It should be noted that in the actual business statistics, since the linearity of the response of the pixels is highest in the brightness interval of the median region, the radiation correction coefficient should be obtained by performing least square fitting. The relative radiation scaling factor may be used for relative radiation correction of the on-track data.
(7) In the embodiment, because the data consistency is higher, the gray level of the two transition areas is still monotonous after the calibration coefficient correction, and no smoothing processing is needed.
The invention is not described in detail in part as being common general knowledge to a person skilled in the art.
Claims (9)
1. An on-orbit relative radiation calibration method of a geosynchronous orbit area array camera is characterized by being realized by the following steps:
s1, according to the change rule of radiation intensity reflected by the earth surface within one year, the on-orbit relative radiation calibration of the geosynchronous orbit area array camera is divided into two statistical time periods with one period per half year;
s2, dividing the average in a period into 6 months, selecting images in the order from low cloud cover to high monthly, taking the images as statistical samples to participate in the calculation of scaling coefficients, and counting only 1 scene when staring at multiple scenes of an imaging sequence; the number of the selected images is determined according to the number of gray level samples preset for each pixel;
s3, carrying out gray statistics on the on-orbit image sample of the geosynchronous orbit area array camera, counting the gray distribution condition of each pixel, and eliminating the influence of a 0 value and a saturated gray value;
s4, taking the integral gray distribution of all pixels as the expected value of gray distribution of each pixel, and drawing an expected gray distribution curve;
s5, according to the quantization bit number of the image, three gray scales are designated as initial values of centers of a high-value region, a medium-value region and a low-value region respectively for clustering, and after clustering, the actual ranges of the high-value region, the medium-value region and the low-value region are confirmed;
and S6, fitting gray distribution of each pixel with expected gray distribution data according to the sequence of the high value region, the middle value region and the low value region to obtain relative radiation calibration coefficients of the high value region, the middle value region and the low value region of each pixel, wherein the relative radiation calibration coefficients are used for relative radiation correction of on-orbit data.
2. The method according to claim 1, characterized in that: the on-orbit image sample participating in statistics in S1 is divided into two statistical periods of half-year length bounded by winter and summer each year.
3. The method according to claim 1, characterized in that: the on-orbit relative radiation calibration frequency meets twice a year.
4. The method according to claim 1, characterized in that: the preset gray level sampling number is not less than 6 ten thousand times.
5. The method according to claim 1, characterized in that: the image data participating in the single statistics is processed in a distributed parallel manner.
6. The method according to claim 1, characterized in that: the designated three gray scales of high, medium and low respectively correspond to 10% -15%, 50% and 85% -90% of the saturation value.
7. The method according to claim 1, characterized in that: the relative radiometric scaling coefficients of the median region are fitted by a least squares model.
8. The method according to claim 1, characterized in that: after the S6 fitting, the pixel gradation data in the transition region between the high value region, the middle value region, and the low value region is smoothed.
9. The method according to claim 8, wherein: the transition area is 5 gray scales which are adjacent up and down by taking the gray scale demarcation points of the high value area and the median area or the low value area and the median area as the center.
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