CN115561828A - Pegmatite type lithium ore identification method - Google Patents
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Abstract
The invention discloses a pegmatite type lithium ore identification method, which comprises the following steps: s1: acquiring various remote sensing image data of a target area; s2: respectively preprocessing each remote sensing image data to obtain a corresponding preprocessing result; s3: respectively extracting rock and ore information in a preprocessing result corresponding to each remote sensing image data; s4: drawing by utilizing the rock and ore information to obtain a corresponding rock and ore information graph; s5: and performing superposition analysis and spatial analysis on each rock and ore information graph to obtain a pegmatite type lithium ore identification result. The invention can carry out high-resolution, high-precision and real-time in-situ rapid detection on the pegmatite type lithium ore by the multi-source remote sensing technology.
Description
Technical Field
The invention relates to the technical field of ore exploration, in particular to a pegmatite type lithium ore identification method.
Background
Lithium as an energy metal in the 21 st century supports the development of new energy industry and clean energy industry, and is also an irreplaceable strategic resource for aerospace and national defense industry. The research of lithium is taken as the key point of the development of new energy resources in all countries in the world, and the mineral resources of the lithium are correspondingly important strategic mineral resources. The pegmatite type lithium ore is the current main industrial application type in China, the existing pegmatite type lithium ore bed accounts for 60 percent of the total reserve of lithium resources in China, and the pegmatite type lithium ore bed has the characteristics of wide resource distribution, rich taste, shallow burial and high prospecting potential. Although China shows huge potential for finding lithium resources, the exploration rate of lithium resources in China is less than 25%, and 80% of lithium resources are imported mainly from Australia (57%) and 'lithium triangle' areas (23%) in south America. The extremely high external dependence on lithium resources causes severe lithium resource supply risk in China, and the rapid lithium resource exploration and mineral exploration breakthrough are urgently needed.
The existing space remote sensing technology has large detection area, is fast and efficient, makes primary progress on pegmatite type lithium ore vein, but is difficult to accurately interpret ore-containing information and establish an identification model due to low resolution. Therefore, aiming at the characteristics of small scale and multiple bands of pegmatite type lithium ore vein, how to carry out high-resolution, high-precision and real-time in-situ rapid detection on the pegmatite type lithium ore vein and establish a fine identification model through a multi-source remote sensing technology becomes a key problem for realizing high-efficiency lithium resource exploration and ore finding breakthrough at present.
Disclosure of Invention
The invention aims to provide a pegmatite type lithium ore identification method, which is used for carrying out high-resolution, high-precision and real-time in-situ rapid detection on pegmatite type lithium ore veins by a multi-source remote sensing technology.
The technical scheme for solving the technical problems is as follows:
the invention provides a pegmatite type lithium ore identification method, which comprises the following steps:
s1: acquiring various remote sensing image data of a target area;
s2: respectively preprocessing each remote sensing image data to obtain a corresponding preprocessing result;
s3: respectively extracting rock and ore information in a preprocessing result corresponding to each remote sensing image data;
s4: drawing by utilizing the rock and ore information to obtain a corresponding rock and ore information graph;
s5: and performing superposition analysis and spatial analysis on each rock and ore information graph to obtain a pegmatite type lithium ore identification result.
Optionally, in step S1, the plurality of remote sensing image data at least include multispectral data and hyperspectral data.
Optionally, in the step S2, the preprocessing includes: radiation calibration, atmospheric correction and geometric correction;
the step S2 includes:
s21: respectively carrying out radiation calibration on each kind of remote sensing image data to obtain a radiation calibration result corresponding to the remote sensing image;
s22: respectively carrying out atmospheric correction on each radiation calibration result to obtain a corresponding atmospheric correction result;
s23: respectively carrying out geometric correction on each atmospheric correction result to obtain a corresponding geometric correction result;
s24: and outputting the corresponding geometric correction result as the corresponding preprocessing result.
Alternatively, step S3 comprises:
s31: extracting first rock and mineral information in a preprocessing result of the multispectral data;
s32: and extracting second rock and ore information in the preprocessing result of the imaging hyperspectral data.
Alternatively, the S31 includes:
s311: respectively performing dimensionality reduction on the preprocessing results of the multispectral data to obtain rock mass and construction information;
s312: carrying out alteration information mapping on the preprocessing result of the imaging multispectral data to obtain a mapping result;
s313: extracting alteration information and mineral information in the mapping result;
s314: extracting potential regions containing pegmatite in the mapping result by using mixed tuning matched filtering;
s315: outputting the rock mass and formation information, the alteration information, the mineral information, and the potential region containing pegmatite as the first rock-mineral information.
Optionally, the multispectral data includes Landsat-8 multispectral remote sensing images and ASTER multispectral remote sensing images, the mapping result includes spectral features of hydrothermally altered minerals, and the step S313 includes:
according to the image band information corresponding to the spectral characteristics of the hydrothermally altered minerals;
extracting hydroxyl alteration information of the Landsat-8 multispectral remote sensing image in the wave band ranges of 0.45-0.51 mu m, 0.85-0.88 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m;
extracting Fe in the waveband ranges of 0.45-0.51 mu m, 0.64-0.67 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m from the Landsat-8 multispectral remote sensing image 2+ And Fe 3+ The mineral information of (a);
extracting Al-OH alteration information of the ASTER multispectral remote sensing image in the wave band ranges of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.185-2.225 mu m;
extracting Mg-OH alteration information of the ASTER multispectral remote sensing image in the wave band range of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.295-2.365 mu m;
extracting Fe content of the ASTER multispectral remote sensing image in the wave band range of 0.520-0.600 μm, 0.630-0.690 μm, 0.780-0.860 μm and 1.600-1.700 μm 2+ And Fe 3+ The mineral information of (1).
Optionally, the hyperspectral data at least comprises GF-5 hyperspectral data and imaging hyperspectral data, and the step S32 comprises:
s321: extracting the wave spectrum of the altered mineral in the preprocessing result of the GF-5 hyperspectral data by using a wave spectrum hourglass tool;
s322: determining an end-member spectrum in the spectrum using hybrid tuned matched filtering;
s323: extracting structure-texture-hue features in the end-member spectrum;
s324: determining different granite masses according to the structure-texture-hue characteristics;
s325: extracting spectral characteristics of different granite masses in the preprocessing result of the imaging hyperspectral data by using an envelope removal mode;
s326: outputting the structure-texture-hue feature and the spectral feature as the second rock information.
The invention has the following beneficial effects:
the invention aims at pegmatite type lithium ore as a research object, identifies and extracts lithium ore alteration minerals and lithium-containing minerals by multi-source remote sensing technologies with different spatial scales, different resolutions and different precisions, establishes pegmatite type lithium ore information extraction processes, and develops four-major-characteristic remote sensing identification technologies of multi-source remote sensing data pegmatite type lithium ore. The method can greatly improve the detection technical capability of key mineral resources in China, provide important technical support for realizing safe supply of lithium resources in China, and simultaneously improve the international control power and speaking right in China and the capability of promoting the conversion of resource advantages to economic advantages.
Drawings
Fig. 1 is a flow chart of a pegmatite-type lithium ore identification method according to the present invention;
FIG. 2 is a schematic diagram of principal component analysis pseudo color synthesis; (A) Landsat-8OLI RGB-PC1, PC2, PC3 and (B) ASTER RGB-PC4, PC2, PC1 Principal Component Analysis (PCA);
FIG. 3 (A) a graphical representation of hydroxyl-containing altered mineral light in the spectral pass band of Landsat-8OLI and (B) ASTER data (from the United States Geological Survey (USGS) spectral library, version 7 spectral curves);
FIG. 4 (A) Landsat-8OLI and (B) an iron-containing altered mineral spectrum in the spectral pass band of the ASTER data (spectral curve from USGS spectral library 7 th edition);
FIG. 5 is a pseudo color gradient plot of RGB combined prominent hydroxyl alterations (A) RGB 432, PC4 regular image-al/Mg-OH mineral image-map of Landsat-8OLI base map; (B) ASTER base map RGB321, PC4 regular image-al/Mg-OH mineral image-pseudo color gradient map of map;
FIG. 6 is (A) base image of RGB 432 for Landsat-8OLI, PC3 regular image-Fe 2+ /Fe 3+ Oxide/hydroxide images; (B) RGB321 is ASTER base map, PC3 regular image-Fe 2+ /Fe 3+ Oxide/hydroxide images;
FIG. 7 is a VNIR-SWIR spectrum of a hand sample collected by an ASD, plotted against the spectrum of Longmen lithium-containing pegmatite, dahong Liu Tan lithium-containing pegmatite, and Dahong Liu Tan tourmaline-containing pegmatite;
fig. 8 is a schematic of the RGB composition of the highlighted pegmatite: (A) RGB 432 for the Landsat-8OLI base map; (B) RGB321 for ASTER base map;
fig. 9 is a mineral spectrum of a hand sample collected by ASD: (a-E) hand samples of spodumene, lepidolite, lithium-containing pegmatite, unmineralized pegmatite, and muscovite granite; (a-c) Likedite-containing pegmatite spectra. (d-f) Muscovitum-containing pegmatite spectra; (g-I) lithium-containing pegmatite spectra; (j-l) non-mineralised pegmatite spectra. (m-o) Muscovite granite Spectrum.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The invention provides a pegmatite type lithium ore identification method, which is shown in figure 1 and comprises the following steps:
s1: acquiring various remote sensing image data of a target area;
the multiple remote sensing image data at least comprise multispectral data and hyperspectral data.
(1) Multispectral image data
The Landsat-8 satellite is part of a terrestrial satellite data continuity mission that was launched on day 11, 2 months, 2013. Landsat-8 has two sensors, including a business land imager (OLI) and a thermal infrared sensor (TIRS), covering 11 bands from visible to thermal infrared, as shown in Table 1. 9 visible-near infrared and short-wave infrared bands (0.43-2.29 μm) and two thermal infrared bands (10.60-12.51 μm) provide spatial resolutions of 30 and 100 meters, respectively. The stripe width of one Landsat-8OLI scene is 185km (each scene covers 185 x 185km of area 2 )。
The ASTER sensor is an advanced satellite-borne heat emission and reflection radiometer mounted on the Terra of the first Earth Observation System (EOS) series satellite of the United states aerospace administration (NASA), and emits 12 months and 18 days in 1999. ASTER contains 3 visible near infrared bands (0.52-0.86 μm) with a spatial resolution of 15m, 6 short-wave infrared bands (1.60-2.43 μm) with a spatial resolution of 30m, and 5 thermal infrared bands (8.13-11.65 μm) with a spatial resolution of 90 m. Each ASTER scene gets 60km of stripes and is subdivided into 60 x 60km stripes 2 Scenario (table 1).
TABLE 1 multispectral image parameters
(2) Hyper-spectral image data
The high-resolution No. 5 satellite is the only high-spectrum satellite in 7 civil satellites in the major project of the high-resolution earth observation system, is the satellite with the most carrying load and the highest spectral resolution in the current major scientific and technological project of China, and is also the optical remote sensing satellite with the most domestic detection means. The satellite carries 6 loads of an atmospheric trace gas differential absorption spectrometer, an atmospheric main greenhouse gas monitor, an atmospheric multi-angle polarization detector, an atmospheric environment infrared very high resolution detector, a visible short wave infrared hyperspectral camera and a full-wave band spectral imager for the first time, and can monitor a plurality of environments such as atmospheric aerosol, sulfur dioxide, nitrogen dioxide, carbon dioxide, methane, water quality, land vegetation, urban heat islands and the like. Specific satellite loading parameters are shown in table 2.
TABLE 2 high-score No. 5 sensor parameters
S2: respectively preprocessing each remote sensing image data to obtain a corresponding preprocessing result;
here, the preprocessing includes: radiation calibration, atmospheric correction and geometric correction;
said step S2 therefore comprises the following sub-steps:
s21: respectively carrying out radiation calibration on each kind of remote sensing image data to obtain a radiation calibration result corresponding to the remote sensing image;
s22: respectively carrying out atmospheric correction on each radiation calibration result to obtain a corresponding atmospheric correction result;
s23: respectively carrying out geometric correction on each atmospheric correction result to obtain a corresponding geometric correction result;
s24: and outputting the corresponding geometric correction result as the corresponding preprocessing result.
S3: respectively extracting rock and ore information in a preprocessing result corresponding to each remote sensing image data;
alternatively, step S3 comprises:
s31: extracting first rock and mineral information in a preprocessing result of the multispectral data;
specifically, the S31 includes:
s311: respectively performing dimensionality reduction on the preprocessing results of the multispectral data to obtain rock mass and construction information;
the invention utilizes principal component analysis to perform dimensionality reduction processing on the preprocessing result of the multispectral data so as to centralize the main information of the image in the first few wave bands, thereby highlighting the rock mass and the construction information. The RGB combination of Landsat-8OLI uses PC1, PC2, and PC3, and the RGB combination of ASTER uses PC4, PC2, and PC1 to perform pseudo color synthesis, and the information such as the geologic body and structure is highlighted, as shown in fig. 2.
Principal Component Analysis (PCA) is a multidimensional orthogonal linear transformation method based on multivariate statistics, and is a common method for remotely sensing abnormal information of alteration at present. The feature principal component analysis is a method of performing principal component analysis according to the spectral feature of the target and the band principal component analysis corresponding to the selected spectral diagnostic feature, and extracting the information of the target ground object according to the magnitude of the contribution coefficient of each principal component of the feature vector matrix.
The principle is as follows: let the original variable be x 1 ，x 2 ，…，x m Then the new variable of the linear combination is F 1 ，F 2 ，…，F s (s is less than or equal to m), and the concrete formula is as follows:
F 1 ，F 2 ，…，F s are respectively x 1 ，x 2 ，…，x m The first principal component …, Z ij (i =1,2, …, s; j =1,2, …, m) as variables in each principal component x i A factor load of above, respectively x 1 ，x 2 ，…，x m The correlation matrix of (2) feature vectors corresponding to s larger eigenvalues. The principal component analysis method can enable as much useful information as possible to be intensively distributed in a few principal components, thereby achieving the purposes of data compression and dimension reduction.
As shown in FIG. 2A, which is a pseudo color synthetic image of Landsat-8OLI, by a principal component method, it can be shown that there is a significant difference between geologic bodies with different lithologies and linear structure imaging. The north part of the research area is broken off in odd stages, and the colors of two sides of the fault are different. The north part of the fault is mainly green, and the south part is mainly pink. The overall appearance is shown extending in the NW direction. The image of the broken southern big Taohuatan-Guo Zacuo is prominent due to different stratums at two sides, and the southern side is mainly pink green. Fig. 2A shows the lenticular and veined shape of the granite mass. Different colors represent different strata. Pink green is the great wall system of the Tianshui sea rock group, rose is the Sanplet system of the Baankara rock group. The mountain and yellow-green groups belong to the dimeric Huang Yangling group. Fig. 2B is a principal component color composite image of ASTER. The image takes red and blue as main parts, the linear structure is protruded, and the fault structure takes a red and blue transition region as a mark.
S312: carrying out alteration information mapping on the preprocessing result of the imaging multispectral data to obtain a mapping result;
optionally, the multispectral data includes Landsat-8 multispectral remote sensing images and ASTER multispectral remote sensing images, and the mapping result includes spectral features of the hydrothermally altered minerals, and certainly does not include spectral features of the hydrothermally altered minerals.
S313: extracting alteration information and mineral information in the mapping result;
based on this, extracting the alteration information and the mineral information in the mapping result comprises:
according to the image wave band information corresponding to the spectral characteristics of the hydrothermally altered minerals;
extracting hydroxyl alteration information of the Landsat-8 multispectral remote sensing image in the wave band ranges of 0.45-0.51 mu m, 0.85-0.88 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m;
extracting Fe-containing Landsat-8 multispectral remote sensing images with wave band ranges of 0.45-0.51 mu m, 0.64-0.67 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m 2+ And Fe 3+ The mineral information of (a);
extracting Al-OH alteration information of the ASTER multispectral remote sensing image in the wave band ranges of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.185-2.225 mu m;
extracting Mg-OH alteration information of the ASTER multispectral remote sensing image in the wave band range of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.295-2.365 mu m;
extracting the ASTER multispectral remote sensing image with the wave band ranges of 0.520-0.600 mu m, 0.630-0.690 mu m, 0.780-0.860 mu m and 1.600-1.700 mu mContaining Fe 2+ And Fe 3+ The mineral information of (1).
Other information corresponding to specific band ranges can be referred to in table 1. Referring to FIG. 3, the extraction results of Al-OH alteration information and Mg-OH alteration information are shown, and the Landsat-8 multispectral remote sensing image contains Fe with wave bands of 0.45-0.51 μm, 0.64-0.67 μm, 1.57-1.65 μm and 2.11-2.29 μm 2+ And Fe 3+ The mineral information and the ASTER multispectral remote sensing image contain Fe with the wave band ranges of 0.520-0.600 mu m, 0.630-0.690 mu m, 0.780-0.860 mu m and 1.600-1.700 mu m 2+ And Fe 3+ The extraction result of the mineral information of (2) is shown with reference to fig. 4.
TABLE 3 Landsat8 OIL data 2, 5, 6, 7 principal component analysis feature vector Table
| Eigenvectors | Band2 | Band5 | Band6 | Band7 |
| PC1 | 0.23 | 0.52 | 0.63 | 0.53 |
| PC2 | 0.68 | 0.51 | -0.39 | -0.34 |
| PC3 | 0.63 | -0.61 | -0.11 | 0.47 |
| PC4 | -0.29 | 0.29 | -0.66 | 0.63 |
TABLE 4 ASTER data 1, 3, 4, 6 principal component analysis feature vector Table
| Eigenvectors | Band1 | Band3 | Band4 | Band6 |
| PC1 | -0.11 | -0.97 | -0.21 | -0.04 |
| PC2 | -0.69 | -0.08 | -0.70 | 0.16 |
| PC3 | -0.72 | 0.23 | -0.64 | -0.18 |
| PC4 | -0.02 | 0.01 | -0.25 | 0.97 |
TABLE 5 ASTER data 1, 3, 4, 8 principal component analysis feature vector Table
| Eigenvectors | Band1 | Band3 | Band4 | Band8 |
| PC1 | -0.11 | -0.97 | -0.21 | -0.03. |
| PC2 | -0.70 | -0.07 | -0.71 | 0.10 |
| PC3 | -0.71 | 0.22 | -0.66 | -0.13 |
| PC4 | -0.02 | 0.01 | -0.16 | 0.99 |
Referring to tables 3 to 5, PC4 was used as the best principal component for extracting hydroxyl anomalies in Landsat-8OLI and ASTER, depending on the magnitude and sign of the eigenvector loadings, and the extraction results of hydroxyl alterations are shown in fig. 5. As shown in fig. 6, PC3 is the most preferable main component of iron-stained and altered minerals.
S314: extracting potential regions containing pegmatite in the mapping result by using mixed tuning matched filtering;
according to the spectral characteristics of the pegmatite containing lithium and the pegmatite containing electrolithargite (figure 7), mineral information is extracted by adopting a method of performing mixed pixel decomposition on Landsat-8OLI and ASTER multispectral data, the pegmatite containing lithium and the pegmatite containing electrolithargite are used as end-member spectra, and the Landsat-8OLI and ASTER images are processed by using mixed tuning matching to perform a potential region of the pegmatite containing minerals (figure 8).
S315: outputting the rock mass and formation information, the alteration information, the mineral information, and the potential region containing pegmatite as the first rock-mineral information.
S32: and extracting second rock and ore information in the preprocessing result of the imaging hyperspectral data.
Optionally, the hyperspectral data at least comprises GF-5 hyperspectral data and imaging hyperspectral data, and the step S32 comprises:
s321: extracting the wave spectrum of the altered mineral in the preprocessing result of the GF-5 hyperspectral data by using a wave spectrum hourglass tool;
the spectrum refers to a mineral spectrum obtained by minimum noise separation (MNF), pure Pel Index (PPI), and Spectral Angle Mapping (SAM).
S322: determining an end-member spectrum in the spectrum using hybrid tuned matched filtering;
s323: extracting structure-texture-hue features in the end-member spectrum;
s324: determining different granite masses according to the structure-texture-hue characteristics;
according to the information of the structure, texture, color tone and the like of the image, different structural fractures of the area are interpreted, and meanwhile, the granite mass of the area is extracted by combining the distribution of the lithology of the geological map of the area.
According to remote sensing and field investigation, mineralized pegmatite is found in the big red willow beach area at high altitude. Meanwhile, beneficial areas of the mineral-containing pegmatite are defined by field investigation in the field.
In the present invention, in the structure-texture-hue feature, the structural features include: the construction information of ore control, ore guiding, ore containing and the like is 'linear'; information on "ring" volcanic mechanisms, intrusions, etc.; "band" mineral source layer information; the 'block' constructs characteristics such as rock information.
The texture features include: smooth, rough, fine, velvet and other surface textures, and different levels are divided by the smoothness-roughness. Texture is not only dependent on surface features but also on illumination angle, contrast. The texture feature is an important non-spectral feature, and objects with similar spectral features can be identified through texture difference.
The hue characteristics include: and extracting and analyzing characteristic information such as color blocks, color halos, color spots, color bands and the like and researching the information and the correlation to extract the hue characteristic in the hyperspectral map information.
Wherein, the color of the surrounding rock is black and yellow, the outcrop area is large, and the bedding is obvious; the color of granite is bright white, the texture is rough, the outcrop area is large, and the boundary line with other lithology on the periphery is obvious; the pegmatite is white, grey tone and stripe-shaped shadow pattern characteristics, the shape is generally irregular, the corners are smooth and directional; pegmatite has the highest reflectivity, granite sub-grade, and gneiss and phyllite have the lowest reflectivity. In addition, the granite pegmatite vein containing the andalusite is light yellow green; granite pegmatite vein containing spodumene and lepidolite is light blue; the black cloud Erythrostictyite is bright white-pink white; the pale blue-green color may be granite pegmatite with andalusite and spodumene.
S325: extracting spectral characteristics of different granite masses in the preprocessing result of the imaging hyperspectral data by using an envelope removal mode;
the effectiveness of the pixel map was evaluated by envelope elimination highlighting spodumene, lepidolite, mineral pegmatite, mineral-free pegmatite, muscovite, and muscovite granite for spectral feature extraction (figure 9), wavelength positions of the deepest features and their depths between 2.195 μm to-2.220 μm.
S326: outputting the structure-texture-hue feature and the spectral feature as the second rock information.
S4: drawing by using the rock and mine information to obtain a corresponding rock and mine information graph;
s5: and performing superposition analysis and spatial analysis on each rock and ore information graph to obtain a pegmatite type lithium ore identification result.
Through ArcGIS superposition analysis and spatial analysis functions, the rock and ore information is superposed to generate a new data layer, the result integrates all characteristic attributes in the content, and simultaneously, superposition analysis not only generates a new spatial relationship, but also associates the attributes of a plurality of input data layers to generate a new attribute relationship. The method is one of methods for extracting spatial hidden information, and refers to an analysis method for superposing data of different frames or different data layers together to generate new attributes at corresponding positions of a superposed map. Determining raster data superposition and vectors such as the mineral points and the structures of different data layers on the basis of the raster and vector data information such as the structure information and the mineral point information acquired in the steps, further judging the spatial relationship between the mineral points and each layer of the raster, determining the attribute types, the areas and other parameter values of other layers in each raster layer, combining the attributes of different raster layers, and finally delineating the mineral finding target area of mineral-bearing pegmatite hidden in the remote sensing image.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1.A pegmatite-type lithium ore identification method is characterized by comprising the following steps:
s1: acquiring various remote sensing image data of a target area;
s2: respectively preprocessing each remote sensing image data to obtain a corresponding preprocessing result;
s3: respectively extracting rock and ore information in a preprocessing result corresponding to each remote sensing image data;
s4: drawing by utilizing the rock and ore information to obtain a corresponding rock and ore information graph;
s5: and performing superposition analysis and spatial analysis on each rock and ore information graph to obtain a pegmatite type lithium ore identification result.
2. The pegmatite-type lithium ore identification method according to claim 1, wherein in the step S1, the plurality of remote sensing image data at least comprise multispectral data and hyperspectral data.
3. The pegmatite-type lithium ore identification method according to claim 1, wherein in the step S2, the preprocessing comprises: radiation calibration, atmospheric correction and geometric correction;
the step S2 includes:
s21: respectively carrying out radiation calibration on each kind of remote sensing image data to obtain a radiation calibration result corresponding to the remote sensing image;
s22: respectively carrying out atmospheric correction on each radiation calibration result to obtain a corresponding atmospheric correction result;
s23: respectively carrying out geometric correction on each atmospheric correction result to obtain a corresponding geometric correction result;
s24: and outputting the corresponding geometric correction result as the corresponding preprocessing result.
4. The pegmatite-type lithium ore identification method according to claim 2, wherein step S3 comprises:
s31: extracting first rock and mineral information in a preprocessing result of the multispectral data;
s32: and extracting second rock and ore information in the preprocessing result of the imaging hyperspectral data.
5. The pegmatite-type lithium ore identification method according to claim 4, wherein the S31 comprises:
s311: respectively performing dimensionality reduction on the preprocessing results of the multispectral data to obtain rock mass and construction information;
s312: carrying out alteration information mapping on the preprocessing result of the imaging multispectral data to obtain a mapping result;
s313: extracting alteration information and mineral information in the mapping result;
s314: extracting potential regions containing pegmatite in the mapping result by using mixed tuning matched filtering;
s315: outputting the rock mass and formation information, the alteration information, the mineral information, and the potential region containing pegmatite as the first rock-mineral information.
6. The pegmatite-type lithium ore identification method according to claim 5, wherein the multispectral data comprises Landsat-8 multispectral remote sensing images and ASTER multispectral remote sensing images, the mapping result comprises spectral characteristics of hydrothermally altered minerals, and the step S313 comprises:
according to the image band information corresponding to the spectral characteristics of the hydrothermally altered minerals;
extracting hydroxyl alteration information of the Landsat-8 multispectral remote sensing image in the wave band ranges of 0.45-0.51 mu m, 0.85-0.88 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m;
extracting Fe-containing Landsat-8 multispectral remote sensing images with wave band ranges of 0.45-0.51 mu m, 0.64-0.67 mu m, 1.57-1.65 mu m and 2.11-2.29 mu m 2+ And Fe 3+ The mineral information of (a);
extracting Al-OH alteration information of the ASTER multispectral remote sensing image in the wave band ranges of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.185-2.225 mu m;
extracting Mg-OH alteration information of the ASTER multispectral remote sensing image in the wave band range of 0.520-0.600 mu m, 0.780-0.860 mu m, 1.600-1.700 mu m and 2.295-2.365 mu m;
extracting Fe content of the ASTER multispectral remote sensing image in the wave band range of 0.520-0.600 μm, 0.630-0.690 μm, 0.780-0.860 μm and 1.600-1.700 μm 2+ And Fe 3+ The mineral information of (1).
7. The pegmatite-type lithium ore identification method according to claim 4, wherein the hyperspectral data at least comprises GF-5 hyperspectral data and imaging hyperspectral data, and the step S32 comprises:
s321: extracting the wave spectrum of the altered mineral in the preprocessing result of the GF-5 hyperspectral data by using a wave spectrum hourglass tool;
s322: determining an end-member spectrum in the spectrum using hybrid tuned matched filtering;
s323: extracting structure-texture-hue features in the end-member spectrum;
s324: determining different granite masses according to the structure-texture-hue characteristics;
s325: extracting spectral characteristics of different granite masses in the preprocessing result of the imaging hyperspectral data by using an envelope removal mode;
s326: outputting the structure-texture-hue feature and the spectral feature as the second rock information.
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