CN105069313A - Phase nonlinear resampling and knife-edge fitting based in-orbit MTF (Modulation Transfer Function) estimation method - Google Patents
Phase nonlinear resampling and knife-edge fitting based in-orbit MTF (Modulation Transfer Function) estimation method Download PDFInfo
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Abstract
The application discloses a phase nonlinear resampling and knife-edge fitting based in-orbit MTF (Modulation Transfer Function) estimation method, comprising the steps of: obtaining a remote sensing check image with a target, with an inclined knife-edge, loaded in an MTF of an in-orbit satellite, and selecting an along-orbit knife-edge region and a cross-orbit knife-edge region in a target region in the remote sensing check image from the remote sensing check image; removing rows of a non-target region for the selected along-orbit knife-edge region to obtain even rows of along-orbit knife-edge region data; and at the same time, removing columns of the non-target region for the selected cross-orbit knife-edge region to obtain even columns of cross-orbit knife-edge region data. The method solves the problem of imaging quality reduction caused by noise and fuzziness formed by vibration and the like of an imaging system, atmospheric disturbance and a satellite platform when the target with the inclined knife-edge is arranged and the MTF of the in-orbit satellite is obtained with a phase sampling method.
Description
Technical field
The present invention relates to spatial remotely sensed imaging field, when being specifically related to optical satellite remote sensor radiometric calibration site, adopting and lay the method that satellite MTF (ModulationTransferFunction, i.e. modulation transfer function) in-orbit estimated by inclination sword limit target.
Background technology
High-resolution remote sensing image, as a kind of main information carrier, has important strategic importance in the every field such as national defence, mapping.In actual imaging system, picture quality is often subject to many-sided impact.The pore size of such as optical imaging system, diffraction effect and aberration; The sensitivity of remote sensor, resolution and nonlinear response; The noise of electronic system; For the remote sensing images of satellite in orbit, be also subject to the impact of the atmospheric extinction such as atmospheric turbulence and scattering, the simultaneously vibrations of satellite, remote sensor detection performance all can cause the decline of image quality in the degeneration of vacuum and the change of orbit altitude and attitude.MTF is the important comprehensive evaluation index of remote optical sensing imaging system, is also simultaneously the important objective parameter of picture quality one of promoting and evaluating.At present; ground calibration obtains the MTF method of satellite in orbit remotely sensed image; the target on inclination sword limit is manually laid in many employings; according to the inclination sword limit phase sample method described in ISO12233 specification; but the method does not eliminate the impact of phase noise well, calculating in the situation such as bending is also existed and out of true for sword limit.Therefore, the MTF of satellite in orbit optical imaging system how is accurately measured by the method for ground calibration, it is the important means evaluating camera serviceability in-orbit, also be that MTFC (ModulationTransferFunctionCompensation is carried out on ground, namely transport function compensates) important evidence that processes is the key issue ensureing that remote sensing image quality is urgently to be resolved hurrily.
As shown in Figure 1, described inclination sword limit phase sample method core utilizes sword limit to tilt certain angle, obtain more data fitting by phase sample method and go out ESF (EdgeSpreadFunction, i.e. edge-spread function), and then obtain LSF (LineSpreadFunction by differential, i.e. line spread function), then obtain along rail and the MTF wearing rail respectively by discrete Fourier transformation, the MTF of final synthesis two dimension.
The key step that described inclination sword limit phase sample method extracts MTF is:
Step 101, chooses the ROI (RegionofInterest, i.e. area-of-interest) along rail direction in the image that image device collects;
Step 102, uses the OECF (OpticalElectricityConversionFunction, i.e. opto-electronic conversion function) of sensor to carry out linearization process (if having Banded improvement) to view data;
Step 103, calculates the discrete differential of ESF on line direction, i.e. LSF;
Step 104, calculates the centre of gravity place of often row LSF in selected sword edge regions, as the marginal position of every row ESF, and does linear fit to all marginal positions;
Step 105, for along rail sword edge regions, the line number that in zoning, each phase cycling comprises, removes unnecessary line number and makes selected areas line number comprise the phase cycling of integer multiple;
All pixels in selected sword edge regions are projected to the first row of sword edge regions by step 106 along the rectilinear direction that matching obtains;
Step 107, the inverse getting phase cycling line number be interval to this row resampling, geometric mean is done to represent the data of this sampling interval to all data points dropped in same sampling interval, thus obtains average up-sampling ESF;
Step 108, can obtain average up-sampling LSF to average up-sampling ESF differentiate;
Step 109, average up-sampling LSF is done Hamming window filtering process, DFT (DiscreteFourierTransformation, i.e. discrete Fourier transformation) is done to Output rusults and obtains OTF (OpticalTransferFunction, i.e. optical transfer function);
Step 110, what the normalized value of the mould of OTF was imaging system wears rail direction MTF;
Step 111, in like manner, by step 1 to step 10, can make the MTF that same process obtains along rail direction to the sword limit of wearing rail direction, and then by along rail MTF with wear the MTF matrix that rail MTF obtains two dimension.
Summary of the invention
The subject matter that the application solves is to provide the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit, to solve at laying inclination sword limit target, and then when adopting phase sample method to obtain satellite in orbit MTF, the noise that the vibration etc. of imaging system, atmospheric disturbance and satellite platform is formed and the fuzzy image quality that causes decline.
In order to solve the problems of the technologies described above, the invention discloses the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit, its feature comprises:
Obtain described satellite and be loaded into remote sensing calibration image containing inclination sword limit target in-orbit in MTF, in the targeting regions utilizing described remote sensing images to choose wherein respectively along rail sword edge regions and wear rail sword edge regions;
To the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data; Meanwhile, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtain even column and wear rail sword edge regions data;
To described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data;
To described along rail sword limit interpolation edge-diffusion data with wear rail sword limit interpolation edge-diffusion data and carry out Fermi's matching respectively, obtain the matching after matching respectively and wear rail sword limit interpolation edge-diffusion data along rail sword limit interpolation edge-diffusion data and matching;
Wear rail sword limit interpolation edge-diffusion data to described matching along rail sword limit interpolation edge-diffusion data and matching to be averaged respectively resampling, obtain respectively wearing rail sword limit edge-diffusion data without interpolation along rail sword limit edge-diffusion data with without interpolation;
To described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data;
To described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF;
To described along rail MTF and wear rail MTF integrate draw two dimension MTF.
Preferably, wherein, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, be further:
To described even number line along rail sword edge regions data and even column wear rail sword edge regions data formed two-dimensional matrix data respectively according to pixels value size resequence and be mapped to one-dimension array.
Preferably, wherein, described Fermi fits to:
Wherein, α represents the amplitude of Fermi function; β represents the coordinate in the corresponding x-axis in Fermi function center; γ represents the steepness of Fermi function; D represents the coordinate in the corresponding y-axis in the dark space of Fermi function; α, β, γ and D obtain respectively by least square fitting.
Preferably, wherein, to the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data, be further:
To the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data, then along rail sword edge regions data, unequal dark space or clear zone data are removed to described even number line, obtain standardization even number line along rail sword edge regions data.
Preferably, wherein, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtain even column and wear rail sword edge regions data, be further:
The row that rail sword edge regions removes non-targeting regions are worn described in choosing, obtain even column and wear rail sword edge regions data, then rail sword edge regions data are worn to described even column and remove unequal dark space or clear zone data, obtain standardization even column and wear rail sword edge regions data.
Preferably, wherein, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data, be further:
Described standardization even number line is according to pixels worth along rail sword edge regions data and resequences and be mapped to a line, obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data.
Preferably, wherein, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data, be further:
Wear rail sword edge regions data to described standardization even column to be according to pixels worth and to resequence and be mapped to a line, obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data.
Preferably, wherein, to described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data, being further:
To described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data, then to described along rail sword sideline growth data with wear rail sword sideline growth data and do rectangular window process respectively, being fixed wears rail sword sideline growth data along rail sword sideline growth data and immobilization respectively.
Preferably, wherein, to described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF, being further:
Along rail sword sideline growth data and immobilization, rail sword sideline growth data is worn to described immobilization and does discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF.
Preferably, wherein, to described along rail MTF with wear rail MTF and integrate the MTF drawing two dimension, be further: to described along rail MTF with wear rail MTF and be multiplied and draw two-dimentional MTF, or to described along rail sword sideline growth data with wear rail sword sideline growth data and be multiplied and draw a diffusion data.
Compared with prior art, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, reaches following effect:
1, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, not only can be eliminated the noise in dark space and clear zone, can also make line spread function likelihood more, can obtain more accurate MTF by Fermi's fitting of a polynomial.
2, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, not only may be used for asking straight line sword limit MTF, can also be used for asking curved edges limit MTF.
Accompanying drawing explanation
Invent accompanying drawing described herein and be used to provide a further understanding of the present invention, invention forms a part of the present invention, and schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the inclination sword limit phase sample method process flow diagram of prior art ISO12233;
Fig. 2 is the method for estimation of the MTF in-orbit process flow diagram based on phase nonlinear resampling matching sword limit described in the embodiment of the present invention 1;
Fig. 3 is the method for estimation of the MTF in-orbit process flow diagram based on phase nonlinear resampling matching sword limit described in the embodiment of the present invention 2;
Fig. 4 is the method for estimation of the MTF in-orbit process flow diagram based on phase nonlinear resampling matching sword limit described in the embodiment of the present invention 3.
Embodiment
As employed some vocabulary to censure specific components in the middle of instructions and claim.Those skilled in the art should understand, and hardware manufacturer may call same assembly with different noun.This specification and claims are not used as with the difference of title the mode distinguishing assembly, but are used as the criterion of differentiation with assembly difference functionally." comprising " as mentioned in the middle of instructions and claim is in the whole text an open language, therefore should be construed to " comprise but be not limited to "." roughly " refer to that in receivable error range, those skilled in the art can solve the technical problem within the scope of certain error, reach described technique effect substantially.In addition, " couple " word and comprise directly any and indirectly electric property coupling means at this.Therefore, if describe a first device in literary composition to be coupled to one second device, then represent described first device and directly can be electrically coupled to described second device, or be indirectly electrically coupled to described second device by other devices or the means that couple.Instructions subsequent descriptions is for implementing better embodiment of the present invention, and right described description is to illustrate for the purpose of rule of the present invention, and is not used to limit scope of the present invention.Protection scope of the present invention is when being as the criterion depending on the claims person of defining.
Embodiment 1
As shown in Figure 2, be the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit described in the embodiment of the present invention 1, specific embodiment of the invention method is as follows:
Step 201, obtains the remote sensing images that described satellite is loaded in MTF in-orbit, in the targeting regions utilizing described remote sensing images to choose wherein respectively along rail sword edge regions and wear rail sword edge regions;
Step 202, to the described row removing non-targeting regions along rail sword edge regions chosen, obtains even number line along rail sword edge regions data; Meanwhile, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtain even column and wear rail sword edge regions data;
Step 203, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data;
Step 204, to described along rail sword limit interpolation edge-diffusion data with wear rail sword limit interpolation edge-diffusion data and carry out Fermi's matching respectively, obtain the matching after matching respectively and wears rail sword limit interpolation edge-diffusion data along rail sword limit interpolation edge-diffusion data and matching;
Step 205, wears rail sword limit interpolation edge-diffusion data to described matching along rail sword limit interpolation edge-diffusion data and matching and to be averaged respectively resampling, obtain respectively wearing rail sword limit edge-diffusion data without interpolation along rail sword limit edge-diffusion data with without interpolation;
Step 206, to described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data;
Step 207, to described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wears rail MTF;
Step 208, to described along rail MTF with wear rail MTF be multiplied draw two dimension MTF.
Embodiment 2
As shown in Figure 3, on the basis of embodiment 1, specific embodiment of the invention method is as follows:
Step 301, obtains the remote sensing images that described satellite is loaded in MTF in-orbit, in the targeting regions utilizing described remote sensing images to choose wherein respectively along rail sword edge regions and wear rail sword edge regions;
Step 302, to the described row removing non-targeting regions along rail sword edge regions chosen, obtains even number line along rail sword edge regions data; Meanwhile, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtain even column and wear rail sword edge regions data;
Step 303, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, be further:
To described even number line along rail sword edge regions data and even column wear rail sword edge regions data formed two-dimensional matrix data respectively according to pixels value size resequence and be mapped to one-dimension array, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data;
Step 304, to described along rail sword limit interpolation edge-diffusion data with wear rail sword limit interpolation edge-diffusion data and carry out Fermi's matching respectively, obtain the matching after matching respectively and wears rail sword limit interpolation edge-diffusion data along rail sword limit interpolation edge-diffusion data and matching;
Step 305, wears rail sword limit interpolation edge-diffusion data to described matching along rail sword limit interpolation edge-diffusion data and matching and to be averaged respectively resampling, obtain respectively wearing rail sword limit edge-diffusion data without interpolation along rail sword limit edge-diffusion data with without interpolation;
Step 306, to described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data;
Step 307, to described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wears rail MTF;
Step 308, to described along rail MTF with wear rail MTF and be multiplied and draw the MTF of two dimension, or to described along rail sword sideline growth data with wear rail sword sideline growth data and be multiplied and draw a diffusion data.
Wherein, described in step 304, Fermi's fitting of a polynomial is:
Wherein, α represents the amplitude of Fermi function; β represents the coordinate in the corresponding x-axis in Fermi function center; γ represents the steepness of Fermi function; D represents the coordinate in the corresponding y-axis in the dark space of Fermi function; α, β, γ and D obtain respectively by least square fitting.
Embodiment 3
As shown in Figure 4, on the basis of embodiment 2, specific embodiment of the invention method is as follows:
Step 401, obtains the remote sensing images that described satellite is loaded in MTF in-orbit, in the targeting regions utilizing described remote sensing images to choose wherein respectively along rail sword edge regions and wear rail sword edge regions;
Step 402, to the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data, then along rail sword edge regions data, unequal dark space or clear zone data are removed to described even number line, obtain standardization even number line along rail sword edge regions data; Simultaneously, the row that rail sword edge regions removes non-targeting regions are worn described in choosing, obtain even column and wear rail sword edge regions data, then rail sword edge regions data are worn to described even column and remove unequal dark space or clear zone data, obtain standardization even column and wear rail sword edge regions data;
Step 403, to described standardization even number line along rail sword edge regions data and standardization even column wear rail sword edge regions data formed two-dimensional matrix data respectively according to pixels value size resequence and be mapped to one-dimension array, to described standardization even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described standardization even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data;
Step 404, to described along rail sword limit interpolation edge-diffusion data with wear rail sword limit interpolation edge-diffusion data and carry out Fermi's matching respectively, obtain the matching after matching respectively and wears rail sword limit interpolation edge-diffusion data along rail sword limit interpolation edge-diffusion data and matching;
Step 405, wears rail sword limit interpolation edge-diffusion data to described matching along rail sword limit interpolation edge-diffusion data and matching and to be averaged respectively resampling, obtain respectively wearing rail sword limit edge-diffusion data without interpolation along rail sword limit edge-diffusion data with without interpolation;
Step 406, to described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data, then to described along rail sword sideline growth data with wear rail sword sideline growth data and do rectangular window process respectively, being fixed wears rail sword sideline growth data along rail sword sideline growth data and immobilization respectively;
Step 407, wears rail sword sideline growth data to described immobilization along rail sword sideline growth data and immobilization and does discrete Fourier transformation respectively, obtains along rail MTF respectively and wears rail MTF;
Step 408, to described along rail MTF with wear rail MTF and be multiplied and draw the MTF of two dimension, or to described along rail sword sideline growth data with wear rail sword sideline growth data and be multiplied and draw a diffusion data.
Wherein, described in step 404, Fermi's fitting of a polynomial is:
Wherein, α represents the amplitude of Fermi function; β represents the coordinate in the corresponding x-axis in Fermi function center; γ represents the steepness of Fermi function; D represents the coordinate in the corresponding y-axis in the dark space of Fermi function; α, β, γ and D obtain respectively by least square fitting.
Compared with prior art, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, reaches following effect:
1, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, not only can be eliminated the noise in dark space and clear zone, can also make line spread function likelihood more, can obtain more accurate MTF by Fermi's matching.
2, the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit of the present invention, not only may be used for asking straight line sword limit MTF, can also be used for asking curved edges limit MTF.
Also it should be noted that, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, commodity or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, commodity or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, commodity or the equipment comprising described key element and also there is other identical element.
It will be understood by those skilled in the art that embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, completely software implementation or the embodiment in conjunction with software and hardware aspect.And the present invention can adopt in one or more form wherein including the upper computer program implemented of computer-usable storage medium (including but not limited to magnetic disk memory, CD-ROM, optical memory etc.) of computer usable program code.
The foregoing is only embodiments of the invention, be not limited to the present invention.To those skilled in the art, the present invention can have various modifications and variations.All do within spirit of the present invention and principle any amendment, equivalent replacement, improvement etc., all should be included within right of the present invention.
Claims (10)
1., based on the method for estimation of MTF in-orbit on phase nonlinear resampling matching sword limit, its feature comprises:
Obtain described satellite and be loaded into remote sensing calibration image containing inclination sword limit target in-orbit in MTF, in the targeting regions utilizing described remote sensing images to choose wherein respectively along rail sword edge regions and wear rail sword edge regions;
To the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data; Meanwhile, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtain even column and wear rail sword edge regions data;
To described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data;
To described along rail sword limit interpolation edge-diffusion data with wear rail sword limit interpolation edge-diffusion data and carry out Fermi's matching respectively, obtain the matching after matching respectively and wear rail sword limit interpolation edge-diffusion data along rail sword limit interpolation edge-diffusion data and matching;
Wear rail sword limit interpolation edge-diffusion data to described matching along rail sword limit interpolation edge-diffusion data and matching to be averaged respectively resampling, obtain respectively wearing rail sword limit edge-diffusion data without interpolation along rail sword limit edge-diffusion data with without interpolation;
To described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data;
To described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF;
To described along rail MTF and wear rail MTF integrate draw two dimension MTF.
2. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, it is characterized in that, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, be further:
To described even number line along rail sword edge regions data and even column wear rail sword edge regions data formed two-dimensional matrix data respectively according to pixels value size resequence and be mapped to one-dimension array.
3. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, it is characterized in that, described Fermi fits to:
Wherein, α represents the amplitude of Fermi function; β represents the coordinate in the corresponding x-axis in Fermi function center; γ represents the steepness of Fermi function; D represents the coordinate in the corresponding y-axis in the dark space of Fermi function; α, β, γ and D obtain respectively by least square fitting.
4. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, is characterized in that, to the described row removing non-targeting regions along rail sword edge regions chosen, obtains even number line along rail sword edge regions data, is further:
To the described row removing non-targeting regions along rail sword edge regions chosen, obtain even number line along rail sword edge regions data, then along rail sword edge regions data, unequal dark space or clear zone data are removed to described even number line, obtain standardization even number line along rail sword edge regions data.
5. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, is characterized in that, described in choosing, wear the row that rail sword edge regions removes non-targeting regions, obtains even column and wears rail sword edge regions data, be further:
The row that rail sword edge regions removes non-targeting regions are worn described in choosing, obtain even column and wear rail sword edge regions data, then rail sword edge regions data are worn to described even column and remove unequal dark space or clear zone data, obtain standardization even column and wear rail sword edge regions data.
6. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 4, it is characterized in that, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data, be further:
Described standardization even number line is according to pixels worth along rail sword edge regions data and resequences and be mapped to a line, obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data.
7. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 5, it is characterized in that, to described even number line along rail sword edge regions data and even column wear rail sword edge regions data respectively according to pixels value resequence and be mapped to a line, to described even number line along rail sword edge regions data obtain this area data line number doubly along rail sword limit interpolation edge-diffusion data, to described even column wear rail sword edge regions data obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data, be further:
Wear rail sword edge regions data to described standardization even column to be according to pixels worth and to resequence and be mapped to a line, obtain this area data columns doubly wear rail sword limit interpolation edge-diffusion data.
8. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, it is characterized in that, to described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data, being further:
To described without interpolation along rail sword limit edge-diffusion data with wear rail sword limit edge-diffusion data without interpolation and make discrete differential respectively, obtain along rail sword sideline growth data respectively and wear rail sword sideline growth data, then to described along rail sword sideline growth data with wear rail sword sideline growth data and do rectangular window process respectively, being fixed wears rail sword sideline growth data along rail sword sideline growth data and immobilization respectively.
9. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 8, it is characterized in that, to described along rail sword sideline growth data with wear rail sword sideline growth data and do discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF, being further:
Along rail sword sideline growth data and immobilization, rail sword sideline growth data is worn to described immobilization and does discrete Fourier transformation respectively, obtain along rail MTF respectively and wear rail MTF.
10. the method for estimation of MTF in-orbit based on phase nonlinear resampling matching sword limit according to claim 1, it is characterized in that, to described along rail MTF and wear rail MTF integrate draw two dimension MTF, be further: to described along rail MTF with wear rail MTF and be multiplied and draw the MTF of two dimension, or to described along rail sword sideline growth data with wear rail sword sideline growth data and be multiplied and draw a diffusion data.
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Cited By (6)
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| CN113870150A (en) * | 2021-12-01 | 2021-12-31 | 南京航空航天大学 | Method for inverting spacecraft low-frequency vibration parameters based on continuous multiple remote sensing images |
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