Hyperspectral satellite target rock and mine identification method and system based on spectral characteristic parameters

Abstract

The invention discloses a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters. The method comprises the following steps: S1, collecting and preprocessing a standard rock and mineral reflection spectrum curve; s2, collecting a regional hyperspectral satellite image and carrying out image preprocessing; s3, identifying diagnostic spectral reflection and absorption characteristics of the target rock mine based on the standard curve; s4, screening and extracting a diagnostic spectral range of the hyperspectral satellite image; s5, calculating diagnostic spectral characteristic parameters of the hyperspectral image target rock mine; and S6, on the basis of the S5, processing the spectral characteristic parameters obtained by calculation, and realizing target rock and mine identification based on multi-dimensional diagnosis spectral characteristics. According to the method, the application potential of the hyperspectral satellite image in the field of remote sensing geology is effectively improved and the scientificity and the accuracy of the hyperspectral satellite image in large-range rock and ore recognition drawing application are improved through the target rock and ore standard spectral curve characteristic analysis, hyperspectral satellite image processing and spectral characteristic parameter estimation methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of remote sensing image processing, and in particular relates to a method and system for identifying target rocks and minerals using hyperspectral satellites based on spectral characteristic parameters. Background Art

[0002] With the rapid development of hyperspectral remote sensing technology, the availability of various satellite hyperspectral imagery data has greatly increased. Due to their high spatial and spectral resolution, their applications in large-scale regional geological mapping, rock and mineral identification, and mineral exploration are becoming increasingly widespread and in-depth. Altered minerals are often closely associated with hydrothermal deposits, and their zoning characteristics are often important indicators for finding such deposits. Therefore, using hyperspectral remote sensing satellite imagery to identify altered minerals is crucial for narrowing prospecting targets and improving exploration efficiency. However, traditional hyperspectral rock and mineral identification methods typically rely on full-spectral waveform matching within the visible and near-infrared bands, such as spectral angle mapping and minimum distance methods. However, the hyperspectral diagnostic spectral signatures of different mineral types often vary significantly, and full-waveform matching methods add a significant amount of redundant information and are prone to introducing error noise. Furthermore, existing hyperspectral rock and mineral identification methods lack in-depth consideration of the diagnostic spectral signatures of the rock and mineral itself, often failing to fully utilize the spectral reflectance mechanism characteristics of different minerals, resulting in limitations in accuracy and precision.

[0003] In response to the above problems, the shortcomings of existing technologies are mainly manifested in two aspects: first, there is a lack of an effective idea for considering the spectral reflectance mechanism analysis of different types of minerals to achieve efficient and rapid analysis of different types of minerals; second, the existing hyperspectral mineral mapping technology lacks a clear extraction process based on the spectral characteristic parameters of the target mineral for identification, especially when dealing with the problem of identifying minerals of the same family.

[0004] Therefore, there is an urgent need to develop a hyperspectral image target rock and mineral identification method and system based on spectral characteristic parameters to solve the above technical problems and meet the needs of efficient and accurate identification of regional rocks and minerals in practical applications. Summary of the Invention

[0005] In order to solve the technical problems existing in the background technology, the present invention aims to provide a hyperspectral satellite target rock and mineral identification method and system based on spectral characteristic parameters.

[0006] In order to solve the technical problem, the technical solution of the present invention is:

[0007] A hyperspectral satellite target rock and mineral identification method based on spectral characteristic parameters, the method comprising:

[0008] S1: Obtain the standardized spectral curve of each mineral, calculate the diagnostic spectral characteristics of the absorption valley position, depth, width, symmetry and reflection peak position, identify the corresponding diagnostic band interval, construct a mineral-spectral parameter comparison table, and output the spectral characteristic parameters and diagnostic band interval of each mineral;

[0009] S2: Map the diagnostic band intervals extracted in S1 to the corresponding actual bands in the preprocessed hyperspectral surface reflectance image, construct a mineral-band mapping table, extract the valid band subset, and perform consistency verification to ensure complete coverage of the mineral key spectral region, and finally output the diagnostic band subset image;

[0010] S3: Perform SG filtering and smoothing on the diagnostic band subset image output by S2, and perform continuum removal. Calculate the absorption center, depth, width, symmetry, and reflection peak characteristic parameters pixel by pixel according to the spectral parameter formula in S1, and output them as multiple spectral characteristic layers respectively;

[0011] S4: Based on the standard spectral characteristic parameters extracted by S1, the pixels in the S3 output layer are gradually screened, and the mineral target pixels that meet all the diagnostic conditions are identified using the multi-parameter superposition method, and the target mineral distribution map is output; finally, the accuracy is verified in combination with ground surveys or existing data, and the identification result map, spectral parameter atlas and verification report are output.

[0012] Furthermore, standard rock and mineral reflectance spectral curves are collected and preprocessed, including the measured spectra of the target rocks and minerals and the spectral curves of the typical surface spectral library, and filtered smoothing and envelope removal are performed to obtain standardized spectral curves of each mineral; hyperspectral satellite images are preprocessed, including radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal, to obtain preprocessed hyperspectral surface reflectance images.

[0013] Furthermore, the step S1 includes:

[0014] S101: Based on the constructed average reflectance spectrum curve of each mineral, analyze its key spectral characteristics in the visible-shortwave infrared band, including the central wavelength, bandwidth, symmetry, absorption depth, reflection peak position and relative reflectance variation characteristics of the typical absorption band, and identify the diagnostic spectral parameters of the target altered mineral to characterize its uniqueness and distinguishability;

[0015] S102: Extract the main response spectrum interval of the target mineral based on the wavelength range where the diagnostic spectral features appear, and define the effective wavelength range with identification value in the hyperspectral image;

[0016] S103: Based on the extracted diagnostic spectral interval, the entire hyperspectral satellite image is subjected to spectral compression processing, and only the band data related to the characteristic response of the target mineral is retained to reduce data redundancy and improve the efficiency of subsequent analysis;

[0017] S104: Establish a diagnostic spectral interval index table for the target mineral, record its key absorption bands and corresponding wavelength information, and provide prior support for mineral identification in hyperspectral images.

[0018] Furthermore, the step S101 includes:

[0019] S1010: Preprocess the average reflectance spectrum curve of the target mineral by using Savitzky-Golay (SG) filtering to smooth and denoise, and combine the spectral envelope removal method to enhance the absorption valley characteristics. Then resample to the corresponding band range of the hyperspectral satellite to provide a standardized basic curve for spectral feature parameter extraction;

[0020] S1011: Determine the average reflectance spectrum curve of the target mineral, and record the curve as R(λ), where λ represents the wavelength and R represents the reflectance at the corresponding wavelength, which is used as the basic data source for subsequent spectral feature parameter extraction;

[0021] S1012: Calculate the absorption center position, i.e., the minimum reflectivity wavelength. By performing a local minimum analysis on the spectrum curve, the calculation formula for the absorption center wavelength λc is: Where [λ1,λ2] is the predefined absorption range;

[0022] S1013: Calculate the absorption depth D, which is defined as the ratio of the reflectivity at the absorption center wavelength to the envelope value. The calculation formula is: Among them, Wei R cont (λ c ) is the envelope reflectivity value at the absorption center wavelength;

[0023] S1014: Calculate the absorption band width W, that is, the wavelength range width corresponding to the reflectivity being lower than a certain relative threshold. The calculation formula is: W = λ r -λ l , where λ l and λ r , respectively, the left and right absorption edge wavelengths, satisfying R(λ l )=R(λ r )=0.9×R cont (λ);

[0024] S1015: Calculate the absorption symmetry S, which is used to characterize the degree of symmetry of the absorption morphology. The calculation formula is: When S ≈ 0.5, it indicates that the absorption characteristics are basically symmetrical, and deviations from 0.5 indicate increased asymmetry;

[0025] S1016: Extract the reflection peak position λmax, that is, the local maximum wavelength in the adjacent region of the absorption band. The calculation formula is: Where λ∈[λ0,λ1], λ represents the wavelength; R(λ) represents the reflectivity corresponding to the wavelength; λ0,λ1 represent the left and right boundaries of a spectral interval.

[0026] Furthermore, the step S2 includes:

[0027] S201: Based on the extracted diagnostic spectral characteristic parameters of the target altered mineral, including absorption center, absorption width, and characteristic reflection peak information, determine the key response band interval corresponding to each mineral and construct a mineral-band mapping table;

[0028] S202: Compare the constructed key band interval mapping table with the actual band information of the hyperspectral satellite image to identify the valid band set corresponding to the diagnostic features in the hyperspectral image, denoted as ∈;

[0029] S203: extracting the band data corresponding to ∈ in the entire hyperspectral image to form a diagnostic band image subset, eliminating bands that are irrelevant to the target mineral spectral response or have redundant information, and reducing the data dimension;

[0030] S204: Perform consistency check on the extracted band subset to ensure that the selected band completely covers the key spectral characteristic area of ​​the mineral and is consistent with the standard spectral band after resampling;

[0031] S205: Using the screened diagnostic band subset as input data for subsequent spectral characteristic parameter calculation and mineral identification comparison, so as to reduce the overall processing calculation amount and improve the spectral accuracy and calculation efficiency of mineral identification.

[0032] Furthermore, the step S3 includes:

[0033] S301: Based on the diagnostic spectrum range of the target mineral identified in S2, all bands belonging to the diagnostic spectrum are screened from the hyperspectral satellite image;

[0034] S302: performing SG filtering and continuum removal operations on the hyperspectral image after screening out the diagnostic spectral bands to further highlight the spectral absorption / reflection characteristics of the target mineral;

[0035] S303: Based on the target mineral spectral characteristic parameter calculation formula given in S1, the spectral characteristic parameters of the target mineral are calculated pixel by pixel in the hyperspectral image to generate corresponding spectral characteristic parameter calculation results.

[0036] Furthermore, the step S4 includes:

[0037] S401: combining the target mineral standard spectral characteristic parameters identified in S1, performing step-by-step analysis on the calculated target mineral spectral characteristic parameter results;

[0038] S402: First, based on the absorption position of the target mineral standard spectrum obtained in S1, the calculated absorption position results are screened to extract pixels that meet the requirements in the calculated results;

[0039] S403: For each pixel in the preliminary candidate area, further calculate multiple characteristic parameters such as the absorption band width, absorption depth and symmetry, compare them with the target mineral standard value in S1, use the set threshold range to make judgments respectively, and construct a multi-parameter discrimination matrix;

[0040] S404: Based on the multi-parameter discriminant matrix, weighted scoring or logical combination is performed on the conformity of each pixel, and pixels that simultaneously meet multiple key parameter characteristics are extracted as the final target mineral identification result;

[0041] S405: Visualize the spatial distribution of the identified target mineral area and interpret and confirm it in combination with geological background data;

[0042] S406: Based on the above extracted pixel results that meet the different spectral characteristic parameters of the target mineral, a comprehensive analysis is performed on the pixels that meet multiple spectral characteristic parameters of the target mineral, and the pixels are considered to be the target mineral to be identified;

[0043] S407: Conduct ground verification of the identified different target minerals, select ground targets with known characteristics and accurate geographic information, conduct actual comparison of the identification results to verify their accuracy and practicality, and record the comparison results to ensure the reliability of mineral identification in practical applications.

[0044] A hyperspectral satellite target rock and mineral identification system based on spectral characteristic parameters, the system is used to execute any of the above methods, the system comprising:

[0045] Standard rock and mineral reflectance spectrum curve preprocessing module: used to obtain standard reflectance spectrum data of target altered minerals, and sequentially perform SG filtering smoothing, envelope removal, resampling according to the actual band of hyperspectral imagery, and statistical averaging processing to build a mineral average reflectance spectrum library with high consistency and comparability;

[0046] Hyperspectral satellite image preprocessing module: Connected to the standard rock and mineral reflectance spectrum curve preprocessing module, it is used to perform radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal on the collected multi-source hyperspectral satellite images, eliminating systematic errors and random noise, and outputting high-quality image data that truly reflects the surface reflectance;

[0047] Target rock and mineral diagnostic spectral parameter identification module: This module is connected to the standard rock and mineral reflectance spectrum curve preprocessing module to extract the target mineral's absorption valley position, reflection peak position, absorption band width, and symmetry diagnostic spectral characteristic parameters in the visible to short-wave infrared band based on the standard spectrum curve. It also constructs a mineral-band correspondence table and outputs a diagnostic band interval index to provide a basis for hyperspectral image band screening.

[0048] Hyperspectral image target rock and mineral diagnostic spectrum screening module: This module is connected to the target rock and mineral diagnostic spectrum parameter identification module to map the diagnostic band interval to the actual band structure of the hyperspectral image, construct a mineral-image band mapping table, screen out a band subset with diagnostic capabilities, eliminate redundant and invalid bands, and verify band consistency, providing efficient and accurate image input data for subsequent spectral parameter extraction and identification processing;

[0049] Hyperspectral image target rock and mineral spectral characteristic parameter calculation module: connected to the hyperspectral image target rock and mineral diagnostic spectrum screening module, the screened diagnostic band subset is preprocessed by SG filtering and continuum removal, and the spectral characteristic parameters of each band are calculated, including absorption center wavelength, absorption depth, absorption width, and symmetry parameters, and multiple spectral characteristic layers are output in the form of layers;

[0050] Target rock and mineral identification module based on multi-dimensional spectral characteristics: connected to the hyperspectral image target rock and mineral spectral characteristic parameter calculation module, adopts a multi-dimensional parameter cross-recognition strategy, comprehensively evaluates the absorption position, depth, width, and symmetry spectral characteristics, and matches and screens with standard thresholds to achieve fine identification of target rocks and minerals; at the same time, combined with ground measured data or existing geological data for verification and analysis, outputs target rock and mineral distribution maps, spectral characteristic atlases and accuracy verification reports, and realizes visualization and scientific evaluation of the results.

[0051] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the above-mentioned hyperspectral satellite target rock and mineral identification methods based on spectral characteristic parameters is implemented.

[0052] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements any one of the above-mentioned hyperspectral satellite target rock and mineral identification methods based on spectral characteristic parameters.

[0053] Compared with the prior art, the advantages of the present invention are:

[0054] Through preprocessing steps such as hyperspectral image streak restoration, bad line removal, radiation correction, and geometric correction, image noise and errors are effectively suppressed, improving the reliability and accuracy of the data and providing a high-quality data foundation for subsequent analysis. Consistent processing and calibration of standard rock and mineral spectral reflectance curves and the diagnostic spectral range of hyperspectral images ensure the consistency of spectral information calculated for rock and mineral identification, solving the problem of existing hyperspectral rock and mineral identification technologies lacking analysis of typical rock and mineral emission mechanisms.

[0055] By constructing high-precision, standardized mineral spectral curves and combining them with processing techniques such as continuum removal and smoothing filtering, the discernibility of the target altered mineral spectral features is effectively improved, significantly enhancing the stability and representativeness of diagnostic feature extraction. This method extracts diagnostic bands closely related to the mineral spectral response and eliminates redundant information irrelevant to the target. While maintaining accurate mineral identification, it significantly reduces the data dimensionality of subsequent processing and improves data processing efficiency.

[0056] By introducing a variety of spectral diagnostic parameters such as absorption depth, absorption width, absorption position, and symmetry, the spectral response characteristics of the target mineral are fully characterized, providing a quantitative basis for pixel-level fine identification, overcoming the subjectivity and ambiguity of traditional similarity-based comparison methods. Further, through comparative analysis with actual objects, its reliability and accuracy in practical applications are ensured. Overall, the method of the present invention is not only suitable for the identification of typical altered minerals (such as sericite, chlorite, kaolinite, hematite, etc.), but can also be extended to other geological prospecting targets and remote sensing identification tasks. It has strong versatility and scalability, and is suitable for promotion and application in a variety of hyperspectral remote sensing platforms and multi-regional geological surveys. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of a method for identifying target rocks and minerals using hyperspectral images using spectral characteristic parameters according to an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of a hyperspectral image target rock and mineral identification system using spectral characteristic parameters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The specific implementation of the present invention is described below in conjunction with examples:

[0060] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented. Any structural modification, change in proportional relationship or adjustment of size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0061] At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of description and are not used to limit the scope of implementation of the present invention. Changes or adjustments to their relative relationships should be regarded as the scope of implementation of the present invention without substantially changing the technical content.

[0062] Example 1:

[0063] like Figure 1 As shown in FIG, a method for identifying target rocks and minerals using hyperspectral images based on spectral characteristic parameters includes the following steps:

[0064] S1: Collect and pre-process standard rock and mineral reflectance spectral curves, including the measured spectra of the target rock and mineral to be identified, and the spectral curves of the corresponding minerals in typical surface spectral libraries such as USGS and JPL, to ensure the representativeness and breadth of the target mineral spectral data sources;

[0065] S2: Collect regional hyperspectral satellite images and perform image preprocessing, including radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal, to suppress noise and errors in hyperspectral data;

[0066] S3: Identify the diagnostic spectral reflectance and absorption characteristics of the target rock or mineral based on the standard curve, which is used to identify the diagnostic spectral characteristics of typical rocks and minerals and extract their main spectral ranges, thereby achieving diagnostic spectral interval identification and data compression of the target rock or mineral;

[0067] S4: Screening and extraction of diagnostic spectral ranges of hyperspectral satellite images. Based on the diagnostic spectral features of the target minerals identified in S3 and their corresponding spectral intervals, the spectral bands in the hyperspectral satellite images are screened and retained accordingly to improve efficiency and reduce errors.

[0068] S5: Calculation of diagnostic spectral characteristic parameters of target rocks and minerals in hyperspectral images. Based on the absorption valleys and reflection peaks of target minerals in the diagnostic spectral range, the absorption position, absorption depth, absorption width, absorption valley, symmetry and other spectral characteristic parameters are calculated pixel by pixel based on the hyperspectral satellite image obtained after S4 processing, and the calculation results are output;

[0069] S6: Based on S5, the various spectral characteristic parameters calculated are processed. Based on the multi-dimensional diagnostic spectral characteristics and the information obtained in S3, the spectral characteristic parameters that meet the standard mineral curve are compared and analyzed to achieve target rock and mineral identification and conduct ground verification to ensure actual application effect.

[0070] S1 specifically includes:

[0071] S1: Collect and pre-process standard rock and mineral reflectance spectral curves, including the measured spectra of the target rock and mineral to be identified, and the spectral curves of the corresponding minerals in typical surface spectral libraries such as USGS and JPL, to ensure the representativeness and breadth of the target mineral spectral data sources;

[0072] S2: Collect regional hyperspectral satellite images and perform image preprocessing, including radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal, to suppress noise and errors in hyperspectral data;

[0073] S3: Identify the diagnostic spectral reflectance and absorption characteristics of the target rock or mineral based on the standard curve, which is used to identify the diagnostic spectral characteristics of typical rocks and minerals and extract their main spectral ranges, thereby achieving diagnostic spectral interval identification and data compression of the target rock or mineral;

[0074] S4: Screening and extraction of diagnostic spectral ranges of hyperspectral satellite images. Based on the diagnostic spectral features of the target minerals identified in S3 and their corresponding spectral intervals, the spectral bands in the hyperspectral satellite images are screened and retained accordingly to improve efficiency and reduce errors.

[0075] S5: Calculation of diagnostic spectral characteristic parameters of target rocks and minerals in hyperspectral images. Based on the absorption valleys and reflection peaks of target minerals in the diagnostic spectral range, the absorption position, absorption depth, absorption width, absorption valley, symmetry and other spectral characteristic parameters are calculated pixel by pixel based on the hyperspectral satellite image obtained after S4 processing, and the calculation results are output;

[0076] S6: Based on S5, the various spectral characteristic parameters calculated are processed. Based on the multi-dimensional diagnostic spectral characteristics and the information obtained in S3, the spectral characteristic parameters that meet the standard mineral curve are compared and analyzed to achieve target rock and mineral identification and conduct ground verification to ensure actual application effect.

[0077] S2 specifically includes:

[0078] S21: Remove the strong water vapor absorption bands, overlapping bands, and bands with too low signal-to-noise ratio and poor image quality from the acquired hyperspectral satellite images, and integrate the remaining bands for subsequent preprocessing;

[0079] S22: Repair bad lines and streak noise in the image after removing low-quality bands. Use the average value of pixels in the adjacent columns of the bad lines to replace the repaired bad lines, and use the global streak removal method to eliminate streak noise.

[0080] S23: Perform radiometric calibration and atmospheric correction on the hyperspectral image after the above processing, and use the FLAASH atmospheric correction model to perform atmospheric correction on it to reduce the radiometric error;

[0081] S24: Perform geometric correction on the noise-removed and atmospheric-corrected images, including orthorectification and image registration, to eliminate geographic location errors;

[0082] S25: Perform mask processing on the interfering ground objects after the above preprocessing, including but not limited to vegetation, snow, artificial buildings, etc. Finally, the real reflectance hyperspectral image of the ground objects after radiometric-geometric correction is obtained;

[0083] S3 specifically includes:

[0084] S31: Based on the average reflectance spectrum curve of each mineral constructed in S1, analyze its key spectral characteristics such as absorption valley position, reflection peak position, absorption depth, etc. in the visible-shortwave infrared band;

[0085] S32: Identify the diagnostic spectral characteristic parameters of the target altered mineral, including the central wavelength of the typical absorption band, the width of the absorption band, the absorption symmetry and the relative reflectivity change characteristics, which are used to characterize the uniqueness and distinguishability of the target mineral;

[0086] S33: Based on the wavelength range where the diagnostic spectral features appear, the main response spectrum interval of the target mineral is extracted, and the effective wavelength range with identification value in the hyperspectral image is defined;

[0087] S34: Based on the extracted diagnostic spectral interval, the entire hyperspectral satellite image is spectrally compressed to retain only the band data related to the characteristic response of the target mineral, thereby reducing data redundancy and improving the efficiency of subsequent analysis;

[0088] S35: Establish a diagnostic spectral interval index table for the target mineral, record its key absorption bands and corresponding wavelength information, and provide prior support for mineral identification in hyperspectral images.

[0089] S31 specifically includes:

[0090] S311: Determine the average reflectance spectrum curve of the target mineral, and record the curve as R(λ), where λ represents the wavelength and R represents the reflectance at the corresponding wavelength, which is used as the basic data source for subsequent spectral feature parameter extraction;

[0091] S312: Calculate the absorption center position (i.e., the minimum reflectivity wavelength). By performing a local minimum analysis on the spectrum curve, the calculation formula for the absorption center wavelength λc is: Where [λ1,λ2] is the predefined absorption range;

[0092] S313: Calculate the absorption depth D, which is defined as the ratio of the reflectivity at the absorption center wavelength to the envelope value. The calculation formula is: Among them, Wei R cont (λ c ) is the envelope reflectivity value at the absorption center wavelength;

[0093] S314: Calculate the absorption band width W, that is, the wavelength range width corresponding to the reflectivity being lower than a certain relative threshold (such as the 90% envelope), using the following formula: W = λ r -λ l , where λ l and λ r , respectively, the left and right absorption edge wavelengths, satisfying R(λ l )=R(λ r )=0.9×R cont (λ).

[0094] S315: Calculate the absorption symmetry S, which is used to characterize the degree of symmetry of the absorption morphology. The calculation formula is: When S ≈ 0.5, it indicates that the absorption characteristics are basically symmetrical, and deviations from 0.5 indicate increased asymmetry;

[0095] S316: Extract the reflection peak position λmax, that is, the local maximum wavelength in the adjacent region of the absorption band. The calculation formula is: Where λ∈[λ0,λ1], λ represents the wavelength; R(λ) represents the reflectivity corresponding to the wavelength; λ0,λ1 represent the left and right boundaries of a spectral interval (for example, a region on the left or right side of the absorption valley).

[0096] S4 specifically includes:

[0097] S41: Based on the diagnostic spectral characteristic parameters of the target altered mineral extracted in S3, including absorption center, absorption width, characteristic reflection peak and other information, the key response band interval corresponding to each mineral is determined, and a mineral-band mapping table is constructed;

[0098] S42: Compare the constructed key band interval mapping table with the actual band information of the hyperspectral satellite image to identify the effective band set corresponding to the diagnostic features in the hyperspectral image, which is recorded as ∈;

[0099] S43: extract the band data corresponding to ∈ in the entire hyperspectral image to form a diagnostic band image subset, remove the bands that are irrelevant to the spectral response of the target mineral or have redundant information, and reduce the data dimension;

[0100] S44: Perform consistency check on the extracted band subset to ensure that the selected bands completely cover the key spectral characteristic areas of the minerals and are consistent with the standard spectral bands after resampling in S1;

[0101] S45: The screened diagnostic band subset is used as input data for subsequent spectral characteristic parameter calculation (S5) and mineral identification comparison (S6) to reduce the overall processing calculation amount and improve the spectral accuracy and calculation efficiency of mineral identification.

[0102] S5 specifically includes:

[0103] S51: Based on the diagnostic spectrum range of the target mineral identified in S4, all bands belonging to the diagnostic spectrum are screened from the hyperspectral satellite image;

[0104] S52: After the diagnostic spectral bands are screened, the hyperspectral image is subjected to SG filtering and continuum removal operations to further highlight the spectral absorption / reflection characteristics of the target minerals;

[0105] S53: Based on the calculation formula of the target mineral spectrum characteristic parameters given in S3, the spectrum characteristic parameters of the target mineral are calculated pixel by pixel in the hyperspectral image to generate corresponding spectrum characteristic parameter calculation results;

[0106] S6 specifically includes:

[0107] S61: combining the target mineral standard spectrum characteristic parameters identified by S3, performing step-by-step analysis on the target mineral spectrum characteristic parameter results calculated by S5;

[0108] S62: First, based on the absorption position of the target mineral standard spectrum obtained in S3, the absorption position results calculated in S5 are screened, and pixels meeting the requirements in the calculation results are extracted;

[0109] S63: further screening the absorption width results calculated in S62 based on the target mineral standard spectrum absorption width obtained in S3, and extracting pixels that meet the requirements in the calculation results;

[0110] S64: further screening the absorption depth result calculated in S63 based on the absorption depth of the target mineral standard spectrum obtained in S3, and extracting pixels that meet the requirements in the calculation result;

[0111] S65: further screening the absorption symmetry results calculated in S64 based on the absorption symmetry of the target mineral standard spectrum obtained in S3, and extracting pixels that meet the requirements in the calculation results;

[0112] S66: Based on the above-extracted pixel results that meet different spectral characteristic parameters of the target mineral, a comprehensive analysis is performed on the pixels that meet multiple spectral characteristic parameters of the target mineral, and the pixels are considered to be the target mineral to be identified;

[0113] S67: Conduct ground verification of the identified target minerals. Select ground targets with known characteristics and accurate geographic information. Perform actual comparisons of the identification results to verify their accuracy and practicality. Record the comparison results to ensure the reliability of mineral identification in practical applications.

[0114] Example 2:

[0115] like Figure 2 As shown, this embodiment proposes a hyperspectral image target rock and mineral identification system based on spectral characteristic parameters, which is used to implement the above-mentioned hyperspectral image target rock and mineral identification method based on spectral characteristic parameters, and includes the following modules:

[0116] Standard rock mineral reflectance spectrum curve preprocessing module: used to obtain standard reflectance spectrum data of target altered minerals, including chlorite and kaolinite, and perform SG filtering smoothing, envelope removal, satellite band resampling and statistical averaging in sequence to build a highly consistent and comparable mineral average reflectance spectrum library.

[0117] Hyperspectral satellite image preprocessing module: connected to the standard rock mineral reflectance spectrum curve preprocessing module, used to perform radiation correction and geometric correction on the calibrated multi-source data to eliminate systematic errors and random noise;

[0118] Target rock and mineral diagnostic spectral parameter identification module: This module is connected to the standard rock and mineral reflectance spectrum curve preprocessing module to extract diagnostic spectral features such as absorption valleys and reflection peaks of target minerals in the visible-shortwave infrared range, identify key parameters such as the central wavelength, width, and symmetry of their absorption bands, determine the main response bands, and construct a diagnostic spectral interval index to guide the band screening of hyperspectral images.

[0119] Hyperspectral image target rock and mineral diagnostic spectral screening module: connected to the target rock and mineral diagnostic spectral parameter identification module, used to construct a mineral-band mapping table based on the target rock and mineral diagnostic spectral parameters of the previous module, and match the corresponding valid bands in the hyperspectral image, extract the diagnostic band subset, eliminate redundant bands and verify the band consistency, providing efficient and accurate data input for subsequent spectral parameter calculation and mineral identification.

[0120] Hyperspectral image target rock and mineral spectral characteristic parameter calculation module: connected to the hyperspectral image target rock and mineral diagnostic spectrum screening module, calculates the spectral characteristic parameters of the hyperspectral image after the target mineral diagnostic spectrum range is screened and outputs the calculation result layer;

[0121] Target rock and mineral identification module based on multi-dimensional spectral characteristics: connected to the hyperspectral image target rock and mineral spectral characteristic parameter calculation module, it conducts multi-dimensional evaluation of the calculation results of the target mineral spectral characteristic parameters, and gradually refines the identification of target minerals based on absorption position, absorption width, absorption depth, absorption symmetry, etc., and conducts ground verification to ensure the actual application effect.

[0122] Example 3:

[0123] This embodiment provides a terminal device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a hyperspectral satellite target rock and mineral identification method based on spectral feature parameters, including the following steps:

[0124] S1: Obtain the standardized spectral curve of each mineral, calculate the diagnostic spectral characteristics of the absorption valley position, depth, width, symmetry and reflection peak position, identify the corresponding diagnostic band interval, construct a mineral-spectral parameter comparison table, and output the spectral characteristic parameters and diagnostic band interval of each mineral;

[0125] S2: Map the diagnostic band intervals extracted in S1 to the corresponding actual bands in the purified hyperspectral surface reflectance image, construct a mineral-band mapping table, extract the valid band subset, and perform consistency verification to ensure complete coverage of the mineral key spectral region, and finally output the diagnostic band subset image;

[0126] S3: Perform SG filtering and smoothing on the diagnostic band subset image output by S2, and perform continuum removal. Calculate the absorption center, depth, width, symmetry, and reflection peak characteristic parameters pixel by pixel according to the spectral parameter formula in S1, and output them as multiple spectral characteristic layers respectively;

[0127] S4: Based on the standard spectral characteristic parameters extracted by S1, the pixels in the S3 output layer are gradually screened, and the mineral target pixels that meet all the diagnostic conditions are identified using the multi-parameter superposition method, and the target mineral distribution map is output; finally, the accuracy is verified in combination with ground surveys or existing data, and the identification result map, spectral parameter atlas and verification report are output.

[0128] Example 4:

[0129] This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0130] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the above-mentioned embodiment related to a method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters. The processor may load and execute the following steps:

[0131] S1: Obtain the standardized spectral curve of each mineral, calculate the diagnostic spectral characteristics of the absorption valley position, depth, width, symmetry and reflection peak position, identify the corresponding diagnostic band interval, construct a mineral-spectral parameter comparison table, and output the spectral characteristic parameters and diagnostic band interval of each mineral;

[0132] S2: Map the diagnostic band intervals extracted in S1 to the corresponding actual bands in the purified hyperspectral surface reflectance image, construct a mineral-band mapping table, extract the valid band subset, and perform consistency verification to ensure complete coverage of the mineral key spectral region, and finally output the diagnostic band subset image;

[0133] S3: Perform SG filtering and smoothing on the diagnostic band subset image output by S2, and perform continuum removal. Calculate the absorption center, depth, width, symmetry, and reflection peak characteristic parameters pixel by pixel according to the spectral parameter formula in S1, and output them as multiple spectral characteristic layers respectively;

[0134] S4: Based on the standard spectral characteristic parameters extracted by S1, the pixels in the S3 output layer are gradually screened, and the mineral target pixels that meet all the diagnostic conditions are identified using the multi-parameter superposition method, and the target mineral distribution map is output; finally, the accuracy is verified in combination with ground surveys or existing data, and the identification result map, spectral parameter atlas and verification report are output.

[0135] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0139] The preferred embodiments of the present invention are described in detail above, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

[0140] Many other changes and modifications can be made without departing from the spirit and scope of the present invention. It should be understood that the present invention is not limited to the specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A hyperspectral satellite target rock and mineral identification method based on spectral characteristic parameters, characterized in that: The method comprises: S1: Obtain the standardized spectral curve of each mineral, calculate the diagnostic spectral characteristics of the absorption valley position, depth, width, symmetry and reflection peak position, identify the corresponding diagnostic band interval, construct a mineral-spectral parameter comparison table, and output the spectral characteristic parameters and diagnostic band interval of each mineral; S2: Map the diagnostic band intervals extracted in S1 to the corresponding actual bands in the preprocessed hyperspectral surface reflectance image, construct a mineral-band mapping table, extract the valid band subset, and perform consistency verification to ensure complete coverage of the mineral key spectral region, and finally output the diagnostic band subset image; S3: Perform SG filtering and smoothing on the diagnostic band subset image output by S2, and perform continuum removal. Calculate the absorption center, depth, width, symmetry, and reflection peak characteristic parameters pixel by pixel according to the spectral parameter formula in S1, and output them as multiple spectral characteristic layers respectively; S4: Based on the standard spectral characteristic parameters extracted by S1, the pixels in the S3 output layer are gradually screened, and the mineral target pixels that meet all the diagnostic conditions are identified using the multi-parameter superposition method, and the target mineral distribution map is output; finally, the accuracy is verified in combination with ground surveys or existing data, and the identification result map, spectral parameter atlas and verification report are output.

2. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 1, characterized in that: Collect and preprocess the standard rock and mineral reflectance spectral curves, including the measured spectra of the target rock and mineral and the spectral curves of the typical surface spectral library, and perform filtering, smoothing, and envelope removal processing to obtain the standardized spectral curves of each mineral; perform image preprocessing on the hyperspectral satellite image, including radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal, to obtain the preprocessed hyperspectral surface reflectance image.

3. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 1, characterized in that: The step S1 includes: S101: Based on the constructed average reflectance spectrum curve of each mineral, analyze its key spectral characteristics in the visible-shortwave infrared band, including the central wavelength, bandwidth, symmetry, absorption depth, reflection peak position and relative reflectance variation characteristics of the typical absorption band, and identify the diagnostic spectral parameters of the target altered mineral to characterize its uniqueness and distinguishability; S102: Extract the main response spectrum interval of the target mineral based on the wavelength range where the diagnostic spectral features appear, and define the effective wavelength range with identification value in the hyperspectral image; S103: Based on the extracted diagnostic spectral interval, the entire hyperspectral satellite image is subjected to spectral compression processing, and only the band data related to the characteristic response of the target mineral is retained to reduce data redundancy and improve the efficiency of subsequent analysis; S104: Establish a diagnostic spectral interval index table for the target mineral, record its key absorption bands and corresponding wavelength information, and provide prior support for mineral identification in hyperspectral images.

4. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 3, wherein: The step S101 includes: S1010: Preprocess the average reflectance spectrum curve of the target mineral by using Savitzky-Golay (SG) filtering to smooth and denoise, and combine the spectral envelope removal method to enhance the absorption valley characteristics. Then resample to the corresponding band range of the hyperspectral satellite to provide a standardized basic curve for spectral feature parameter extraction; S1011: Determine the average reflectance spectrum curve of the target mineral, and record the curve as R(λ), where λ represents the wavelength and R represents the reflectance at the corresponding wavelength, which is used as the basic data source for subsequent spectral feature parameter extraction; S1012: Calculate the absorption center position, i.e., the minimum reflectivity wavelength. By performing a local minimum analysis on the spectrum curve, the calculation formula for the absorption center wavelength λc is: Where [λ1,λ2] is the predefined absorption range; S1013: Calculate the absorption depth D, which is defined as the ratio of the reflectivity at the absorption center wavelength to the envelope value. The calculation formula is: Among them, Wei R cont (λ c ) is the envelope reflectivity value at the absorption center wavelength; S1014: Calculate the absorption band width W, that is, the wavelength range width corresponding to the reflectivity being lower than a certain relative threshold. The calculation formula is: W = λ r -λ l , where λ l and λ r , respectively, the left and right absorption edge wavelengths, satisfying R(λ l )=R(λ r )=0.9×R cont (λ); S1015: Calculate the absorption symmetry S, which is used to characterize the degree of symmetry of the absorption morphology. The calculation formula is: When S ≈ 0.5, it indicates that the absorption characteristics are basically symmetrical, and deviations from 0.5 indicate increased asymmetry; S1016: Extract the reflection peak position λmax, that is, the local maximum wavelength in the adjacent region of the absorption band. The calculation formula is: Where λ∈[λ0,λ1], λ represents the wavelength; R(λ) represents the reflectivity corresponding to the wavelength; λ0,λ1 represent the left and right boundaries of a spectral interval.

5. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 1, wherein: The step S2 includes: S201: Based on the extracted diagnostic spectral characteristic parameters of the target altered mineral, including absorption center, absorption width, and characteristic reflection peak information, determine the key response band interval corresponding to each mineral and construct a mineral-band mapping table; S202: Compare the constructed key band interval mapping table with the actual band information of the hyperspectral satellite image to identify the valid band set corresponding to the diagnostic features in the hyperspectral image, denoted as ∈; S203: extracting the band data corresponding to ∈ in the entire hyperspectral image to form a diagnostic band image subset, eliminating bands that are irrelevant to the spectral response of the target mineral or have redundant information, and reducing the data dimension; S204: Perform consistency check on the extracted band subset to ensure that the selected band completely covers the key spectral characteristic area of ​​the mineral and is consistent with the standard spectral band after resampling; S205: Using the screened diagnostic band subset as input data for subsequent spectral characteristic parameter calculation and mineral identification comparison, so as to reduce the overall processing calculation amount and improve the spectral accuracy and calculation efficiency of mineral identification.

6. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 1, characterized in that: The step S3 includes: S301: Based on the diagnostic spectrum range of the target mineral identified in S2, all bands belonging to the diagnostic spectrum are screened from the hyperspectral satellite image; S302: performing SG filtering and continuum removal operations on the hyperspectral image after screening out the diagnostic spectral bands to further highlight the spectral absorption / reflection characteristics of the target mineral; S303: Based on the target mineral spectral characteristic parameter calculation formula given in S1, the spectral characteristic parameters of the target mineral are calculated pixel by pixel in the hyperspectral image to generate corresponding spectral characteristic parameter calculation results.

7. The method for identifying target rocks and minerals using a hyperspectral satellite based on spectral characteristic parameters according to claim 1, wherein: The step S4 comprises: S401: combining the target mineral standard spectral characteristic parameters identified in S1, performing step-by-step analysis on the calculated target mineral spectral characteristic parameter results; S402: First, based on the absorption position of the target mineral standard spectrum obtained in S1, the calculated absorption position results are screened to extract pixels that meet the requirements in the calculated results; S403: For each pixel in the preliminary candidate area, further calculate its absorption band width, absorption depth and symmetry and other characteristic parameters, compare them with the target mineral standard value in S1, use the set threshold range to make judgments respectively, and construct a multi-parameter discrimination matrix; S404: Based on the multi-parameter discriminant matrix, weighted scoring or logical combination is performed on the conformity of each pixel, and pixels that simultaneously meet multiple key parameter characteristics are extracted as the final target mineral identification result; S405: Visualize the spatial distribution of the identified target mineral area and interpret and confirm it in combination with geological background data; S406: Based on the above extracted pixel results that meet the different spectral characteristic parameters of the target mineral, a comprehensive analysis is performed on the pixels that meet multiple spectral characteristic parameters of the target mineral, and the pixels are considered to be the target mineral to be identified; S407: Conduct ground verification of the identified different target minerals, select ground targets with known characteristics and accurate geographic information, conduct actual comparison of the identification results to verify their accuracy and practicality, and record the comparison results to ensure the reliability of mineral identification in practical applications.

8. A hyperspectral satellite target rock and mineral identification system based on spectral characteristic parameters, characterized in that: The system is used to perform the method according to any one of claims 1 to 7, and the system includes: Standard rock and mineral reflectance spectrum curve preprocessing module: used to obtain standard reflectance spectrum data of target altered minerals, and sequentially perform SG filtering smoothing, envelope removal, resampling according to the actual band of hyperspectral imagery, and statistical averaging processing to build a mineral average reflectance spectrum library with high consistency and comparability; Hyperspectral satellite image preprocessing module: Connected to the standard rock and mineral reflectance spectrum curve preprocessing module, it is used to perform radiation correction, atmospheric correction, geometric correction, and stripe / bad line removal on the collected multi-source hyperspectral satellite images, eliminating systematic errors and random noise, and outputting high-quality purified image data that truly reflects the surface reflectance; Target rock and mineral diagnostic spectral parameter identification module: This module is connected to the standard rock and mineral reflectance spectrum curve preprocessing module to extract the target mineral's absorption valley position, reflection peak position, absorption band width, and symmetry diagnostic spectral characteristic parameters in the visible to short-wave infrared band based on the standard spectrum curve. It also constructs a mineral-band correspondence table and outputs a diagnostic band interval index to provide a basis for hyperspectral image band screening. Hyperspectral image target rock and mineral diagnostic spectrum screening module: This module is connected to the target rock and mineral diagnostic spectrum parameter identification module to map the diagnostic band interval to the actual band structure of the hyperspectral image, construct a mineral-image band mapping table, screen out a band subset with diagnostic capabilities, eliminate redundant and invalid bands, and verify band consistency, providing efficient and accurate image input data for subsequent spectral parameter extraction and identification processing; Hyperspectral image target rock and mineral spectral characteristic parameter calculation module: connected to the hyperspectral image target rock and mineral diagnostic spectrum screening module, the screened diagnostic band subset is preprocessed by SG filtering and continuum removal, and the spectral characteristic parameters of each band are calculated, including absorption center wavelength, absorption depth, absorption width, and symmetry parameters, and multiple spectral characteristic layers are output in the form of layers; Target rock and mineral identification module based on multi-dimensional spectral characteristics: connected to the hyperspectral image target rock and mineral spectral characteristic parameter calculation module, adopts a multi-dimensional parameter cross-recognition strategy, comprehensively evaluates the absorption position, depth, width, and symmetry spectral characteristics, and matches and screens with standard thresholds to achieve fine identification of target rocks and minerals; at the same time, combined with ground measured data or existing geological data for verification and analysis, outputs target rock and mineral distribution maps, spectral characteristic atlases and accuracy verification reports, and realizes visualization and scientific evaluation of the results.

9. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a hyperspectral satellite target rock and mineral identification method based on spectral characteristic parameters as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a hyperspectral satellite target rock and mineral identification method based on spectral characteristic parameters according to any one of claims 1 to 7.

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