CN113379621B - Optical remote sensing satellite relative radiation correction method based on statistical sample weighting - Google Patents
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Abstract
The application discloses an optical remote sensing satellite relative radiation correction method based on statistical sample weighting, which comprises the following steps: collecting 0-level data as a statistics sample corresponding to the nth month according to a preset starting time, a preset ending time and a preset statistics period, carrying out histogram statistics and matching according to the statistics sample to obtain a first gray level lookup table, and correcting a remote sensing satellite image corresponding to the nth month according to the first gray level lookup table to obtain an initial corrected image; judging whether stripe noise exists in a lap joint area in the image after initial correction; if yes, adjusting preset starting time, ending time and statistical period; and correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, ending time and statistical period to obtain a corrected image. The technical problem that the correction effect of the acquired image is poor when the state of the detector jumps in the prior art is solved.
Description
Technical Field
The application relates to the technical field of aerospace optical remote sensing image processing, in particular to an optical remote sensing satellite relative radiation correction method based on statistical sample weighting.
Background
The optical remote sensing satellite camera generally adopts a plurality of full-color multispectral TDICCDs for reflector splicing, the CCD reflector splicing is to divide an image plane into a plurality of image planes which are spatially separated by utilizing a light splitting reflector, a plurality of CCDs can be arranged on each image plane in a staggered way, and pixels at the boundary of two staggered adjacent CCDs meet the overlapping requirement, so that an equivalent large-view-field detector is formed. However, the mirror splicing method is adopted to generate vignetting phenomenon in the overlap region, so that the radiation energy of the overlap region gradually decreases, the radiation response consistency among pixels is obviously reduced, and the stripe noise of the overlap region is formed, thereby affecting the remote sensing satellite image. Therefore, in order to avoid the influence of the stripe noise of the lap joint area on the optical remote sensing satellite image, the remote sensing satellite needs to be subjected to radiation correction.
At present, common methods for solving the stripe noise of the lap joint area comprise a table look-up method, a progressive scanning method, a function approximation method and the like. See application number 2017106446289, a remote sensing satellite relative radiometric calibration processing method based on big data statistics, describes a method for carrying out relative radiometric correction on data of 0 level based on a table look-up method, and the specific process of the method is as follows: carrying out histogram statistics on the data by adopting a remote sensing satellite 0 and stripe data total statistics method, then carrying out probability calculation according to the histogram statistics, carrying out boundary processing on the DN value which is 0 and the DN value which is the maximum quantization value, and then matching the histogram and the statistical probability to form a lookup table; and carrying out relative radiation correction on the data of the 0 level according to the lookup table. However, the method described in this patent is to count the normalized histogram for each probe element over a period of time, and sum and normalize the probe element histograms for all non-overlapping regions as the desired histogram. The histogram of each probe element of the overlap region and the non-overlap region is matched with the expected histogram to generate a lookup table. The statistical period is typically two months. Taking the data of 0 th data of the upper half month of the N-1 th month to the lower half month of the N month as a statistical sample to carry out histogram statistics and matching, wherein a gray level lookup table at the calculation position is used for correcting the data of the upper half month of the N month; taking the data of 0 th data of the first half month from the second half month of the N-1 month to the first half month of the N+1th month as a statistical sample to carry out histogram statistics and matching, and correcting the data of the second half month of the N month by using the calculated gray level lookup table; and so on. For the slow change of the imaging state of the detector, the method has good correction effect under the condition that no concussion jump exists. However, when the imaging states of the detectors jump at a certain frequency within the statistical period time, the generated lookup table has poor correction effect on the images acquired when the states of the detectors jump.
Disclosure of Invention
The technical problem that this application solved is: the correction effect of the acquired images when the state of the detector jumps in the prior art is poor. According to the scheme provided by the embodiment of the application, when stripe noise exists in a lap zone in an initially corrected image obtained by carrying out radiation correction on a remote sensing satellite image corresponding to the nth month according to preset starting time, preset ending time and preset statistical period, the problem that the correction effect of a generated lookup table on the image acquired when the state of a detector jumps in a certain frequency is poor when the state of the detector jumps in the statistical period is avoided.
In a first aspect, an embodiment of the present application provides a method for correcting relative radiation of an optical remote sensing satellite based on weighting of a statistical sample, where the method includes:
collecting 0-level data as a statistics sample corresponding to the nth month according to a preset starting time, a preset ending time and a preset statistics period, carrying out histogram statistics and matching according to the statistics sample to obtain a first gray level lookup table, and correcting a remote sensing satellite image corresponding to the nth month according to the first gray level lookup table to obtain an initial corrected image, wherein N is 1, 12;
judging whether stripe noise exists in a lap joint area in the initially corrected image;
if the preset starting time, the preset ending time and the preset statistical period are met, the preset starting time, the preset ending time and the preset statistical period are adjusted until the adjusted starting time, the adjusted ending time and the adjusted statistical period meet preset requirements;
and correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, ending time and statistical period to obtain a corrected image.
According to the scheme provided by the embodiment of the application, when the radiation correction is performed on the remote sensing satellite image corresponding to the nth month according to the preset starting time, the preset ending time and the preset counting period, and the initial corrected image has stripe noise in the overlapping area, the problem that the correction effect of the generated lookup table on the acquired images when the detector state jumps is poor when the detector state jumps at a certain frequency is avoided by adjusting the preset starting time, the preset ending time and the preset counting period until the adjusted starting time, the adjusted ending time and the adjusted counting period meet preset requirements and then correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, the adjusted ending time and the adjusted counting period is solved.
Optionally, collecting the data of 0 level as the statistics sample corresponding to the nth month according to the preset starting time, the preset ending time and the preset statistics period includes:
collecting data of 0 steps of the upper half month of the N-1 th month and two months from the upper half month to the lower half month of the N month as statistical samples of the upper half month of the N month;
collecting data of 0 th data from the lower half month of the N-1 th month to the upper half month of the N+1 th month as a statistical sample of the lower half month of the N.
Optionally, adjusting the preset starting time, the preset ending time and the preset statistical period until the adjusted starting time, ending time and statistical period meet preset requirements includes:
the preset statistical period is kept unchanged, the preset starting time and the preset ending time are adjusted, a statistical sample is acquired again according to the adjusted starting time and the adjusted ending time, a second gray level lookup table is obtained through recalculation according to the acquired statistical sample, the corrected image is obtained through relative radiation correction on the image according to the second gray level lookup table, the jump direction of the detector is determined according to the correction effect, and the starting time and the ending time after the last adjustment are determined until the correction effect determined according to the second gray level lookup table meets the preset requirement;
and adjusting the preset statistical period according to the jump direction of the detector and a preset adjustment step length, and recalculating to obtain a third gray level lookup table according to the adjusted statistical period until the correction effect determined according to the third gray level lookup table meets the preset requirement, so as to determine the statistical period after the last adjustment.
Optionally, keeping the preset statistical period unchanged, and adjusting the preset starting time and the preset ending time includes:
and keeping the preset statistical period unchanged, and moving the preset starting time and the preset ending time forward or backward according to a preset adjusting step.
Optionally, if the preset start time and the preset end time are shifted backward, determining the detector jump direction according to the second gray level lookup table includes:
correcting the remote sensing satellite image according to the second gray level lookup table to obtain a corrected image, and comparing the corrected image with the remote sensing satellite image to determine a correction effect;
judging whether the quality of the corrected image is higher than the quality of the remote sensing satellite image or not according to the correction effect;
if yes, the detector jump direction is backward jump, otherwise, the detector jump direction is forward jump.
Optionally, adjusting the preset statistical period according to the detector jump direction and a preset adjustment step length includes:
if the jump direction of the detector is backward jump, keeping the starting time after the last adjustment unchanged, and adjusting the preset statistical period according to the preset adjustment step length by shifting the ending time after the last adjustment.
Drawings
Fig. 1 is a flow chart of an optical remote sensing satellite relative radiation correction method based on statistical sample weighting provided in an embodiment of the present application;
fig. 2 is a flow chart for adjusting a preset start time, a preset end time and a preset statistical period according to an embodiment of the present application;
FIG. 3a is a schematic diagram of a conventional radiation corrected remote sensing satellite image according to an embodiment of the present application;
FIG. 3b is a schematic view of an optical remote sensing satellite with relative radiation corrected based on statistical sample weighting according to an embodiment of the present application;
FIG. 4a is a schematic diagram of a conventional radiation corrected remote sensing satellite image according to an embodiment of the present application;
fig. 4b is a schematic view of an optical remote sensing satellite image after relative radiation correction based on statistical sample weighting according to an embodiment of the present application.
Detailed Description
In the solutions provided by the embodiments of the present application, the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The following describes in further detail an optical remote sensing satellite relative radiation correction method based on statistical sample weighting according to the embodiments of the present application with reference to the accompanying drawings, and a specific implementation manner of the method may include the following steps (the method flow is shown in fig. 1):
step 101, collecting data of 0 steps as statistics samples corresponding to the nth month according to preset starting time, preset ending time and preset statistics period, carrying out histogram statistics and matching according to the statistics samples to obtain a first gray level lookup table, and correcting remote sensing satellite images corresponding to the nth month according to the first gray level lookup table to obtain initial corrected images, wherein N is 1, 12.
Specifically, in the solution provided in the embodiment of the present application, there are various ways to collect 0 data as the statistics sample corresponding to the nth month according to the preset start time, the preset end time and the preset statistics period, and one of them is taken as an example to be described below.
In one possible implementation manner, collecting the data of 0 steps according to the preset starting time, the preset ending time and the preset statistics period as the statistics sample corresponding to the nth month includes: collecting data of 0 steps of the upper half month of the N-1 th month and two months from the upper half month to the lower half month of the N month as statistical samples of the upper half month of the N month; collecting data of 0 th data from the lower half month of the N-1 th month to the upper half month of the N+1 th month as a statistical sample of the lower half month of the N.
Specifically, taking 0-stage data of the upper half month of the N-1 month to the lower half month of the N month as a statistical sample to carry out histogram statistics and matching, and correcting the data of the upper half month of the N month by using the calculated gray level lookup table; and carrying out histogram statistics and matching by taking the data of the 0 th data from the lower half month of the N-1 month to the upper half month of the N+1 month as a statistics sample, and correcting the data of the lower half month of the N month by using the calculated gray level lookup table, and the like. If N is 1, then N-1 represents month 12 of the previous year.
Step 102, determining whether stripe noise exists in the overlapping area in the initially corrected image.
Specifically, after correcting the remote sensing satellite image corresponding to the nth month according to the first gray level lookup table to obtain an initial corrected image, it is further required to determine whether stripe noise exists in a lap joint area in the initial corrected image. In the solution provided in the embodiment of the present application, there are various ways to determine that the stripe noise exists in the overlap area, for example, whether the dark stripe exists in the overlap area is determined.
Step 103, if yes, adjusting the preset starting time, the preset ending time and the preset statistical period until the adjusted starting time, ending time and statistical period meet preset requirements.
In the solution provided in the embodiment of the present application, if stripe noise exists in the overlap area, the preset start time, the preset end time and the preset statistical period are adjusted. Specifically, there are various ways to adjust the preset start time, the preset end time and the preset statistical period, and one of them will be described as an example.
In one possible implementation manner, adjusting the preset start time, the preset end time, and the preset statistics period until the adjusted start time, end time, and statistics period meet preset requirements includes:
the preset statistical period is kept unchanged, the preset starting time and the preset ending time are adjusted, a statistical sample is acquired again according to the adjusted starting time and the adjusted ending time, a second gray level lookup table is obtained through recalculation according to the acquired statistical sample, the corrected image is obtained through relative radiation correction on the image according to the second gray level lookup table, the jump direction of the detector is determined according to the correction effect, and the starting time and the ending time after the last adjustment are determined until the correction effect determined according to the second gray level lookup table meets the preset requirement;
and adjusting the preset statistical period according to the jump direction of the detector and a preset adjustment step length, and recalculating to obtain a third gray level lookup table according to the adjusted statistical period until the correction effect determined according to the third gray level lookup table meets the preset requirement, so as to determine the statistical period after the last adjustment.
Further, in one possible implementation manner, keeping the preset statistical period unchanged, adjusting the preset start time and the preset end time includes: and keeping the preset statistical period unchanged, and moving the preset starting time and the preset ending time forward or backward according to a preset adjusting step.
Further, in one possible implementation manner, if the preset start time and the preset end time are shifted backward, determining the detector jump direction according to the second gray level lookup table includes:
correcting the remote sensing satellite image according to the second gray level lookup table to obtain a corrected image, and comparing the corrected image with the remote sensing satellite image to determine a correction effect;
judging whether the quality of the corrected image is higher than the quality of the remote sensing satellite image or not according to the correction effect;
if yes, the detector jump direction is backward jump, otherwise, the detector jump direction is forward jump.
Further, in one possible implementation manner, the adjusting the preset statistical period according to the detector jump direction and a preset adjustment step length includes:
if the jump direction of the detector is backward jump, keeping the starting time after the last adjustment unchanged, and adjusting the preset statistical period according to the preset adjustment step length by shifting the ending time after the last adjustment.
In order to facilitate understanding the foregoing adjustment process of the preset start time, the preset end time, and the preset statistical period, the adjustment process will be briefly described below, referring to fig. 2, and specific steps are as follows:
the first step: and (5) optimizing and configuring sample weight. When stripe noise appears in the image overlap region after radiation correction, firstly keeping the statistical period unchanged, advancing or retreating to count the starting date and the ending date, and judging the jump direction of the state. If the correction effect of the gray level lookup table generated by the backward statistical start date and the finish date is good, the state is backward jump; otherwise, the state is a forward jump. Selecting a group of statistical start date and end date with optimal correction effect, and marking as [ T ] S ,T E ]. The technical scheme is described by taking backward jump as an example.
And a second step of: and optimally configuring the sample weight and the sample number. In the first step, if it has been determined that the state is backward jump, then backward statistics is performed on the end date, [ T ] S ,T E +15]、[T S ,T E +30]、[T S ,T E +45]… …. Selecting a group of statistical start date and end date T with best correction effect S ,T E +N]As a final statistical period.
And 104, correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, ending time and statistical period to obtain a corrected image.
Further, in order to facilitate understanding of the effect of the scheme provided in the embodiments of the present application, the following description will take the scene one 01 star data as an example.
For example, in the imaging data of the first 01 star of the altitude of 2019, 5 months to 7 months, dark stripes exist in the overlapping area of the image after the radiation correction of 165-scene data. The verification data selects the 165 scene question data and 100 scene quality data imaged during the period of time. Referring to fig. 3a, a remote sensing satellite image after existing radiation correction is provided in an embodiment of the present application; referring to fig. 3b, a remote sensing satellite image after relative radiation correction of an optical remote sensing satellite based on statistical sample weighting is provided in an embodiment of the present application.
As can be seen from fig. 3a and fig. 3b, after the image with stripe noise in the 165-scene overlap region is processed according to the scheme provided by the embodiment of the present application, approximately 75% of stripe noise of the image radiation is completely removed, approximately 24% of stripe noise in the image overlap region is obviously reduced, and 1% of stripe noise in the image overlap region is slightly reduced; in 100-view high-quality images, the quality of all images is not reduced. As can be seen from fig. 3a and 3b, when the existing radiation correction method is used to correct the remote sensing satellite, the overlapping area of the full-color image will have occasional banding noise; by adopting the scheme provided by the embodiment of the application, the stripe noise of the lap-joint area can be reduced or even removed, and the original high-quality image is free from quality degradation, so the scheme provided by the embodiment of the application can effectively reduce or even remove the stripe noise of the lap-joint area of the full-color image.
For example, in the imaging data of the first 01 st and the second 01 st, there is a color difference between two taps in the first slice of the near infrared band of 20-scene images. The verification data selects the 20-scene image data and 20-scene high-quality images imaged in the period of time. Referring to fig. 4a, a remote sensing satellite image after existing radiation correction is provided in an embodiment of the present application; referring to fig. 4b, a remote sensing satellite image after relative radiation correction of an optical remote sensing satellite based on statistical sample weighting is provided in an embodiment of the present application.
According to fig. 4a and fig. 4b, the corrected image is obtained by performing radiation correction processing on the 20-scene remote sensing satellite image according to the scheme provided by the embodiment of the present application, wherein 18 scene differences in the 20 scenes are completely removed, and the 2 scene differences are obviously reduced. The quality of the 20-scene high-quality images is not reduced. As can be seen from fig. 4a and fig. 4b, when the existing radiation correction method is used for correcting the remote sensing satellite, the image may have sporadic inter-chip or inter-tap chromatic aberration; by adopting the technology, the chromatic aberration among the inter-chip or intra-chip taps can be reduced or even removed, and the original high-quality image has no quality degradation. By adopting the scheme provided by the embodiment of the application, the chromatic aberration between full-color multispectral image slices or intra-slice taps can be effectively reduced or even removed.
According to the scheme provided by the embodiment of the application, when the radiation correction is performed on the remote sensing satellite image corresponding to the nth month according to the preset starting time, the preset ending time and the preset counting period, and the initial corrected image has stripe noise in the overlapping area, the problem that the correction effect of the generated lookup table on the acquired images when the detector state jumps is poor when the detector state jumps at a certain frequency is avoided by adjusting the preset starting time, the preset ending time and the preset counting period until the adjusted starting time, the adjusted ending time and the adjusted counting period meet preset requirements and then correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, the adjusted ending time and the adjusted counting period is solved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. An optical remote sensing satellite relative radiation correction method based on statistical sample weighting is characterized by comprising the following steps:
collecting 0-level data as a statistics sample corresponding to the nth month according to a preset starting time, a preset ending time and a preset statistics period, carrying out histogram statistics and matching according to the statistics sample to obtain a first gray level lookup table, and correcting a remote sensing satellite image corresponding to the nth month according to the first gray level lookup table to obtain an initial corrected image, wherein N is E [1,12];
judging whether stripe noise exists in a lap joint area in the initially corrected image;
if the preset starting time, the preset ending time and the preset statistical period are met, the preset starting time, the preset ending time and the preset statistical period are adjusted until the adjusted starting time, the adjusted ending time and the adjusted statistical period meet preset requirements; the method specifically comprises the following steps: the preset statistical period is kept unchanged, the preset starting time and the preset ending time are adjusted, a statistical sample is acquired again according to the adjusted starting time and the adjusted ending time, a second gray level lookup table is obtained through recalculation according to the acquired statistical sample, the corrected image is obtained through relative radiation correction on the image according to the second gray level lookup table, the jump direction of the detector is determined according to the correction effect, and the starting time and the ending time after the last adjustment are determined until the correction effect determined according to the second gray level lookup table meets the preset requirement; adjusting the preset statistical period according to the jump direction of the detector and a preset adjustment step length, and recalculating to obtain a third gray level lookup table according to the adjusted statistical period until the correction effect determined according to the third gray level lookup table meets the preset requirement, and determining the statistical period after the last adjustment;
and correcting the remote sensing satellite image corresponding to the nth month according to the adjusted starting time, ending time and statistical period to obtain a corrected image.
2. The method of claim 1, wherein collecting the data of 0 level as the corresponding statistical sample for the nth month according to the preset start time, the preset end time, and the preset statistical period, comprises:
collecting data of 0 steps of the upper half month of the N-1 th month and two months from the upper half month to the lower half month of the N month as statistical samples of the upper half month of the N month;
collecting data of 0 th data from the lower half month of the N-1 th month to the upper half month of the N+1 th month as a statistical sample of the lower half month of the N.
3. The method of claim 1, wherein the adjusting the preset start time and preset end time, while maintaining the preset statistical period, comprises:
keeping the preset statistical period unchanged, and moving the preset starting time and the preset ending time forward or backward according to a preset adjustment step length.
4. The method of claim 3, wherein determining a detector transition direction based on the second gray level lookup table if the preset start time and the preset end time are shifted backward comprises:
correcting the remote sensing satellite image according to the second gray level lookup table to obtain a corrected image, and comparing the corrected image with the remote sensing satellite image to determine a correction effect;
judging whether the quality of the corrected image is higher than the quality of the remote sensing satellite image or not according to the correction effect;
if yes, the detector jump direction is backward jump, otherwise, the detector jump direction is forward jump.
5. The method of claim 4, wherein adjusting the preset statistical period according to the detector jump direction and a preset adjustment step size comprises:
if the jump direction of the detector is backward jump, keeping the starting time after the last adjustment unchanged, and adjusting the preset statistical period according to the preset adjustment step length by shifting the ending time after the last adjustment.
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| CN106871925A (en) * | 2016-12-29 | 2017-06-20 | 中国资源卫星应用中心 | A kind of remote sensing satellite relative radiometric calibration processing method of in-orbit comprehensive dynamic adjustment |
| CN107562791A (en) * | 2017-08-01 | 2018-01-09 | 中国资源卫星应用中心 | A kind of remote sensing satellite relative radiometric calibration processing method based on big data statistics |
| CN112529807A (en) * | 2020-12-15 | 2021-03-19 | 北京道达天际科技有限公司 | Relative radiation correction method and device for satellite image |
| CN112819729A (en) * | 2021-02-23 | 2021-05-18 | 中国科学院空天信息创新研究院 | Image correction method and device, computer storage medium and equipment |
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