CN113418873A - Hyperspectral imaging system and reconstruction spectral imaging method - Google Patents
Hyperspectral imaging system and reconstruction spectral imaging method Download PDFInfo
- Publication number
- CN113418873A CN113418873A CN202110698475.2A CN202110698475A CN113418873A CN 113418873 A CN113418873 A CN 113418873A CN 202110698475 A CN202110698475 A CN 202110698475A CN 113418873 A CN113418873 A CN 113418873A
- Authority
- CN
- China
- Prior art keywords
- spectrum
- image sensor
- module
- imaging
- data
- 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.)
- Granted
Links
Images
Classifications
-
- 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
-
- 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
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Abstract
A hyperspectral imaging system and a reconstruction spectral imaging method belong to the technical field of photoelectric imaging, not only can maintain high sensitivity and high spatial resolution of the imaging system, but also can solve the contradiction between spectral resolution and volume weight and limited on-satellite resources by on-orbit reconstruction of a spectral band. The hyperspectral imaging system provided by the invention can keep high sensitivity and high spatial resolution of the imaging system under a small volume size, and the spectrum band reconfigurable method can solve the contradiction between the spectral resolution and the volume weight as well as the hardware resources such as limited compressed storage data transmission and the like.
Description
Technical Field
The invention belongs to the technical field of photoelectric imaging, and relates to a hyperspectral imaging system and a reconstruction spectrum imaging method.
Background
The spectral imaging technology can acquire the spectral information of the target while acquiring the two-dimensional spatial characteristic information of the target, so that the spectral imaging technology has wide application space and plays an important role in the fields of military investigation, agriculture and forestry observation, environmental monitoring, disaster investigation, geological exploration and the like. The hyperspectral imaging system plays an important role in most fields of natural science and partial fields of people's life, such as crop yield estimation, soil quality assessment, water body pollution detection, air pollution detection and the like.
At present, a hyperspectral imaging system at home and abroad generally adopts a method of firstly aiming at the requirements of application and analysis of a spectral band and then developing an imaging system with a fixed spectral band according to the requirements, so that once the application is designed or developed, the imaging system is also limited and can not be flexibly adapted to various application requirements, and in order to meet the dual requirements of high spectral resolution and wide spectral band range, the imaging system generally has the problems of large volume, heavy weight and high cost. In recent years, due to the limitation of the volume and weight of the microsatellite and the limitation of limited compression storage and transmission resources on the satellite, the hyperspectral imaging application with high spatial resolution and high spectral resolution is difficult to realize.
Disclosure of Invention
In order to solve the problems, the invention provides a hyperspectral imaging system and a reconstruction spectral imaging method, which can not only keep the high sensitivity and high spatial resolution of the imaging system, but also solve the contradiction between the spectral resolution and the volume weight and the limited on-satellite resources by on-orbit reconstruction of a spectral band.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a hyperspectral imaging system, the system comprising: a spectroscopic element, an image sensor, and a data processing unit; the data processing unit includes: the system comprises a communication module, a spectral band windowing index module, an imaging parameter recording module, a sensor driving control module, an image data receiving module, a spectral band reconfigurable TDI module and a data output module; the light splitting element is attached to the surface of the image sensor, light rays enter the light splitting element to realize the gradual change of light transmission wavelength, and the image sensor selects to perform exposure imaging and reading through different windows according to the wavelength; the communication module completes imaging instructions, receives analysis and framing and sending state information; the spectral band windowing index module prestores calibration results of projection positions of all light-transmitting wavelengths of the light splitting element, and completes the indexing of the windowing position of the image sensor according to instruction parameters after receiving a spectral band selection instruction; the imaging parameter recording module performs read-write operation on the FLASH to realize the functions of recording and storing imaging parameters of spectrum selection execution results, window opening positions of the image sensor, integration stages of different spectrum sections, exposure time and gain and automatically reading power on; the sensor driving control module generates an image sensor driving time sequence according to the windowing setting parameters of the image sensor, and completes exposure imaging of a window corresponding to a required spectrum; the image data receiving module receives data output by the image sensor, performs data decoding and format conversion, and provides preprocessed image data for the band reconfigurable TDI module; the spectrum band reconfigurable TDI module is used for respectively carrying out independent time delay integration operation on the spectral element and the required m spectrum bands received by the image sensor according to a spectrum band selection instruction parameter and a required spectrum band integration series parameter; and the data output module is used for numbering the 1-line integral image of each spectrum section in each line period of the image sensor in a spectrum section and line flow sequence and then outputting the numbered data according to a data transmission format, the spectrum section numbers are sequentially numbered according to the wavelength, the line flow numbers of m spectrum section data in the same line are the same, the line flow numbers are automatically added by 1 after one line period, and the data are packaged into output data.
Preferably, the light splitting element is a gradient filter.
Preferably, the image sensor is an area array CMOS image sensor.
Preferably, the installation direction of the image sensor and the graduated filter is a row direction orthogonal to the transmission spectrum change direction of the graduated filter.
A reconstruction spectrum imaging method of a hyperspectral imaging system comprises the following steps:
the method comprises the following steps: the system is powered on, and the imaging parameter recording module reads the spectrum selection result stored in the FLASH, the windowing position size of the image sensor, the integration stages of different spectrum, the exposure time and the imaging parameters of the gain;
step two: aiming at a certain application scene, a group of spectra with the number of spectral bands being m is obtained through analysis;
step three: selecting m wavelengths in the spectrum in the step two to generate a spectrum section selection instruction, and sending the spectrum section selection instruction to a spectrum section windowing index module;
step four: the spectral band windowing indexing module is used for indexing a pre-stored calibration result of the transmission wavelength projection position of the gradual filter according to a spectral band selection instruction parameter, determining the positions and sizes of m windows of the image sensor, generating an image sensor windowing parameter and a spectral band selection execution result, and storing the image sensor windowing parameter and the spectral band selection execution result into a FLASH;
step five: the spectrum section reconfigurable TDI module determines the integration series of different spectrum sections according to the spectrum section selection execution result, in combination with calibrated wavelength response energy distribution data and on the basis of uniform response gray scale of each spectrum section, generates TDI integration series parameters and stores the TDI integration series parameters to FLASH;
step six: after receiving a photographing starting instruction, a sensor driving control module automatically generates a sensor driving time sequence according to window setting parameters of an image sensor, and meanwhile, a spectrum section reconfigurable TDI module carries out independent time delay integration on m spectrum sections according to integration stage number parameters;
step seven: the data output module formats the TDI image through spectrum segment and line flow serial number data, and m lines of m spectrum segment integral images are output outwards in each line period;
step eight: when the application requirement is changed, a group of m wavelengths is obtained again; and repeating the second step to the seventh step, namely sending a spectrum section selection instruction to the data processing unit to obtain m spectrum section integral images after spectrum section reconstruction.
Preferably, the band-reconfigurable TDI module employs a separate TDI algorithm for each band.
Preferably, the acquisition of the spectrum selection in step three includes the following steps:
the method comprises the following steps: the surface of the gradual filter is pasted on the photosensitive surface of the area array CMOS image sensor, and the pasting direction of the gradual filter is the direction of the line direction of the area array CMOS image sensor, which is orthogonal to the change direction of the transmission spectrum of the gradual filter;
step two: setting an area array CMOS image sensor as a non-windowing area array output;
step three: selecting short, medium and long 3 types of wavelengths as test wavelengths in the transmission wavelength range of the gradual filter respectively, and selecting a high-precision monochromator to output monochromatic light with the test wavelengths;
step four: setting imaging parameters of exposure time and gain of the imaging parameter recording module in the imaging parameter recording module, so that the gray level of an image bright strip area with the strongest transmittance wavelength in the first class of test wavelengths is about a saturation value 3/4;
step five: adjusting the monochromator to output a type of monochromatic light wavelength according to the minimum step length of the monochromator;
step six: photographing and storing images;
step seven: repeating the fifth step and the sixth step in the first type of test wavelength until traversing the first type of light-transmitting wavelength range of the gradient filter; repeating the fifth step and the sixth step in all types of test wavelengths until all types of light-transmitting wavelength ranges of the gradient filter are traversed;
step eight: and calculating all image response intervals and energy distribution, and establishing a projection mapping relation and a response energy distribution curve of all wavelengths on the photosensitive surface of the area array CMOS image sensor.
The invention has the beneficial effects that: the hyperspectral imaging system provided by the invention can keep high sensitivity and high spatial resolution of the imaging system under a small volume size, and the spectrum band reconfigurable method can solve the contradiction between the spectral resolution and the volume weight as well as the hardware resources such as limited compressed storage data transmission and the like.
Drawings
FIG. 1 is a schematic diagram of a focal plane unit structure of a hyperspectral imaging system.
FIG. 2 is a schematic representation of spectral reconstruction in accordance with the present invention.
FIG. 3 is a schematic diagram of normalized energy distribution of transmission wavelength of the graded filter according to the present invention.
FIG. 4 is a flow chart of a reconstructed spectrum imaging method of a hyperspectral imaging system.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, the hyperspectral imaging system of the invention comprises: a focal plane unit and a data processing unit; the focal plane unit comprises a gradual filter and an area array CMOS image sensor. The gradual filter is tightly attached to the photosensitive surface of the area array CMOS image sensor, and the installation direction of the gradual filter is that the row direction of the area array CMOS image sensor is orthogonal to the change direction of the transmission spectrum of the gradual filter. The area array CMOS image sensor has a row-direction multi-windowing function, and can perform multi-window imaging according to the transmission spectrum projection of the gradual filter on the sensor, wherein m spectral bands correspond to m imaging windows. According to the rule that the penetration wavelength of the gradual filter linearly changes in the line direction of the area array CMOS image sensor, the adjustment of the spectrum can be realized by only adjusting the windowing position and size of the area array CMOS image sensor, as shown in FIG. 2.
The data processing unit takes a programmable logic device as a core, can reconstruct the hyperspectral imaging requirement around a spectrum band, and realizes the functions of communication, spectrum band windowing index, imaging parameter recording, sensor drive control, image data receiving, spectrum band reconfigurable TDI, data output and the like. The communication module completes the receiving and analyzing of the imaging instruction and the framing and sending of the state information. The spectral band windowing index module prestores calibration results of projection positions of all light transmission wavelengths of the gradient filter, and completes the indexing of the windowing positions of the area array CMOS image sensor according to instruction parameters after receiving a spectral band selection instruction. The imaging parameter recording module performs read-write operation on the FLASH to realize the functions of recording and storing imaging parameters such as spectrum selection execution results, area array CMOS image sensor windowing position sizes, different spectrum integration stages, exposure time, gain and the like and automatically reading power-on. And the sensor driving control module generates a sensor driving time sequence according to the windowing setting parameters of the area array CMOS image sensor, and completes exposure imaging of a window corresponding to the required spectrum band. The image data receiving module receives data output by the area array CMOS image sensor, performs data decoding and format conversion, and provides preprocessed image data for the spectrum band reconfigurable TDI module. The TDI module with the reconfigurable spectrum segments respectively carries out independent time delay integration operation on the required m spectrum segments according to the spectrum segment selection instruction parameters and the required spectrum segment integration progression parameters, and adopts a dynamic accumulation design idea to avoid the influence of partial or all spectrum segment modification reconfiguration, meanwhile, the sensitivity of the system can be improved on the premise of not losing spatial resolution and time resolution, and the problem of low energy commonly existing in hyperspectral imaging is solved. The data output module carries out spectrum and line serial number on the 1-line integral image of each spectrum section in each line period and then outputs the image according to a data transmission format, the spectrum section numbers are sequentially numbered according to the wavelength, the serial numbers of the m spectrum section data lines in the same line are the same, the serial numbers of the line lines in one line period are automatically added by 1, and in addition, data such as spectrum section selection execution results, sensor windowing positions, different spectrum section integral levels, exposure time, gain, imaging time and the like can be packaged into output data for convenience of later image processing.
The number m of the spectral bands of the hyperspectral imaging system can be calculated according to the selected image sensor and the number transmission resource under the limitation of satellite resources, and the calculation formula is as follows:
in the formula, NcolumnFor area array CMOS image sensor column resolution, NbitFor the bit depth, N, of the image datasensorThe number of focal plane units contained in an imaging system is T, a line period is T, k is a data compression ratio, lossless compression with the compression ratio of at most 4 is usually selected for remote sensing imaging, and N ischnlIs the number of data transmission channels, Q is the data transmission single channel rate, t1Duration for satellite photographing, t2The satellite data transmission duration is.
The hyperspectral imaging system can perform spectrum segment reconstruction according to application requirements after determining the number of spectrum segments. A schematic diagram of transmission spectrum projection and normalized energy distribution of the graded filter on the sensor is shown in fig. 3, and not only is monochromatic light of each wavelength have a transmission window with a certain width, but also the transmittance of each wavelength is different, and in addition, the transmission windows of adjacent spectra with short wavelength intervals are partially overlapped. The distribution characteristic is inherently caused by the fact that the graded filter realizes spectral subdivision in a small volume size. Therefore, in addition to considering the distribution of the transmission spectrum projection position interval and the energy distribution, each spectral band reconstruction also needs to meet the requirement of minimum spectral band interval in order to avoid spectral band aliasing.
The distribution of the transmission spectrum projection position interval, the energy distribution and the minimum spectral band interval are obtained by calibrating the transmission spectrum projection of the focal plane unit, and the method comprises the following specific steps of:
the method comprises the following steps: the surface of the gradual filter is pasted on the photosensitive surface of the area array CMOS image sensor, and the pasting direction of the gradual filter is the direction of the line direction of the area array CMOS image sensor, which is orthogonal to the change direction of the transmission spectrum of the gradual filter;
step two: setting an area array CMOS image sensor as a non-windowing area array output;
step three: selecting short, medium and long 3 types of wavelengths as test wavelengths in the transmission wavelength range of the gradual filter respectively, and selecting a high-precision monochromator to output monochromatic light with the test wavelengths;
step four: setting imaging parameters such as exposure time, gain and the like to enable the gray level of the bright strip area of the image with the strongest wavelength of the transmittance in the test wavelength to be about a saturation value 3/4;
step five: adjusting the wavelength of monochromatic light output by the monochromator according to the minimum step length of the monochromator;
step six: photographing and storing images;
step seven: repeating the fifth step and the sixth step in the first type of test wavelength until traversing the first type of light-transmitting wavelength range of the gradient filter; repeating the fifth step and the sixth step in all types of test wavelengths until all types of light-transmitting wavelength ranges of the gradient filter are traversed;
step eight: and calculating response intervals and energy distribution of all the images, and establishing a projection mapping relation and a response energy distribution curve of all the wavelengths on the photosensitive surface of the sensor.
The spectral band reconstruction imaging method of the hyperspectral imaging system comprises the following steps:
the method comprises the following steps: the system is powered on, and the imaging parameter recording module reads the spectrum selection result stored in the FLASH, the windowing position size of the image sensor, the integration stages of different spectrum, the exposure time and the imaging parameters of the gain;
step two: aiming at a certain application scene, a group of spectra with the number of spectral bands being m is obtained through analysis;
step three: selecting m wavelengths in the spectrum in the step two to generate a spectrum section selection instruction, and sending the spectrum section selection instruction to a spectrum section windowing index module;
step four: the spectral band windowing indexing module is used for indexing a pre-stored calibration result of the transmission wavelength projection position of the gradual filter according to a spectral band selection instruction parameter, determining the positions and sizes of m windows of the image sensor, generating an image sensor windowing parameter and a spectral band selection execution result, and storing the image sensor windowing parameter and the spectral band selection execution result into a FLASH;
step five: the spectrum band reconfigurable TDI module determines the integral series of different spectrum bands according to the spectrum band selection execution result, in combination with calibrated wavelength response energy distribution data and on the basis of uniform response gray scale of each spectrum band, generates TDI integral series parameters, and transmits the TDI integral series parameters to the spectrum band reconfigurable TDI module on one hand and stores the integral series parameters to FLASH on the other hand;
step six: after receiving a photographing starting instruction, a sensor driving control module automatically generates a sensor driving time sequence according to window setting parameters of an image sensor, and meanwhile, a spectrum section reconfigurable TDI module carries out independent time delay integration on m spectrum sections according to integration stage number parameters;
step seven: the data output module formats the TDI image through spectrum segment and line flow serial number data, and m lines of m spectrum segment integral images are output outwards in each line period;
step eight: when the application requirement is changed, a group of m wavelengths is obtained again; and repeating the second step to the seventh step, namely sending a spectrum selection instruction to the data processing unit to obtain m spectrum integral images after spectrum reconstruction, thereby realizing the imaging method for spectrum reconstruction.
The m spectral bands are determined by closely combining application requirements, and considering a projection mapping calibration result of a transparent spectrum of a focal plane unit on a photosensitive surface of a sensor, so that the selected spectral bands meet the minimum spectral band interval requirement, aliasing crosstalk among the spectral bands is avoided, the operation can be automatically judged by a data processing unit, the spectral bands which do not meet the minimum interval requirement are finely adjusted, and the operation can be directly sent to a hyperspectral imaging system after other computing platforms such as an industrial personal computer and the like are completed.
Claims (7)
1. A hyperspectral imaging system, the system comprising: a spectroscopic element, an image sensor, and a data processing unit; the data processing unit includes: the system comprises a communication module, a spectral band windowing index module, an imaging parameter recording module, a sensor driving control module, an image data receiving module, a spectral band reconfigurable TDI module and a data output module; the light splitting element is attached to the surface of the image sensor, light rays enter the light splitting element to realize the gradual change of light transmission wavelength, and the image sensor selects to perform exposure imaging and reading through different windows according to the wavelength; the communication module completes imaging instructions, receives analysis and framing and sending state information; the spectral band windowing index module prestores calibration results of projection positions of all light-transmitting wavelengths of the light splitting element, and completes the indexing of the windowing position of the image sensor according to instruction parameters after receiving a spectral band selection instruction; the imaging parameter recording module performs read-write operation on the FLASH to realize the functions of recording and storing imaging parameters of spectrum selection execution results, window opening positions of the image sensor, integration stages of different spectrum sections, exposure time and gain and automatically reading power on; the sensor driving control module generates an image sensor driving time sequence according to the windowing setting parameters of the image sensor, and completes exposure imaging of a window corresponding to a required spectrum; the image data receiving module receives data output by the image sensor, performs data decoding and format conversion, and provides preprocessed image data for the band reconfigurable TDI module; the spectrum band reconfigurable TDI module is used for respectively carrying out independent time delay integration operation on the spectral element and the required m spectrum bands received by the image sensor according to a spectrum band selection instruction parameter and a required spectrum band integration series parameter; and the data output module is used for numbering the 1-line integral image of each spectrum section in each line period of the image sensor in a spectrum section and line flow sequence and then outputting the numbered data according to a data transmission format, the spectrum section numbers are sequentially numbered according to the wavelength, the line flow numbers of m spectrum section data in the same line are the same, the line flow numbers are automatically added by 1 after one line period, and the data are packaged into output data.
2. The hyperspectral imaging system according to claim 1, wherein the light splitting element is a graded filter.
3. The hyperspectral imaging system according to claim 1, wherein the image sensor is an area array CMOS image sensor.
4. The hyperspectral imaging system according to claim 1, 2 or 3, wherein the installation direction of the image sensor and the graduated filter is a row direction orthogonal to the transmission spectrum change direction of the graduated filter.
5. The method for reconstructing spectral imaging of a hyperspectral imaging system according to any of the claims 1 to 4 is characterized by comprising the following steps:
the method comprises the following steps: the system is powered on, and the imaging parameter recording module reads the spectrum selection result stored in the FLASH, the windowing position size of the image sensor, the integration stages of different spectrum, the exposure time and the imaging parameters of the gain;
step two: aiming at a certain application scene, a group of spectra with the number of spectral bands being m is obtained through analysis;
step three: selecting m wavelengths in the spectrum in the step two to generate a spectrum section selection instruction, and sending the spectrum section selection instruction to a spectrum section windowing index module;
step four: the spectral band windowing indexing module is used for indexing a pre-stored calibration result of the transmission wavelength projection position of the gradual filter according to a spectral band selection instruction parameter, determining the positions and sizes of m windows of the image sensor, generating an image sensor windowing parameter and a spectral band selection execution result, and storing the image sensor windowing parameter and the spectral band selection execution result into a FLASH;
step five: the spectrum section reconfigurable TDI module determines the integration series of different spectrum sections according to the spectrum section selection execution result, in combination with calibrated wavelength response energy distribution data and on the basis of uniform response gray scale of each spectrum section, generates TDI integration series parameters of a digital domain and stores the TDI integration series parameters to FLASH;
step six: after receiving a photographing starting instruction, a sensor driving control module automatically generates a sensor driving time sequence according to window setting parameters of an image sensor, and meanwhile, a spectrum section reconfigurable TDI module carries out independent time delay integration on m spectrum sections according to integration stage number parameters;
step seven: the data output module formats the TDI image through spectrum segment and line flow serial number data, and m lines of m spectrum segment integral images are output outwards in each line period;
step eight: when the application requirement is changed, a group of m wavelengths is obtained again; and repeating the second step to the seventh step, namely sending a spectrum selection instruction to the data processing unit to obtain m spectrum integral images after spectrum reconstruction, thereby realizing the imaging method for spectrum reconstruction.
6. The method of reconstructed spectral imaging according to claim 5, wherein the spectral band reconfigurable TDI module employs a separate TDI algorithm for each spectral band.
7. The method of reconstructed spectral imaging according to claim 5, wherein the acquisition of the spectral band selection in step three comprises the steps of:
the method comprises the following steps: the surface of the gradual filter is pasted on the photosensitive surface of the area array CMOS image sensor, and the pasting direction of the gradual filter is the direction of the line direction of the area array CMOS image sensor, which is orthogonal to the change direction of the transmission spectrum of the gradual filter;
step two: setting an area array CMOS image sensor as a non-windowing area array output;
step three: selecting short, medium and long 3 types of wavelengths as test wavelengths in the transmission wavelength range of the gradual filter respectively, and selecting a high-precision monochromator to output monochromatic light with the test wavelengths;
step four: setting imaging parameters of exposure time and gain of the imaging parameter recording module in the imaging parameter recording module, so that the gray level of an image bright strip area with the strongest transmittance wavelength in the first class of test wavelengths is about a saturation value 3/4;
step five: adjusting the monochromator to output a type of monochromatic light wavelength according to the minimum step length of the monochromator;
step six: photographing and storing images;
step seven: repeating the fifth step and the sixth step in the first type of test wavelength until traversing the first type of light-transmitting wavelength range of the gradient filter; repeating the fifth step and the sixth step in all types of test wavelengths until all types of light-transmitting wavelength ranges of the gradient filter are traversed;
step eight: and calculating all image response intervals and energy distribution, and establishing a projection mapping relation and a response energy distribution curve of all wavelengths on the photosensitive surface of the area array CMOS image sensor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110698475.2A CN113418873B (en) | 2021-06-23 | 2021-06-23 | Hyperspectral imaging system and reconstruction spectral imaging method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110698475.2A CN113418873B (en) | 2021-06-23 | 2021-06-23 | Hyperspectral imaging system and reconstruction spectral imaging method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113418873A true CN113418873A (en) | 2021-09-21 |
| CN113418873B CN113418873B (en) | 2022-05-17 |
Family
ID=77716328
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110698475.2A Active CN113418873B (en) | 2021-06-23 | 2021-06-23 | Hyperspectral imaging system and reconstruction spectral imaging method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113418873B (en) |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504316A (en) * | 2008-12-16 | 2009-08-12 | 北京理工大学 | Color filter tuning type window scanning optical spectrum imaging system and method |
| US20140253713A1 (en) * | 2011-10-25 | 2014-09-11 | Guangjie Zhai | Time-Resolved Single-Photon or Ultra-Weak Light Multi-Dimensional Imaging Spectrum System and Method |
| WO2016086043A1 (en) * | 2014-11-24 | 2016-06-02 | Massachusetts Institute Of Technology | Methods and apparatus for spectral imaging |
| US20170205337A1 (en) * | 2014-07-24 | 2017-07-20 | Ecole Polytechnique Federale De Lausanne (Epfl) | Compact Multifunctional System for Imaging Spectroscopy |
| CN108061600A (en) * | 2017-12-14 | 2018-05-22 | 中国科学院长春光学精密机械与物理研究所 | A kind of miniaturization spectrum imaging system and imaging method |
| US20180172515A1 (en) * | 2015-07-07 | 2018-06-21 | Vito Nv | Method and system for transforming spectral images |
| US20180180476A1 (en) * | 2016-12-27 | 2018-06-28 | Urugus S.A. | Dynamic hyper-spectral imaging of objects in apparent motion |
| US20180209850A1 (en) * | 2015-07-30 | 2018-07-26 | Technology Innovation Momentum Fund (Israel) Limited Partnership | Spectral imaging method and system |
| CN108332855A (en) * | 2018-05-16 | 2018-07-27 | 德州尧鼎光电科技有限公司 | A kind of hyperspectral imager device of Wavelength tunable |
| CN108680502A (en) * | 2018-04-19 | 2018-10-19 | 广州德擎光学科技有限公司 | The laser processing state monitoring device of multiphase feature is reconstructed based on spectrum constituency |
| CN109697697A (en) * | 2019-03-05 | 2019-04-30 | 北京理工大学 | The reconstructing method of the spectrum imaging system of neural network based on optimization inspiration |
| JP2019220176A (en) * | 2018-06-15 | 2019-12-26 | 大学共同利用機関法人情報・システム研究機構 | Image processing device and method |
| CN110823374A (en) * | 2019-10-31 | 2020-02-21 | 中国科学院西安光学精密机械研究所 | Spectral imaging method and system for simultaneously obtaining high-space and high-spectral-resolution spectral images |
| CN111191514A (en) * | 2019-12-04 | 2020-05-22 | 中国地质大学（武汉） | Hyperspectral image band selection method based on deep learning |
| EP3660490A1 (en) * | 2018-11-30 | 2020-06-03 | Fundació Institut de Ciències Fotòniques | Multi-color imaging |
| WO2020117245A1 (en) * | 2018-12-06 | 2020-06-11 | Massachusetts Institute Of Technology | Computational reconfigurable imaging spectrometer |
| CN111556264A (en) * | 2020-04-27 | 2020-08-18 | 中国科学院长春光学精密机械与物理研究所 | Novel hyperspectral optical TDI camera imaging system |
| CN112881308A (en) * | 2021-01-22 | 2021-06-01 | 浙江大学 | Spectrum camera based on wide spectrum coding and deep learning |
-
2021
- 2021-06-23 CN CN202110698475.2A patent/CN113418873B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101504316A (en) * | 2008-12-16 | 2009-08-12 | 北京理工大学 | Color filter tuning type window scanning optical spectrum imaging system and method |
| US20140253713A1 (en) * | 2011-10-25 | 2014-09-11 | Guangjie Zhai | Time-Resolved Single-Photon or Ultra-Weak Light Multi-Dimensional Imaging Spectrum System and Method |
| US20170205337A1 (en) * | 2014-07-24 | 2017-07-20 | Ecole Polytechnique Federale De Lausanne (Epfl) | Compact Multifunctional System for Imaging Spectroscopy |
| WO2016086043A1 (en) * | 2014-11-24 | 2016-06-02 | Massachusetts Institute Of Technology | Methods and apparatus for spectral imaging |
| US20180172515A1 (en) * | 2015-07-07 | 2018-06-21 | Vito Nv | Method and system for transforming spectral images |
| US20180209850A1 (en) * | 2015-07-30 | 2018-07-26 | Technology Innovation Momentum Fund (Israel) Limited Partnership | Spectral imaging method and system |
| US20180180476A1 (en) * | 2016-12-27 | 2018-06-28 | Urugus S.A. | Dynamic hyper-spectral imaging of objects in apparent motion |
| CN108061600A (en) * | 2017-12-14 | 2018-05-22 | 中国科学院长春光学精密机械与物理研究所 | A kind of miniaturization spectrum imaging system and imaging method |
| CN108680502A (en) * | 2018-04-19 | 2018-10-19 | 广州德擎光学科技有限公司 | The laser processing state monitoring device of multiphase feature is reconstructed based on spectrum constituency |
| CN108332855A (en) * | 2018-05-16 | 2018-07-27 | 德州尧鼎光电科技有限公司 | A kind of hyperspectral imager device of Wavelength tunable |
| JP2019220176A (en) * | 2018-06-15 | 2019-12-26 | 大学共同利用機関法人情報・システム研究機構 | Image processing device and method |
| EP3660490A1 (en) * | 2018-11-30 | 2020-06-03 | Fundació Institut de Ciències Fotòniques | Multi-color imaging |
| WO2020117245A1 (en) * | 2018-12-06 | 2020-06-11 | Massachusetts Institute Of Technology | Computational reconfigurable imaging spectrometer |
| CN109697697A (en) * | 2019-03-05 | 2019-04-30 | 北京理工大学 | The reconstructing method of the spectrum imaging system of neural network based on optimization inspiration |
| CN110823374A (en) * | 2019-10-31 | 2020-02-21 | 中国科学院西安光学精密机械研究所 | Spectral imaging method and system for simultaneously obtaining high-space and high-spectral-resolution spectral images |
| CN111191514A (en) * | 2019-12-04 | 2020-05-22 | 中国地质大学（武汉） | Hyperspectral image band selection method based on deep learning |
| CN111556264A (en) * | 2020-04-27 | 2020-08-18 | 中国科学院长春光学精密机械与物理研究所 | Novel hyperspectral optical TDI camera imaging system |
| CN112881308A (en) * | 2021-01-22 | 2021-06-01 | 浙江大学 | Spectrum camera based on wide spectrum coding and deep learning |
Non-Patent Citations (3)
| Title |
|---|
| DIOLINE SARA: "《Hyperspectral and multispectral image fusion techniques for high resolution applications: a review》", 《EARTH SCIENCE INFORMATICS》 * |
| 杨赞伟: "《一种改进的矩匹配高光谱图像非均匀校正算法》", 《激光与光电子学进展》 * |
| 蒋伊琳: "《基于深度学习的编码孔径光谱成像重构算法》", 《吉林大学学报》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113418873B (en) | 2022-05-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Decker et al. | The effect of spectral bandwidth and positioning on the spectral signature analysis of inland waters | |
| CN111007021A (en) | Hyperspectral water quality parameter inversion system and method based on one-dimensional convolution neural network | |
| Knapp | Scientific data stewardship of International Satellite Cloud Climatology Project B1 global geostationary observations | |
| CN102768069B (en) | Single-photon spectral counting and imaging system and method for complementary measurement | |
| Bezy et al. | MERIS-A new generation of ocean-colour sensor onboard Envisat | |
| CN102353449A (en) | Ultra-weak light multispectral imaging method and system | |
| CN112991288A (en) | Hyperspectral remote sensing image fusion method based on abundance image sharpening reconstruction | |
| CN104121990A (en) | Random grating based compressed sensing broadband hyperspectral imaging system | |
| Orlando et al. | The Sun as an X-ray star. I. Deriving the emission measure distribution versus temperature of the whole solar corona from the Yohkoh/Soft X-ray telescope data | |
| Mo et al. | Assessing biomass of diverse coastal marsh ecosystems using statistical and machine learning models | |
| CN104486562B (en) | Embedded infrared image superframe processing method based on the fixed time of integration | |
| CN112837385A (en) | Biochemical information measuring method using color space conversion | |
| Gower et al. | On the potential of MODIS and MERIS for imaging chlorophyll fluorescence from space | |
| CN115931123A (en) | Spectrum reconstruction calculation method and spectrum system based on self-adaptive optimization sparse dictionary | |
| Fiorentin et al. | Calibration of digital compact cameras for sky quality measures | |
| CN113408111B (en) | Atmospheric precipitation inversion method and system, electronic equipment and storage medium | |
| CN113418873B (en) | Hyperspectral imaging system and reconstruction spectral imaging method | |
| CN102322957A (en) | Spectrum drifting detection method for interference type hyperspectral imager | |
| Anthony-Twarog | CCD UVBY photometry in M67 | |
| Lesnichii et al. | A technique of measuring spectral characteristics of detector arrays in amateur and professional photocameras and their application for problems of digital holography | |
| Åhlén | Colour correction of underwater images using spectral data | |
| Ladner et al. | Validation Test Report for the Automated Optical Processing System (AOPS) Version 16 | |
| Kuchkorov et al. | Satellite image formation and preprocessing methods | |
| Zimmermann et al. | MOS/PRIRODA: an imaging VIS/NIR spectrometer for ocean remote sensing | |
| CN115452167A (en) | Satellite remote sensor cross calibration method and device based on invariant pixel |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |