CN116306210A - Design method of space-based infrared hyperspectral imaging index - Google Patents
Design method of space-based infrared hyperspectral imaging index Download PDFInfo
- Publication number
- CN116306210A CN116306210A CN202211511034.8A CN202211511034A CN116306210A CN 116306210 A CN116306210 A CN 116306210A CN 202211511034 A CN202211511034 A CN 202211511034A CN 116306210 A CN116306210 A CN 116306210A
- Authority
- CN
- China
- Prior art keywords
- infrared hyperspectral
- index
- space
- image
- noise ratio
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000701 chemical imaging Methods 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000013461 design Methods 0.000 title claims abstract description 31
- 238000001514 detection method Methods 0.000 claims abstract description 35
- 238000001228 spectrum Methods 0.000 claims abstract description 27
- 238000003384 imaging method Methods 0.000 claims abstract description 21
- 238000005457 optimization Methods 0.000 claims abstract description 20
- 238000004088 simulation Methods 0.000 claims description 29
- 230000003595 spectral effect Effects 0.000 claims description 25
- 230000005855 radiation Effects 0.000 claims description 19
- 230000004044 response Effects 0.000 claims description 14
- 239000012634 fragment Substances 0.000 claims description 12
- 210000001747 pupil Anatomy 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000012952 Resampling Methods 0.000 claims description 4
- 238000002310 reflectometry Methods 0.000 claims description 4
- 238000005316 response function Methods 0.000 claims description 3
- 238000000985 reflectance spectrum Methods 0.000 claims description 2
- 238000012360 testing method Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 10
- 238000011161 development Methods 0.000 description 6
- 238000003331 infrared imaging Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000007637 random forest analysis Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000010206 sensitivity analysis Methods 0.000 description 2
- 238000002834 transmittance Methods 0.000 description 2
- 230000005457 Black-body radiation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 210000000887 face Anatomy 0.000 description 1
- 238000002329 infrared spectrum Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Chemical compound O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J5/007—Radiation pyrometry, e.g. infrared or optical thermometry for earth observation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/171—Systems in which incident light is modified in accordance with the properties of the material investigated with calorimetric detection, e.g. with thermal lens detection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3504—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing gases, e.g. multi-gas analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/005—Tree description, e.g. octree, quadtree
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Biochemistry (AREA)
- Chemical & Material Sciences (AREA)
- Geometry (AREA)
- Pathology (AREA)
- Software Systems (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Health & Medical Sciences (AREA)
- Analytical Chemistry (AREA)
- Medical Informatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Computer Graphics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Image Processing (AREA)
Abstract
The invention provides a design method of an on-orbit infrared hyperspectral imaging index in order to ensure that the infrared hyperspectral load can be applied in an on-orbit manner. Step one, calculating the lowest joint signal-to-noise ratio of the corresponding infrared hyperspectral image according to the satellite detection task requirement; step two, generating an infrared hyperspectral radiance image of a task target scene according to satellite application requirements; step three, simulating and generating a corresponding space-based infrared hyperspectral image by utilizing the infrared hyperspectral radiance image generated in the step two according to the radiance signal of the task target scene; step four, calculating to obtain the optimal joint signal-to-noise ratio of the infrared hyperspectral image based on the characteristic spectrum band in the space-based infrared hyperspectral image; establishing a relation between the joint signal-to-noise ratio and imaging indexes, and analyzing the influence weight of each index; and step six, constructing an optimization function based on the relation and the influence weight obtained in the step five, and optimizing the infrared hyperspectral imaging index through the optimization function to obtain an optimal design index.
Description
Technical Field
The invention relates to a design method of an antenna-based infrared hyperspectral imaging index, which is mainly applied to the field of design of infrared hyperspectral satellite load indexes.
Background
Infrared radiation contains objective information which is not possessed by a visible light wave band, and particularly in the imaging aspect, an infrared imaging system has the unique advantage of working all the day, and the infrared imaging system receives attention from various countries in the field of aerospace sensing. Compared with the traditional single-band/multi-band infrared imaging technology, the infrared hyperspectral detection can obtain a plurality of continuous narrow-band spectral radiation information, can accurately detect the spectral distribution characteristics of the target, remarkably improves the detection and identification capability of the target, particularly enhances the detection capability of the target with very low contrast ratio with background radiation, and has irreplaceable effect on the detection application of the target on the sea, the land and the air.
The imaging index design is a precondition of the design of an infrared hyperspectral imaging system, and is an important foundation for ensuring the on-orbit high-efficiency application of infrared hyperspectral remote sensing imaging. Different from the visible light wave band, the infrared wave band target characteristics have the characteristics of mild emissivity curve and insignificant spectral characteristics, and weaker spectral characteristics are difficult to detect and characterize and are also more difficult to separate from the atmospheric effect, so that spectral detection can put higher requirements on indexes such as Noise Equivalent Temperature Difference (NETD); meanwhile, the space-based infrared hyperspectral detection is limited by all the factors of the full link, such as weak infrared radiation energy, large influence of water vapor on transmittance, high development difficulty of the infrared detector, limited calibration precision and the like, so that the difference exists between the application level actually achieved by the infrared hyperspectral detection and theoretical analysis. Therefore, a design method for considering the correlation performance index under the non-ideal condition and the practical application of the space-based infrared hyperspectral imaging efficiency index needs to be established, the relation between the imaging index and the application efficiency is established, and the design of the infrared hyperspectral imaging system is guided.
The current research on the design of infrared hyperspectral imaging indexes applied to target detection is relatively lagged, the Eismann first proposes the concept of the joint signal-to-noise ratio in the 90 s in the United states, and the target detection in the thermal crossing period is researched in a targeted manner, wherein 'Comparison of infrared imaging hyperspectral sensors for military target detection applications' (SPIE, 1996) analyzes the detection capability of infrared hyperspectrum on the target in the thermal crossing period, but the work mainly faces to ground application, the influence of the day-based observation conditions such as the atmosphere is not considered, meanwhile, the work only analyzes the requirement of the ground application on the related indexes, does not analyze the optimization strategy of the related indexes, and a complete design method cannot be formed. The Beijing aviation aerospace university Zhao Huijie issues a publication of 'application of infrared multispectral technology to detection in day and night alternation period' (infrared and laser engineering, 2018, 47 (2)), proposes that the infrared multispectral technology can be utilized to enhance the target detection capability under the condition of low target background contrast in the thermal crossover period, and provides an infrared multispectral detection system design, but the system only comprises 5 infrared spectrum segments, is not influenced by the energy radiation introduced by hyperspectral imaging, the detector development level and other limiting factors, and also does not consider the influence of atmospheric and other day-based observation conditions.
In summary, at present, there is no index design method based on full-link application efficiency analysis for the space-based infrared hyperspectral imaging, so as to ensure that the infrared hyperspectral imaging system meets the actual on-orbit application requirement of the satellite, which is also an important premise and difficulty in the design of the infrared hyperspectral satellite.
Disclosure of Invention
The invention provides a design method of an on-orbit infrared hyperspectral imaging index in order to ensure that the infrared hyperspectral load can be applied in an on-orbit manner.
The invention is realized by the following technical scheme.
A design method of an antenna-based infrared hyperspectral imaging index comprises the following steps:
step one, calculating the lowest joint signal-to-noise ratio of the corresponding infrared hyperspectral image according to the satellite detection task requirement;
step two, generating an infrared hyperspectral radiance image of a task target scene according to satellite application requirements;
step three, simulating and generating a corresponding space-based infrared hyperspectral image by utilizing the infrared hyperspectral radiance image generated in the step two according to the radiance signal of the task target scene;
step four, calculating to obtain the optimal joint signal-to-noise ratio of the infrared hyperspectral image based on the characteristic spectrum band in the space-based infrared hyperspectral image;
step five, repeating the step three and the step four, establishing the relation between the joint signal-to-noise ratio and the imaging index, and analyzing the influence weight of each index;
and step six, constructing an optimization function based on the relation and the influence weight obtained in the step five, and optimizing the infrared hyperspectral imaging index through the optimization function to obtain an optimal design index.
The invention has the beneficial effects that:
1. the method is based on the requirements of satellites on typical scenes and typical targets on detection rate/false alarm rate, and the combined signal-to-noise ratio threshold requirement of the hyperspectral image is calculated; simultaneously obtaining an space-based infrared hyperspectral image under corresponding indexes through full-link simulation, calculating a joint signal-to-noise ratio, establishing a relation between an imaging index and the joint signal-to-noise ratio, analyzing influence weights of the indexes on the joint signal-to-noise ratio, and optimizing the imaging index based on a joint signal-to-noise ratio threshold and the index influence weights;
2. the method is suitable for the infrared hyperspectral imaging index optimization design facing the target detection application, the target detection application efficiency is evaluated through the spectrum combination signal-to-noise ratio, the influence of all-link factors of a target characteristic-atmosphere-space-based imaging system on the target detection application is considered, the on-orbit imaging state is truly simulated, the index optimization weight is confirmed through sensitivity analysis, and the optimal index design facing the real space-based application efficiency is ensured to be realized;
3. the invention adopts an end-to-end infrared hyperspectral full-link simulation means of the target characteristic-atmosphere-space-based imaging system to truly simulate an on-orbit imaging state;
4. according to the method, the image joint signal-to-noise ratio is used for optimizing the index and simultaneously associating the imaging performance index with the application index, the optimization method is simple, the calculation is efficient, the application capability of the infrared hyperspectral load can be accurately estimated, and the imaging index design can be applied to the true on-orbit;
5. according to the method, the influence weights of different indexes on the target detection application indexes are obtained through sensitivity analysis based on the random forest model, and the influence weights are introduced into the process of optimizing the design of the imaging indexes, so that the index optimizing process is ensured to be more reasonable, the situation that related indexes are too high is effectively avoided, and the load development cost is reduced.
Drawings
FIG. 1 is a flow chart of an infrared hyperspectral imaging index design method of the invention;
fig. 2 is a graph of the combined signal-to-noise ratio versus target detection rate/false alarm rate of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described in detail below with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely illustrative of the principles and spirit of the invention and are not intended to limit the scope of the invention.
The realization principle of the invention is based on the requirements of satellites on detection rate/false alarm rate of typical scenes and typical targets, and the combined signal-to-noise ratio threshold requirement of hyperspectral images can be calculated; and simultaneously, obtaining an space-based infrared hyperspectral image under the corresponding index through full-link simulation, calculating the joint signal-to-noise ratio, establishing the relation between the imaging index and the joint signal-to-noise ratio, analyzing the influence weight of each index on the joint signal-to-noise ratio, and optimizing the imaging index based on the joint signal-to-noise ratio threshold and the index influence weight.
As shown in FIG. 1, the design method of the space-based infrared hyperspectral imaging index of the invention specifically comprises the following steps:
step one, calculating the minimum combined signal-to-noise ratio SCR of a corresponding infrared hyperspectral image according to satellite detection task requirements th ；
In this embodiment, the satellite detection task requirement includes a detection rate P d0 And false alarm rate P f0 The method comprises the steps of carrying out a first treatment on the surface of the The relation between the detection rate, the false alarm rate and the joint signal-to-noise ratio is specifically as follows:
P D ＝1-Q(SCR-Q -1 (P F ))
wherein P is F The constant false alarm rate is achieved, Q is a right tail function of standard normal distribution, and the calculation method is that
As shown in FIG. 2, the different false alarm rates are substituted into the two equations to obtain the curve relation of the target detection rate, the false alarm rate and the combined signal-to-noise ratio of the image, and the calculated false alarm rate is 10 -7 When the SCR meets the SCR low And when the target detection rate is more than or equal to 6.4, the target detection rate meets the satellite task requirement.
In specific implementation, in order to avoid excessive load development cost caused by excessive index, the upper limit of SCR is limited to SCR high ～SCR low +1, the threshold of the image joint signal-to-noise ratio is set to SCR th =7.5; the method can ensure that the subsequent index optimization meets the application requirement, and avoid the overhigh load development cost and the overhigh index.
Step two, generating an infrared hyperspectral radiance image of a task target scene according to satellite application requirements; the method specifically adopts the following steps:
determining a task target scene according to satellite application requirements, judging whether the emissivity and the reflectivity of the target scene are known, if so, setting the temperature of the target and the background, and obtaining an infrared hyperspectral radiance image through simulation; if not, carrying out ground or airborne test, obtaining a hyperspectral image by using a high-performance thermal infrared hyperspectral detector, and taking the hyperspectral image as an infrared hyperspectral radiance image of the target scene.
In this embodiment, the specific formula of the infrared hyperspectral radiance image obtained through simulation is as follows:
L 0 (x 0 ,y 0 ,λ)＝L e (x 0 ,y 0 ,λ)+L r (x 0 ,y 0 ,λ)
wherein L is e (x 0 ,y 0 Lambda) is the radiation spectrum signal of the task object scene, L r (x 0 ,y 0 Lambda) is the reflectance spectrum signal of the task object scene, (x) 0 ,y 0 ) The coordinates of the task target scene images are shown, and lambda is the wavelength;
the radiation spectrum signal of the task target scene is as follows:
wherein ε (x) 0 ,y 0 λ) is the target/background emissivity, T is the set temperature, h is the planck constant, c is the speed of light, k is the boltzmann constant;
the reflection spectrum signals of the task target scene are as follows:
wherein ρ (x 0 ,y 0 Lambda) is the target/background reflectivity, E 0 (lambda) is the energy of the sun directly incident to the target/background after passing through the atmosphere, and can be obtained through MODTRA simulation, theta s Is the zenith angle of the sun, L s-down Is solar downlink radiation, and can be obtained by MODTRA simulation, and when the imaging time is night, L r (x 0 ,y 0 ,λ)＝0。
Step three, simulating and generating a corresponding space-based infrared hyperspectral image by utilizing the infrared hyperspectral radiance image generated in the step two according to the radiance signal of the task target scene; the method comprises the following specific steps:
s301, firstly, transmitting a radiance signal of a task target scene to an entrance pupil of a space-based infrared hyperspectral load through the atmosphere, wherein the radiance signal at the entrance pupil is
L 1 (x 0 ,y 0 ,λ)＝τ(λ)L 0 (x 0 ,y 0 ,λ)+L p (λ)+L s-up (λ)
Wherein, tau (lambda) is the spectral transmittance of the atmosphere, which can be obtained by MODTRA simulation; l (L) p (lambda) is the radiance of the atmospheric upstroke radiation, which can be obtained by MODTRA simulation; l (L) s-up The solar upward travel radiation can be obtained through MODTRA simulation;
s302, taking the radiance signal at the entrance pupil as input, and obtaining a space-based infrared hyperspectral image under an infrared hyperspectral imaging index in a simulation mode according to an infrared hyperspectral imaging principle;
in this embodiment, the simulation mainly includes a spatial response simulation, a spectral response simulation, and a radiation response simulation; the method comprises the following specific steps:
the space response simulation carries out bilinear interpolation space resampling on the entrance pupil radiance image based on the space resolution, and carries out convolution on the image based on the system MTF simulation point spread function PSF to obtain an infrared hyperspectral image after space response; the formula is as follows:
wherein (x, y) is (x) 0 ,y 0 ) The influence coordinates after resampling of the infrared hyperspectral imaging spatial resolution are simulated by using a two-dimensional Gaussian function by using a point spread function PSF, and the full width half maximum FWHM is as follows:
FWHM＝7*σ psf
wherein, the liquid crystal display device comprises a liquid crystal display device,
spectral response simulation is carried out to obtain the radiance signal of each wave band by convolving the infrared hyperspectral image with a spectral response function; the formula is as follows:
wherein lambda is i In order to obtain a spectrum band after resampling according to the spectrum resolution, SRF is a spectrum response function, and approximation can be performed by a Gaussian function with the spectrum resolution of half-width;
the radiation response simulation is carried out according to the noise equivalent radiance corresponding to NETD and the random multiplicative error caused by radiation calibration, and finally the space-based infrared hyperspectral image is obtained; the formula is as follows:
L'(x,y,λ i )＝(L 3 (x,y,λ i )+randn*NESR)*e k
wherein NESR is a noise equivalent temperature difference, nesr=b (t+netd, λ) -B (T, λ), B (x) is a planck blackbody radiation calculation formula;
wherein e k E is a random multiplicative error coefficient k ＝rand*e rad /2+1，e rad Is an absolute scaling error;
wherein randn is Gaussian random noise with the mean value of 0.
Step four, calculating to obtain the optimal joint signal-to-noise ratio of the infrared hyperspectral image based on the characteristic spectrum band in the space-based infrared hyperspectral image;
the formula of the optimal joint signal-to-noise ratio is as follows:
s is a contrast vector of a target spectrum and a background spectrum, the length of the vector is the optimal spectrum number N, M is a covariance matrix of the background, and the width of the matrix is equal to the optimal spectrum number N;
when N is less than or equal to 2, traversing a spectrum segment with optimal combined signal-to-noise ratio through the spectrum segment; and when N is more than 2, traversing the rest spectral fragments and the previous N-1 spectral fragments to form N spectral fragments based on the N-1 spectral fragments, calculating the combined signal-to-noise ratio, taking the N spectral fragments with the highest combined signal-to-noise ratio as the N preferred spectral fragments, and taking the calculated combined signal-to-noise ratio as the optimal combined signal-to-noise ratio.
Step five, repeating the step three and the step four, establishing the relation between the joint signal-to-noise ratio and the imaging index, and analyzing the influence weight of each index;
in this embodiment, the relationship between the joint signal-to-noise ratio and the imaging index is as follows:
SCR var ＝f(var)
wherein var represents three main indicators affecting infrared hyperspectral imaging: spatial resolution Δd, spectral resolution Δλ, and netdΔt; keeping two indexes unchanged, and changing the other index to obtain a corresponding hyperspectral image combined signal-to-noise ratio; meanwhile, calculating various index combinations to obtain signal-to-noise ratio as matrix input random forest model, and obtaining the influence weight of each index on the combined signal-to-noise ratio through deep learning:
{R var }＝RF{Δd,Δλ,ΔT,SCR}
wherein R is var Representing the influence weights of three indexes and meeting
RF is a random forest deep learning model.
Step six, constructing an optimization function based on the relation and the influence weight obtained in the step five, and optimizing the infrared hyperspectral imaging index through the optimization function to obtain an optimal design index;
in this embodiment, the specific formula of the optimization function is as follows:
s.t.var min ≤var≤var max
wherein var max Designing a required value for satellite index, var min Is an optimal value which can be achieved based on the current development level index.
In the specific implementation, after the optimized design index is obtained in the step six, the index is further verified, so that the optimized index meets the requirement of the application index; the method specifically adopts the following steps:
inputting the optimized design index obtained in the step six into the step three again, obtaining the infrared hyperspectral image combined signal-to-noise ratio under the optimized index through the step four, and calculating the false alarm rate P required f0 Target detection rate P d Judgment of P d ≥P d0 If so, outputting an optimization index result; if not, returning to the step six to perform optimization again.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (8)
1. The design method of the space-based infrared hyperspectral imaging index is characterized by comprising the following steps of:
step one, calculating the lowest joint signal-to-noise ratio of the corresponding infrared hyperspectral image according to the satellite detection task requirement;
step two, generating an infrared hyperspectral radiance image of a task target scene according to satellite application requirements;
step three, simulating and generating a corresponding space-based infrared hyperspectral image by utilizing the infrared hyperspectral radiance image generated in the step two according to the radiance signal of the task target scene;
step four, calculating to obtain the optimal joint signal-to-noise ratio of the infrared hyperspectral image based on the characteristic spectrum band in the space-based infrared hyperspectral image;
step five, repeating the step three and the step four, establishing the relation between the joint signal-to-noise ratio and the imaging index, and analyzing the influence weight of each index;
and step six, constructing an optimization function based on the relation and the influence weight obtained in the step five, and optimizing the infrared hyperspectral imaging index through the optimization function to obtain an optimal design index.
2. The method for designing an index of space-based infrared hyperspectral imaging as claimed in claim 1, wherein the satellite detection task requirements include a detection rate and a false alarm rate.
3. The method for designing the space-based infrared hyperspectral imaging index as claimed in claim 1, wherein the second step specifically adopts the following modes:
determining a task target scene according to satellite application requirements, judging whether the emissivity and the reflectivity of the target scene are known, if so, setting the temperature of the target and the background, and obtaining an infrared hyperspectral radiance image through simulation; if not, carrying out ground or airborne test, obtaining a hyperspectral image by using a high-performance thermal infrared hyperspectral detector, and taking the hyperspectral image as an infrared hyperspectral radiance image of the target scene.
4. The method for designing an index of space-based infrared hyperspectral imaging as claimed in claim 3, wherein the specific formula is as follows:
L 0 (x 0 ,y 0 ,λ)＝L e (x 0 ,y 0 ,λ)+L r (x 0 ,y 0 ,λ)
wherein L is e (x 0 ,y 0 Lambda) is the radiation spectrum signal of the task object scene, L r (x 0 ,y 0 Lambda) is the reflectance spectrum signal of the task object scene, (x) 0 ,y 0 ) The coordinates of the task target scene images are shown, and lambda is the wavelength;
the radiation spectrum signal of the task target scene is as follows:
wherein ε (x) 0 ,y 0 λ) is the target/background emissivity, T is the set temperature, h is the planck constant, c is the speed of light, k is the boltzmann constant;
the reflection spectrum signals of the task target scene are as follows:
wherein ρ (x 0 ,y 0 Lambda) is the target/background reflectivity, E 0 (lambda) is the energy of the sun directly incident to the target/background after passing through the atmosphere, θ s Is the zenith angle of the sun, L s-down Is solar downstream radiation.
5. The method for designing an index of space-based infrared hyperspectral imaging as claimed in claim 1, 2 or 3, wherein the third specific steps are as follows:
s301, firstly, transmitting a radiance signal of a task target scene to an entrance pupil of a space-based infrared hyperspectral load through the atmosphere;
s302, taking the radiance signal at the entrance pupil as input, and obtaining the space-based infrared hyperspectral image under the infrared hyperspectral imaging index through simulation according to the infrared hyperspectral imaging principle.
6. The method for designing an antenna-based infrared hyperspectral imaging index as claimed in claim 5, wherein the simulation includes a spatial response simulation, a spectral response simulation, and a radiation response simulation; the method comprises the following specific steps:
the space response simulation carries out bilinear interpolation space resampling on the entrance pupil radiance image based on the space resolution, and carries out convolution on the image based on the system MTF simulation point spread function PSF to obtain an infrared hyperspectral image after space response;
spectral response simulation is carried out to obtain the radiance signal of each wave band by convolving the infrared hyperspectral image with a spectral response function;
and the radiation response simulation is carried out according to the noise equivalent radiance corresponding to NETD and the random multiplicative error caused by radiation calibration, so that the space-based infrared hyperspectral image is finally obtained.
7. The method for designing an space-based infrared hyperspectral imaging index as claimed in claim 5 or 6, wherein the formula of the optimal joint signal-to-noise ratio in the fourth step is as follows:
s is a contrast vector of a target spectrum and a background spectrum, the length of the vector is the optimal spectrum number N, M is a covariance matrix of the background, and the width of the matrix is equal to the optimal spectrum number N;
when N is less than or equal to 2, traversing a spectrum segment with optimal combined signal-to-noise ratio through the spectrum segment; and when N is more than 2, traversing the rest spectral fragments and the previous N-1 spectral fragments to form N spectral fragments based on the N-1 spectral fragments, calculating the combined signal-to-noise ratio, taking the N spectral fragments with the highest combined signal-to-noise ratio as the N preferred spectral fragments, and taking the calculated combined signal-to-noise ratio as the optimal combined signal-to-noise ratio.
8. The method for designing an space-based infrared hyperspectral imaging index as claimed in claim 1, 2, 5 or 6, wherein after the optimization design index is obtained in the sixth step, the index is further verified to ensure that the optimization index meets the application index requirement; the method specifically adopts the following steps:
inputting the optimized design index obtained in the step six into the step three again, obtaining the infrared hyperspectral image combined signal-to-noise ratio under the optimized index through the step four, and calculating the false alarm rate P required f0 Target detection rate P d Judgment of P d ≥P d0 If so, outputting an optimization index result; if not, returning to the step six to perform optimization again.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211511034.8A CN116306210A (en) | 2022-11-29 | 2022-11-29 | Design method of space-based infrared hyperspectral imaging index |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211511034.8A CN116306210A (en) | 2022-11-29 | 2022-11-29 | Design method of space-based infrared hyperspectral imaging index |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116306210A true CN116306210A (en) | 2023-06-23 |
Family
ID=86829303
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211511034.8A Pending CN116306210A (en) | 2022-11-29 | 2022-11-29 | Design method of space-based infrared hyperspectral imaging index |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116306210A (en) |
-
2022
- 2022-11-29 CN CN202211511034.8A patent/CN116306210A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8355536B2 (en) | Passive electro-optical tracker | |
| CN111563962A (en) | Remote sensing image simulation method based on geometric radiation integrated sampling | |
| CN108287350A (en) | Method is determined based on the space-based track production key parameter of multiple information synthesis | |
| US11614367B2 (en) | Characterizing tropospheric boundary layer thermodynamic and refractivity profiles utilizing selected waveband infrared observations | |
| CN102163264A (en) | Method for evaluating quality and application capability of hyperspectral data | |
| US20190128805A1 (en) | Characterizing tropospheric boundary layer thermodynamic and refractivity profiles utilizing selected waveband infrared observations | |
| CN108120510A (en) | A kind of in-orbit absolute radiation calibration method of optical sensor based on reflection mirror array | |
| Yuan et al. | Performance analysis of the infrared imaging system for aircraft plume detection from geostationary orbit | |
| CN114581349A (en) | Visible light image and infrared image fusion method based on radiation characteristic inversion | |
| van Eijk et al. | The FESTER field trial | |
| CN116306210A (en) | Design method of space-based infrared hyperspectral imaging index | |
| Cao et al. | Attitude measurement method of spinning projectile based on infrared focal plane array | |
| CN103411896B (en) | Low rail space junk Extra-low Dispersion spectral signature acquisition methods | |
| de Jong | IRST and its perspective | |
| CN108897059A (en) | A kind of Infrared Targets imaging detectivity analysis method | |
| Cao et al. | Design and comparison of fault monitoring methods in attitude measurement based on uniaxial infrared focal plane array | |
| Huang et al. | Validating theoretical simulations of thermal emission hot spot effects on maize canopies | |
| Xu et al. | Ill-posed surface emissivity retrieval from multi-geometry hyperspectral images using a hybrid deep neural network | |
| CN113075150B (en) | Space-based infrared early warning spectrum section selection method and device based on detection efficiency | |
| Simoneau et al. | MATISSE-v1. 5 and MATISSE-v2. 0: new developments and comparison with MIRAMER measurements | |
| Han et al. | Comparison of inverse estimation methods of radiative reflection properties using temporal and directional IR radiances | |
| Spulber et al. | An analytical approach regarding some radiometric problems during the detection on the sea surface with a thermal camera | |
| Choi et al. | Study on Spectral Transmission Characteristics of the Reflected and Self-emitted Radiations through the Atmosphere | |
| Villa-Aleman et al. | Assessment of Ivanpah Playa as a site for thermal vicarious calibration for the MTI satellite | |
| Turgut | Radiometry-based range prediction for mid-and long-wave infrared imaging systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |