CN114021372A - Moon surface mineral quantitative analysis method and system based on moon hyperspectral data - Google Patents
Moon surface mineral quantitative analysis method and system based on moon hyperspectral data Download PDFInfo
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Abstract
本发明提出了基于月球高光谱数据的月球表面矿物定量分析方法及系统，包括：获取研究区的月球高光谱图像；基于典型月球返回样品建立端元光谱数据库；分别计算研究区的月球高光谱图像和端元光谱数据库中的返回样品光谱数据的单次散射反照率；基于计算的单次散射反照率，采用稀疏解混算法从端元光谱数据库中选取最优端元矿物光谱来拟合观测光谱，从而反演出矿物的类型、分布和含量。该发明方法是一种准确的、高效的、直观的矿物定量反演方法。
The invention proposes a method and system for quantitative analysis of lunar surface minerals based on lunar hyperspectral data, including: acquiring lunar hyperspectral images of a study area; establishing an endmember spectral database based on typical lunar return samples; separately calculating the lunar hyperspectral images of the research area and the single scattering albedo of the returned sample spectral data in the endmember spectrum database; based on the calculated single scattering albedo, the optimal endmember mineral spectrum is selected from the endmember spectrum database by the sparse unmixing algorithm to fit the observed spectrum , so as to invert the type, distribution and content of minerals. The invented method is an accurate, efficient and intuitive mineral quantitative inversion method.
Description
Technical Field
The invention belongs to the field of planet remote sensing, and particularly relates to a method and a system for quantitatively analyzing lunar surface minerals based on lunar hyperspectral data.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The moon is the extraterrestrial celestial body closest to the earth, and deep research on the geology of the moon has important significance for understanding the earth and other planets. The mineral type, content and distribution characteristics of the moon can provide important clues and evidences for the deep study of the geological evolution of the moon. Currently, some countries return samples from the lunar surface one after the other, sampling 10 lunars, returning about 386kg of samples, wherein our country returns 1731 grams from the lunar surface using Chang' e five lunar probes. However, since the number and location of samples are limited, and sampling in the field is not possible as in geological studies on the earth, the spectral data obtained by remote sensing is very effective for detecting lunar minerals and rocks. The method for mineral identification and quantitative inversion is constructed by combining the spectrum data returned by the lunar probe with the spectrum of the returned lunar sample acquired by a spectrum laboratory (such as an RELAB spectrum laboratory), and is applied to the surface of the non-sampled lunar, so that more mineral component information on the lunar surface can be acquired, the rock type can be determined, and important evidence and constraint can be provided for the lunar evolution process.
The main types of lunar minerals are plagioclase, pyroxene, olivine and the like, the visible-near infrared spectrum is sensitive to the minerals, each mineral has a specific spectral absorption characteristic, the spectral absorption characteristic is as most typical as the pyroxene has strong absorption at 1000nm and 2000nm and weak absorption at 1200nm, the reflectivity of plagioclase is high, and has strong absorption only at 1250nm (absorption is also very weak at 2200 nm), the spectral characteristics of olivine are very unique and have wide absorption at 1000nm, and therefore the types of the minerals can be inverted through spectral data. However, the content of different minerals is less restricted in regions due to mixing of minerals, spatial efflorescence and data quality.
Currently, for the inversion of lunar surface minerals, qualitative and semi-quantitative interpretation and analysis are the main reasons due to complex convolution of mineral spectra, instrument and calibration noise, and spectral variability of end-members in nonlinear environments. A certain range near the absorption peak of the main minerals is superposed by using an Integrated band depth method (IBD), and then the IBDs of several main absorption peaks are synthesized into an RGB pseudo-color image so as to judge the distribution characteristics of the main minerals more intuitively, but the types and the content of the minerals cannot be determined specifically. The method is a method for classifying and identifying mixed pixels only aiming at spectral curves, and the method needs to estimate the composition components of the spectrum in advance, has different results due to the influence of human factors, is less influenced by the particle size, and can only obtain the relative volume content of minerals contained in a single mixed spectral curve finally. The mixed spectrum unmixing based on the Hapke radiation transmission model is also determined by performing the mixed spectrum unmixing by a more conventional method, but the method needs to set an end member spectrum in advance, then unmixing any unknown mixed spectrum, and finally comparing the unmixed spectrum with a standard spectrum library to determine the type and relative content of minerals, but only part of landing sites on the moon extract a spectrum with higher precision, so that the type and characteristics of the end member spectrum are difficult to accurately determine.
From the above method for identifying and content inverting lunar surface minerals, the following three problems mainly exist at present:
(1) only a single mixed spectrum curve can be unmixed, and the mineral type and relative content of a certain spectrum pixel point are identified;
(2) the spectrum of the unmixed end member needs to be determined in advance, and the unmixed result has errors caused by certain human factors;
(3) the characteristics and content information of mineral distribution cannot be intuitively displayed in a large range.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for quantitatively analyzing lunar surface minerals based on lunar hyperspectral data, which utilizes the lunar hyperspectral data to identify the type, distribution and content of lunar surface minerals and visually display the content distribution map of various decoded minerals under the condition of not assuming the components and spectrum of original data.
In order to achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
in a first aspect, a method for quantitatively analyzing lunar surface minerals based on lunar hyperspectral data is disclosed, which comprises the following steps:
acquiring a moon hyperspectral image of a research area;
establishing an end member spectrum database based on a typical moon returned sample;
respectively calculating single scattering albedo of the lunar hyperspectral image of the research area and the returned sample spectral data in the end member spectral database;
based on the calculated single scattering albedo, selecting an optimal end-member mineral spectrum from an end-member spectrum database by adopting a sparse unmixing algorithm model to fit an observation spectrum, and inverting the type, distribution and content of minerals according to the fitting result.
In some embodiments, the cutting, geometric correction and mosaic processing are performed on lunar hyperspectral data stored in a grid format, and a hyperspectral image of the research area is obtained, wherein the hyperspectral image of the research area preliminarily reflects the spectral characteristics and mineral distribution characteristics of the example area.
In some embodiments, the single scattering albedo ωmixCan be expressed as mineral components omegaiLinear combination of (a):
ωmix(λi)＝F1*ω1(λi)+F2*ω2(λi)+…+Fj*ωj(λi)+…+Fk*ωk(λi) (1)
wherein K is the number of mineral species in the mixture, lambdaiIs the wavelength of the ith band, FjIs the relative geometric cross section of the jth mineral.
In some embodiments, L is required for single-scatter sampling prior to spectral unmixing1And (4) normalization calculation to correct the scale difference of the reflectivity between the lunar hyperspectral image data of the research area and the experimental end member spectral data in the end member spectral database.
In some embodiments, a sparse solution blending algorithm is used to find the optimal subset of spectra from the large end-member library to best model each blended pixel in the scene in turn, then each pixel is fitted with the observed spectra, and the blended spectra of each pixel are unmixed according to the fitting result to determine the type and content of minerals.
The sparse unmixing algorithm solution comprises the following steps: and solving and selecting the matching degree of the main mineral spectrum in the hyperspectral data and the end member spectrum library, determining the type of the mineral contained in the hyperspectral data, and solving to obtain the mineral content.
In some embodiments, after reversing the type, distribution and content of minerals, the results of the quantitative inversion of lunar surface minerals are presented as a profile for each mineral.
In some embodiments, the sparse unmixing algorithm solves:
modeling pixels in a scene by finding an optimal subset from a relatively large end-member spectral library;
adding a sparsity constraint in the standard linear decomposition model;
by minimizing the respective lagrangian quantities, it can be converted into an unconstrained form;
fitting the optimized end member mineral spectrum with hyperspectral data obtained by a lunar probe;
and (5) inverting the type and content of the minerals according to the fitting result.
In a second aspect, a system for quantitative analysis of lunar surface minerals based on lunar hyperspectral data is disclosed, comprising:
the data acquisition module is used for acquiring a moon hyperspectral image of a research area;
the end member spectrum database establishing module is used for establishing an end member spectrum database based on a typical lunar returned sample;
the single scattering albedo calculation module is used for respectively calculating single scattering albedo of the lunar hyperspectral image of the research area and the returned sample spectral data in the end member spectral database;
and the mineral inversion module is used for selecting the optimal end-member mineral spectrum from the end-member spectrum database by adopting a sparse unmixing algorithm to fit the observation spectrum based on the calculated single scattering albedo, so that the type, distribution and content of minerals are inverted.
The above one or more technical solutions have the following beneficial effects:
under the condition of not assuming the components and the spectrum of original data, in order to obtain the mineral type, distribution and content characteristics of a research area, firstly, preprocessing (including cutting, geometric correction and inlaying) is carried out on the hyperspectral number of a moon in the research area, secondly, a typical moon is established and returned to a sample laboratory spectral library, thirdly, a Hapke model is adopted to respectively calculate the observation spectral data and the single scattering albedo of the sample spectrum, lastly, a sparse demixing model is adopted to select the optimal end-member mineral spectrum from the spectral library to fit the observation spectrum, according to the fitting result, the type, the distribution and the content of minerals are inverted, and the result shows the quantitative inversion result of the lunar surface minerals by the distribution diagram of each mineral.
With the development of deep space exploration, more and more samples are returned to the outside of the ground, the method can be popularized to mars or other outside-ground planets, important material composition information can be provided for the research of planet geology, and reference data can be provided for the delineation of a research interest area, a preselected landing area and the like.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flowchart illustrating the operation of an embodiment of the present invention;
FIG. 2 is a moon M of an exemplary zone of the present invention3A false color map of hyperspectral data;
FIG. 3 is a plot of a laboratory spectrum of a returned sample selected in accordance with the present invention;
FIG. 4 is a graph of the results of mineral type and content calculations for an exemplary zone of the present invention;
FIG. 5 is a root mean square error graph of the present invention;
fig. 6 is a verification graph of the mineral content interpretation result.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example one
The invention utilizes Moon hyperspectral data and Moon mineral plotter data (Moon mineral Mapper, M) of Moon ship I3Data) is taken as an example, a large end member database is constructed, a sparse solution mixing algorithm is adopted, an optimal subset of spectra is found from the large end member database, optimal modeling is sequentially carried out on each mixed pixel in a scene, then the observation spectra are fitted to each pixel, the mixed spectra of each pixel are unmixed according to the fitting result, the type and the content of minerals are determined, and finally the quantitative inversion result of lunar surface minerals is presented by a distribution graph of each mineral.
The sparse solution hybrid algorithm is based on a sparse model, and the sparse model is calculated by a calculation formula and codes in the following step four.
Usually, a remote sensing pixel only contains a small amount of substances, so that the remote sensing pixel is sparse relative to dozens of spectra in an end-member spectrum library, the spectra are observed through end-member mineral spectrum fitting, the fitting result is within a certain error range, and then the end-member minerals in the end-member mineral spectrum library are the mineral types in the pixel, and meanwhile, in the fitting process, the proportional content of each end-member mineral is the content of the obtained mineral.
The method utilizes the lunar hyperspectral data to identify the type, distribution and content of the lunar surface minerals without assuming the components and spectra of the original data, and visually displays the decoded content distribution map of various minerals.
The embodiment discloses a moon surface mineral quantitative analysis method based on moon hyperspectral data, and the flow chart of the invention is shown in figure 1:
the concrete steps and contents comprise:
step (1) preparation of moon hyperspectral data
The data input by the invention is Moon hyperspectral data which is stored in a grid format, and the data is from Moon mineral plotter data (Moon mineral Mapper, M)3) The resolution ratio is 140m/pixel, the total number of the bands is 85, the spectral range is 430-3000 nm, the effective band is a band 3-85 (82) and is the most effective data for detecting and inverting the full-moon minerals at present.
According to the method, a moon east sea area is taken as an example area, moon hyperspectral data of the same optical period are screened according to the range of a research area, and the data of the research area are subjected to cutting, geometric correction, embedding and other processing, so that a hyperspectral image of the research area is obtained. FIG. 2 is a moon M of an exemplary zone3And (3) a false color map of the hyperspectral data preliminarily reflects the spectral characteristics and mineral distribution characteristics of the sample area.
Selecting the spectral data of the lunar return sample in the step (2)
Currently, some countries accumulate 10 moon ball samples and return about 386kg of samples, wherein Chang' e five returns 1731 g. Among them, reflection Laboratory (RELAB) has conducted a lot of Laboratory spectrum studies (http:// www.planetary.brown.edu/RELAB /) on earth, moon, meteorite and simulation samples, and the Laboratory spectrum curve of the returned sample is selected, as shown in FIG. 3, to construct a relatively complete spectrum library. According to the characteristics of lunar mineral rocks, spectral data of 21 minerals and lunar soil are selected to form an end-member spectral database.
TABLE 1 selected returned sample numbers and characteristics
Calculating single scattering albedo of the lunar hyperspectral data and the returned sample spectral data;
the conversion of reflectance into single-shot scattered reflectance is an important prerequisite for the linear sparse unmixing of minerals. The mixture of mineral reflectivities is non-linear due to the effects of bulk scattering, but its single-scattering albedo can be viewed approximately as a linear mixture, hence the single-scattering albedo ω of a mineral mixturemixCan be expressed as mineral components omegaiLinear combination of (a):
ωmix(λi)＝F1*ω1(λi)+F2*ω2(λi)+…+Fj*ωj(λi)+…+Fk*ωk(λi) (1)
wherein K is the number of mineral species in the mixture, lambdaiIs the wavelength of the ith band, FjIs the relative geometric cross section of the jth mineral.
It should be noted that due to the volume scattering, the mixed reflectance spectrum of a specific mineral is nonlinear, and the single scattering albedo can be approximately regarded as linear mixing, so in order to be able to calculate the type and content of the mineral, the single scattering albedo needs to be calculated first, and the purpose is to convert it into approximate linear mixing.
According to the method, moon hyperspectral data and returned sample laboratory spectral data (namely an end-member mineral spectrum library) are converted into single scattering albedo by adopting a Hapke radiation transmission model, and then a single scattering albedo image and the end-member single scattering albedo spectrum library are unmixed by utilizing a sparse unmixing algorithm.
Assuming that the mineral particles in the mixture have an approximate shape, FjCan be expressed as:
wherein M isjIs the bulk density, p, of the j-th mineraljIs the solid density of the mineral, DjIs the particle diameter. Assuming all particles have similar diameters, FjCan be considered as volume content.
Because of different calibration and measurement conditions, the scale difference of reflectivity exists between the real image data and the experimental end member spectral data, and in order to correct the difference, L needs to be carried out on the single scattering simulation rate before spectral unmixing is carried out1And (3) normalization calculation, wherein the calculation formula is as follows:
wherein λ isiIs the wavelength of the ith band, and L is the number of bands.
Thus, equation (1) can be written in the form:
with respect to F in formula (1)j，Fj' is a corrected volume content, and FjAnd FjThe relationship between' can be expressed as:
wherein the factorjThe scaling coefficients related to the j end member pixels illustrate the variability of signal intensity characteristics in the pixels of the lunar hyperspectral remote sensing data, and are usually the positive scaling coefficients.
After the calculation and the processing of the steps, the single scattering albedo of the moon hyperspectral data and the returned sample (end member mineral) spectral data is calculated, and data preparation is made for sparse unmixing.
Step (4) quantitatively inverting the content of the lunar surface minerals based on a sparse unmixing algorithm
The sparse unmixing algorithm is a new optimization algorithm for solving the linear sparsity problem, and an optimal subset is found in a relatively large end-member spectrum library to model pixels in a scene. Usually, only a small amount of material in a pixel is sparse compared with tens of spectra in the end-member spectrum library, so a sparsity constraint is added to the standard linear decomposition model (formula 6).
Wherein x (mxn) is the volume content, y (lxn) is the observed value, M (lxm) is the end-member spectral library, M is the number of end-member spectra in the spectral library, and N is the number of pixels. Delta > 0 is an error tolerance value, | | x | | | | non-woven phosphor0Is L0Norm, which represents the number of non-zero components of x. However, this is a typical NP (Non-deterministic polynomial) problem that is difficult to solve, so L0Norm quilt L1The norm is instead transformed into an unconstrained form (equation 7) by minimizing the respective lagrangian quantities:
wherein, lambda is more than or equal to 0 and is a regularization parameter, and because the method carries out normalization processing on moon hyperspectral data and end member spectra of the spectral library, non-negative constraint is realized
The sum can automatically be made equal to 1.
Based on the Alternating Direction multiplier (ADMM), the optimal solution problem can be written as (formula 8) by using the Variable splitting and augmented Lagrange algorithm (SUnSAL):
where G is the identity matrix, the detailed process of the optimal solution can be expressed as (equation 9):
where d is the Lagrangian multiplier and μ is the augmented Lagrangian parameter, this value will be determined by taking the original residual (norm (x) of the ADMMk-uk) And binary residual (norm (u))k-uk-1)) is kept within a 10-fold range.
The program code for the variable splitting and augmented lagrange algorithm (SUnSAL) can be written as:
[UF，SF]＝svd(MT*M)；sF＝diag(SF)；IF＝UF*diag(1./(sF+μ))*UFT；
Initializations：x0＝IF*MTy，u0＝x0，d＝0*u0
Set k＝0，μ＞0
xk+1＝(MTM)-1(MTy+μ(uk+dk))
uk+1＝max{0，soft(xk+1-dk，λ/μ)}
dk+1＝dk-(xk+1-uk+1)
k＝k+1
Until k＞iterations||(primal residual＜error&&dual residual＜error).
in the above code, the meaning of diag () is the diagonal element of the matrix, MTIs a transposed matrix of M, the soft function refers to the soft threshold, for soft (x, T), if abs (x) -T > 0, (abs (x) -T)/(abs (x) · x) x is output, otherwise the output value is 0, where abs (x) refers to the absolute value of x.
The algorithm is mainly used for optimizing the optimal end-member mineral spectrum to simulate the observation spectrum, then determining the type and the content of the selected end-member mineral according to the simulation result, if the error of the simulation result is within a certain range, the optimized end-member mineral is the main mineral type in the pixel, and the volume content distributed in the simulation process is the calculated mineral content.
Moon M3The unmixing of the hyperspectral data is carried out according to columns, and the intermediate spectrum of each column is contained in the end-member spectrum library. In the step, the process of sparse unmixing is the process of optimizing the end-member minerals, the sparse unmixing algorithm needs to carry out solution twice, the first solution is to select the main mineral spectrum in the hyperspectral data to match the end-member spectrum library and determine the main range of the minerals, the second solution is to further optimize the end-member mineral spectrum to simulate the observation spectrum, and at the moment, if the error is within a certain range, the calculated type and content of the minerals are the final mineral content result.
The invention takes the east China sea area of the moon as an example area, and the moon M is prepared according to the step one3After the hyperspectral data is calculated in the second step, the third step and the fourth step, the mineral type of the area and the content distribution map of each mineral or lunar soil are obtained, and a hyperspectral image and a Root Mean Square Error (RMSE) are simulated, wherein the RMSE is shown in figure 5. The simulated hyperspectral image is shown in fig. 4, in which (a) the simulated hyperspectral image; (b) lunar soil of highland; (c) lunar sea lunar soil; (d) ilmenite; (e) anorthite; (f) common pyroxene; (g) pyroxene easily variable; (h) according to the results of calculation, the olivine has the main components of high-upland lunar soil and lunar-sea lunar soil on the surface of the area, 5 kinds of minerals can be identified in areas where rocks are exposed or are exposed freshly, the main types of the minerals are ilmenite, plagioclase, common pyroxene, variable pyroxene and olivine, and the types and the contents of the minerals in different areas are different. In addition, as can be seen from the root mean square error graph of fig. 5, the error of the calculation result of the method is relatively small, which indicates that the calculation result is accurate and reliable.
Step (5) verification of mineral content interpretation result
In order to verify the accuracy of the method, the north part of the example area is selected as a verification area, and Mauder is used for impacting the verification areaTaking pit as an example, see a diagram in FIG. 6, the actual observation M is selected from the central peak, the pit bottom, the pit wall, the pit edge and the outer fresh small impact pit of the impact pit respectively3The hyperspectral data and the spectral data after calculation and simulation are compared, and the fitting degree is basically consistent as shown in a graph b in fig. 6, which shows that the method has higher calculation accuracy. The c diagram in fig. 6 is a mineral content diagram, and in addition, the main mineral type and content of each verification point are in accordance with the actual situation, for example, a fresh impact pit outside the Mauder impact pit, namely, the position of the point 1 in the a diagram in fig. 6, the main exposed mineral is plagioclase feldspar, the content is 65%, the second is high lunar soil, which is high lunar soil formed by late weathering, and the positions of the pit edge of the Mauder impact pit, namely, the positions of the point 6 and the point 7, the main exposed minerals are ilmenite, olivine and common pyroxene, but the lunar surface is covered by a large amount of lunar soil, the content is relatively low, and the position is close to the lunar sea area, so that the lunar sea lunar soil is mainly calculated.
In conclusion, the method can accurately determine the mineral type and content, and finally presents the lunar surface mineral quantitative inversion result by the distribution graph of each mineral, so that the method is an accurate, efficient and visual mineral quantitative inversion method.
The method is suitable for identifying and filling the minerals and rocks on the lunar surface, can be popularized to Mars or other extraterrestrial planets, can provide important material composition information for the research of planet geology, and provides reference data for delineating a research interest area, a preselected landing area and the like.
Example two
It is an object of this embodiment to provide a computing device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
EXAMPLE III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
Example four
The object of this embodiment is to provide a lunar surface mineral quantitative analysis system based on lunar hyperspectral data, including:
the data acquisition module is used for acquiring a moon hyperspectral image of a research area;
the end member spectrum database establishing module is used for establishing an end member spectrum database based on a typical lunar returned sample;
the single scattering albedo calculation module is used for respectively calculating single scattering albedo of the lunar hyperspectral image of the research area and the returned sample spectral data in the end member spectral database;
and the mineral inversion module is used for selecting the optimal end-member mineral spectrum from the end-member spectrum database by adopting a sparse unmixing algorithm to fit the observation spectrum based on the calculated single scattering albedo, so that the type, distribution and content of minerals are inverted.
The steps involved in the apparatuses of the above second, third and fourth embodiments correspond to the first embodiment of the method, and the detailed description thereof can be found in the relevant description of the first embodiment. The term "computer-readable storage medium" should be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using general purpose computer means, or alternatively, they can be implemented using program code that is executable by computing means, such that they are stored in memory means for execution by the computing means, or they are separately fabricated into individual integrated circuit modules, or multiple modules or steps of them are fabricated into a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114580524A (en) * | 2022-03-01 | 2022-06-03 | 中国科学院国家天文台 | Training method of geological unit classification model, geological unit classification method and device |
| CN115112575A (en) * | 2022-06-29 | 2022-09-27 | 中国地质科学院地质研究所 | Quantitative inversion method and device for lunar surface mineral content under multi-type spectral variation |
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock Mineral Content Testing Method and System |
| CN118209520A (en) * | 2024-03-29 | 2024-06-18 | 中国地质科学院地质研究所 | Month surface mineral content inversion method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104952050A (en) * | 2015-07-07 | 2015-09-30 | 西安电子科技大学 | Self-adaptive hyperspectral image unmixing method based on region segmentation |
| CN109696406A (en) * | 2018-11-29 | 2019-04-30 | 北京航空航天大学 | A kind of menology high spectrum image shadow region solution mixing method based on compound end member |
| CN111489309A (en) * | 2020-04-07 | 2020-08-04 | 重庆工商大学 | Sparse unmixing pretreatment device and method |
-
2021
- 2021-11-16 CN CN202111353674.6A patent/CN114021372A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104952050A (en) * | 2015-07-07 | 2015-09-30 | 西安电子科技大学 | Self-adaptive hyperspectral image unmixing method based on region segmentation |
| CN109696406A (en) * | 2018-11-29 | 2019-04-30 | 北京航空航天大学 | A kind of menology high spectrum image shadow region solution mixing method based on compound end member |
| CN111489309A (en) * | 2020-04-07 | 2020-08-04 | 重庆工商大学 | Sparse unmixing pretreatment device and method |
Non-Patent Citations (2)
| Title |
|---|
| 宋义刚;吴泽彬;韦志辉;孙乐;刘建军;: "稀疏性高光谱解混方法研究", 南京理工大学学报, no. 04, 30 August 2013 (2013-08-30) * |
| 林红磊;张霞;孙艳丽;: "基于单次散射反照率的矿物高光谱稀疏解混", 遥感学报, no. 01, 23 September 2015 (2015-09-23), pages 53 - 61 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114580524A (en) * | 2022-03-01 | 2022-06-03 | 中国科学院国家天文台 | Training method of geological unit classification model, geological unit classification method and device |
| CN115112575A (en) * | 2022-06-29 | 2022-09-27 | 中国地质科学院地质研究所 | Quantitative inversion method and device for lunar surface mineral content under multi-type spectral variation |
| CN115656053A (en) * | 2022-10-19 | 2023-01-31 | 山东大学 | Rock Mineral Content Testing Method and System |
| CN115656053B (en) * | 2022-10-19 | 2024-05-31 | 山东大学 | Rock mineral content testing method and system |
| CN118209520A (en) * | 2024-03-29 | 2024-06-18 | 中国地质科学院地质研究所 | Month surface mineral content inversion method and device |
| CN118209520B (en) * | 2024-03-29 | 2024-10-29 | 中国地质科学院地质研究所 | Month surface mineral content inversion method and device |
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