CN109580497B - Hyperspectral mineral abnormal information extraction method based on singularity theory - Google Patents
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Abstract
The invention discloses a method for extracting hyperspectral mineral abnormal information based on a singularity theory, and relates to the technical field of hyperspectral remote sensing mineral identification. Firstly, obtaining a characteristic absorption position of a target mineral on a spectral image, then comparing the characteristic absorption position on the image with a characteristic absorption position on a standard spectrum, and if the characteristic absorption position on the image is consistent with the characteristic absorption position on the standard spectrum and the matching degree between hyperspectral image data and the standard spectrum data reaches a set threshold value, determining that a pixel of the spectral image contains the target mineral, thereby completing mineral information extraction; and finally, calculating the singularity index on the whole spectral image pixel by pixel according to the extracted mineral information to obtain a mineral abnormal information distribution map. Therefore, the method provided by the invention enhances the extraction of the mineral weak information, and compared with a large amount of background mineral information, the mineral abnormal information with the mutation characteristic is more closely related to mineralization, so that the method is more beneficial to extracting the mineral information.
Description
Technical Field
The invention relates to the technical field of hyperspectral remote sensing mineral identification, in particular to a method for extracting hyperspectral mineral abnormal information based on a singularity theory.
Background
With the development of remote sensing technology, the quantitative remote sensing technology represented by aviation hyperspectrum is widely applied to various fields and becomes an important means for earth observation. Unlike multispectral, hyperspectral has abundant spectral information that can be used to identify mineral information.
Currently, the main methods for hyperspectral mineral identification can be summarized into two categories: an extraction method based on spectrum similarity matching and an extraction method based on spectrum characteristic parameters. The spectral similarity matching method is difficult to distinguish minerals with similar spectral characteristics, a very detailed extraction rule needs to be formulated based on the spectral characteristic parameter method, and the implementation process is complex. Both the two methods are indispensable to participate in expert knowledge and analyze standard mineral spectra, so that the mineral information is difficult to be extracted finely in the application process by only using one method.
From a geological application perspective, the purpose of mineral information extraction is to provide anomaly information and clues related to mineralization. However, mineral information associated with mineralization tends to be a minority, and most mineral information reflects background mineral information over an area. How to react abnormal information of minerals on a spatial scale through spectral analysis is a difficulty in the current hyperspectral geological application.
Disclosure of Invention
The invention aims to provide a method for extracting hyperspectral mineral abnormal information based on a singularity theory, so as to solve the problems in the prior art.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a hyperspectral mineral abnormal information extraction method based on singularity theory comprises the following steps:
s1, acquiring hyperspectral image data and standard spectrum data, and respectively intercepting characteristic interval reflectivity data of a hyperspectral image and a standard spectrum according to a mineral spectrum characteristic range to obtain hyperspectral image reflectivity data and standard spectrum reflectivity data;
s2, performing continuum removal on the reflectivity data obtained in the S1 to obtain spectral data after the continuum is removed;
s3, performing Gaussian fitting on the image spectrum data obtained in the S2 and subjected to continuum removal to obtain a fitting curve;
s4, obtaining the characteristic absorption position of the image spectrum according to the lowest point of the fitting curve;
s5, calculating the fitting degree of the image spectrum data after Gaussian fitting in S3 and the standard spectrum data after continuous unification removal in S2;
s6, if the characteristic absorption position of the image spectrum obtained in S4 is consistent with the characteristic absorption position of the standard spectrum, and the fitting degree obtained by calculation in S5 is larger than a set threshold value, determining that the pixel contains the target mineral;
and S7, calculating the singularity index pixel by pixel on the whole spectral image containing the target mineral information obtained in the S6 to obtain a mineral abnormal information distribution map.
Preferably, the formula employed in S2 is:
Rcr＝R/Rc
wherein Rcr is the spectrum data after removing the continuum, R is the original spectrum data, and Rc is the continuum of the original spectrum.
Preferably, in S5, the matching degree is calculated according to the following formula:
wherein R is2Representing the degree of fitting, and the value range is [0,1 ]]R is2Closer to 1 indicates higher fitness, yiIndicating standard spectral data after removing continuum,
is yiIs determined by the average value of (a) of (b),
the image spectrum data after Gaussian fitting.
Preferably, the step of S4 further includes the step of calculating the absorption depth of the characteristic absorption position by using the following formula:
Depthmin＝1-Rcrmin
wherein, DepthminIndicating the depth of absorption, RcrminThe value of the lowest point of the fitted curve is shown.
Preferably, the method in S6 further includes a step of determining that the image includes the target mineral if the characteristic absorption position of the image spectrum obtained in S4 matches the characteristic absorption position of the standard spectrum, and the calculated fitting degree in S5 is greater than a set threshold, and the absorption depth corresponding to the absorption position represents the relative abundance of the target mineral.
Preferably, S6 further includes the step of: and performing secondary check on the image spectrum pixels based on expert knowledge, finishing extraction of target mineral information if the image spectrum pixels pass the expert knowledge, and otherwise, continuously adjusting the threshold until the extraction result meets the application requirement.
Preferably, S7 is specifically: is provided withDefining a series of sliding windows, the size L of the windows is increased progressively in turn, Lmin＝L1＜L2…＜Ln＝LmaxSliding a window at a certain point on the whole image of the mineral information, calculating the average mineral density in each window, and calculating the singularity index according to the following formula by fitting the functional relationship between the mineral density and the window size L:
<ρ(A)>＝cA-Δα
wherein c is the fractal density; Δ α is a singularity index; < ρ (a) > is a certain mineral density in a region of size L and area a.
The invention has the beneficial effects that: the method for extracting the abnormal information of the hyperspectral mineral based on the singularity theory comprises the steps of firstly obtaining a characteristic absorption position of a target mineral on a spectral image, then comparing the characteristic absorption position on the image with a characteristic absorption position on a standard spectrum, and if the characteristic absorption position on the image is consistent with the characteristic absorption position on the standard spectrum and the matching degree between hyperspectral image data and the standard spectrum data reaches a set threshold value, determining that a pixel of the spectral image contains the target mineral, and thus finishing mineral information extraction; and finally, calculating the singularity index on the whole spectral image pixel by pixel according to the extracted mineral information to obtain a mineral abnormal information distribution map. Therefore, the method provided by the invention enhances the extraction of the mineral weak information, and compared with a large amount of background mineral information, the mineral abnormal information with the mutation characteristic is more closely related to mineralization, so that the method is more beneficial to extracting the mineral information.
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FIG. 1 is a flow diagram of a hyperspectral mineral abnormal information extraction method based on singularity theory provided by the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, an embodiment of the present invention provides a method for extracting abnormal information of a hyperspectral mineral based on a singularity theory, including the following steps:
s1, acquiring hyperspectral image data and standard spectrum data, and respectively intercepting characteristic interval reflectivity data of a hyperspectral image and a standard spectrum according to a mineral spectrum characteristic range to obtain hyperspectral image reflectivity data and standard spectrum reflectivity data;
s2, performing continuum removal on the reflectivity data obtained in the S1 to obtain spectral data after the continuum is removed;
s3, performing Gaussian fitting on the image spectrum data obtained in the S2 and subjected to continuum removal to obtain a fitting curve;
s4, obtaining the characteristic absorption position of the image spectrum according to the lowest point of the fitting curve;
s5, calculating the fitting degree of the image spectrum data after Gaussian fitting in S3 and the standard spectrum data after continuous unification removal in S2;
s6, if the characteristic absorption position of the image spectrum obtained in S4 is consistent with the characteristic absorption position of the standard spectrum, and the fitting degree obtained by calculation in S5 is larger than a set threshold value, determining that the pixel contains the target mineral;
and S7, calculating the singularity index pixel by pixel on the whole spectral image containing the target mineral information obtained in the S6 to obtain a mineral abnormal information distribution map.
From a geological application perspective, the purpose of mineral information extraction is to provide anomaly information and clues related to mineralization. However, mineral information associated with mineralization tends to be a minority, and most mineral information reflects background mineral information over an area. A large number of scholars demonstrate that the distribution rule of minerals and elements related to mineralization in an mineralization abnormal range is in accordance with power law distribution based on a nonlinear theory and a complexity theory, and the distribution of the minerals and elements in a background area which is not related to the mineralization is in accordance with normal distribution. As an extension of the nonlinear theory, the underlying principle of local singularity is the spatial-temporal distribution law quantitatively characterized as mineral enrichment and aggregation. Based on the thought, the abnormal information of the minerals on the spatial scale is reflected through the regional mineral singularity index, so that the variation of the mineral information at a certain local position relative to the overall background is reflected, the extraction of the weak mineral information is enhanced, and the abnormal mineral information with the mutation characteristic is often more closely related to the mineralization relative to a large amount of background mineral information.
According to the method provided by the invention, firstly, hyperspectral image data are processed to obtain the characteristic absorption position of a target mineral on a spectral image, then, the characteristic absorption position on the image is compared with the characteristic absorption position on a standard spectrum, if the two positions are consistent and the matching degree between the hyperspectral image data and the standard spectrum data reaches a set threshold value, the spectral image pixel is considered as the target mineral, and thus mineral information extraction is completed;
and finally, calculating a singularity index on the whole spectral image pixel by pixel according to the extracted mineral information, wherein the singularity index can reflect abnormal information of the mineral on a spatial scale, so that a mineral abnormal information distribution map can be obtained by calculating the singularity index.
Therefore, the method provided by the invention enhances the extraction of the mineral weak information, and compared with a large amount of background mineral information, the mineral abnormal information with the mutation characteristic is more closely related to mineralization, so that the method is more beneficial to extracting the mineral information.
In S1, the acquired hyperspectral image data may be preprocessed by radiometric calibration, atmospheric correction, and the standard spectral data may be acquired from the USGS or TSG spectral library.
The diagnosis characteristic position of a standard spectrum (mainly referring to USGS and TSG spectral library) is analyzed by using expert knowledge, and on the basis, the mineral spectral characteristic range is determined, wherein the range can be one section or multiple sections, and for common minerals, the spectral characteristic of the mineral can be accurately described basically by three sections of spectral characteristic ranges. The spectral characteristics of several minerals are shown in Table 1, for example, the spectral characteristics of muscovite minerals are three-segment, 2145-2285nm interval, 2285-2405nm interval and 2405-2528nm interval.
TABLE 1 Hyperspectral-based common mineral information extraction rule Table
And intercepting the hyperspectral image data and the standard spectrum data within the mineral spectrum characteristic range to obtain corresponding characteristic interval reflectivity data.
In S2, the continuum removal operation is performed on both the reflectance image and the standard spectrum obtained in S1, so that the influence of the terrain, light, and the like on the spectrum can be eliminated.
Wherein, the continuum is a connecting line between reflection peaks in the reflection spectrum curve. And removing the continuum, namely dividing the spectrum of the continuum by the spectrum of the reflection spectrum, wherein after treatment, each reflection peak becomes 1, and the numerical values among the reflection peaks are all smaller than 1.
In the invention, in S3, gaussian fitting is performed on the image spectrum obtained in S2 after the continuum is removed, so as to obtain an optimized light image spectrum curve.
And the lowest point of the fitted curve is the characteristic absorption position.
And extracting target mineral information according to the fitting degree and the characteristic absorption position of the image spectral data and the standard spectral data, namely judging whether the target mineral is contained.
The characteristic absorption positions of the standard spectrum are the information extraction rules summarized by analyzing the standard spectrum by using expert knowledge, as shown in table 1, the muscovite mineral has absorption characteristics at 2190/2210/2225nm position in 2145-and 2285nm interval, and has absorption characteristics near 2348nm in 2285-and 2405nm interval and near 2440nm in 2405-and 2528nm interval.
And if the absorption position of the image spectrum conforms to the extraction rule, and the pixel with the fitting degree larger than the set threshold value contains the target mineral.
And finally, in S7, a mineral abnormal information distribution map is obtained by calculating the singularity index of each pixel, and the extraction of mineral abnormal information is completed.
In one embodiment of the present invention, the formula used in S2 is:
Rcr＝R/Rc
wherein Rcr is the spectrum data after removing the continuum, R is the original spectrum data, and Rc is the continuum of the original spectrum.
In a preferred embodiment of the present invention, in S5, the fitting degree can be calculated according to the following formula:
wherein R is2Representing the degree of fitting, and the value range is [0,1 ]]R is2Closer to 1 indicates higher fitness, yiIndicating standard spectral data after removing continuum,
is yiIs determined by the average value of (a) of (b),
the image spectrum data after Gaussian fitting.
In the present invention, S4 may further include a step of calculating the absorption depth of the characteristic absorption position by using the following formula:
Depthmin＝1-Rcrmin
wherein, DepthminIndicating the depth of absorption, RcrminThe value of the lowest point of the fitted curve is shown.
The content of the target mineral can be judged according to the absorption depth.
In the invention, if the characteristic absorption position of the image spectrum obtained in S4 is consistent with the characteristic absorption position of the standard spectrum, and the calculated fitting degree in S5 is greater than a set threshold value, the pixel is determined to contain the target mineral, and the absorption depth corresponding to the absorption position represents the relative abundance of the target mineral.
In the present invention, S6 may further include: and performing secondary check on the image spectrum pixels based on expert knowledge, finishing extraction of target mineral information if the image spectrum pixels pass the expert knowledge, and otherwise, continuously adjusting the threshold until the extraction result meets the application requirement.
In the invention, the extraction method for matching the fitting degree by adopting the standard spectrum simplifies the step of extracting the mineral information purely based on the spectral characteristics to a certain extent, and the mineral information extraction rule based on expert knowledge further enhances the accuracy of extracting the mineral information purely by using a spectrum matching mode.
In the embodiment of the present invention, S7 may specifically be: setting a series of sliding windows, wherein the size L of the windows is increased gradually in sequence, and L ismin＝L1＜L2…＜Ln＝LmaxSliding a window at a certain point on the whole image of the mineral information, calculating the average mineral density in each window, and calculating the singularity index according to the following formula by fitting the functional relationship between the mineral density and the window size L:
<ρ(A)>＝cA-Δα
wherein c is the fractal density; Δ α is a singularity index; < ρ (a) > is a certain mineral density in a region of size L and area a.
The method has the advantages that the singularity index is calculated in a sliding window mode, the process is simple and easy to use, the change quantity of mineral information at a certain local position is obtained by analyzing the spatial change rule of minerals under different scales, the extraction of the mineral information can be enhanced, and the abnormal mineral information with 'mutation' characteristics on the area is highlighted.
The specific embodiment is as follows:
in order to better explain the method and steps of the invention, a muscovite mineral abnormal information map filling test is carried out by taking HyMap airborne hyperspectral data in a certain area as an example.
(1) The equipment used for the test is a graphic workstation, the specification model is Dell Precision T7600, the operating system is Windows7(64 bits), the CPU is 2.66GHz, the content is 32GB, and the hard disk is 1T.
(2) The method comprises the following specific steps:
the method comprises the following steps: and acquiring reflectivity interval data.
Firstly, preprocessing Hymap data such as radiometric calibration and atmospheric correction is carried out to obtain reflectivity data, and muscovite standard spectrum data is obtained from a USGS spectrum library. According to the muscovite spectral characteristic range in Table 1, the reflectivity data of the hyperspectral image and the standard spectrum in three sections of 2145 + 2285nm, 2285 + 2405nm and 2405 + 2528nm are intercepted.
Step two: spectral continuum removal.
Removing continuum, namely dividing the spectrum of the continuum by the spectrum of the reflection spectrum, wherein after treatment, each reflection peak becomes 1, the numerical value between the reflection peaks is less than 1, and the specific calculation process is shown as the following formula:
Rcr＝R/Rc
wherein Rcr is the spectrum data after the continuum removing processing, R is the original spectrum data, and Rc is the continuum of the original spectrum. And D, performing continuum removing treatment on the interval reflectivity image and the standard spectrum obtained in the step one according to the formula.
Step three: and (5) extracting mineral information.
Firstly, Gaussian fitting is carried out on the image spectrum obtained in the step two after the continuum is removed, and an optimized image spectrum curve is obtained. Obtaining a characteristic absorption position by searching the lowest point of the fitting curve, and calculating the absorption depth of the position, wherein the formula is as follows:
absorption depth calculation formula:
Depthmin＝1-Rcrmin
Depthminwherein denotes the absorption depth, RcrminThe value of the lowest point of the fitted curve is shown.
Respectively calculating the matching degree of the optimized image spectrum and the interval standard spectrum after the continuum is removed, wherein the calculation formula is as follows:
wherein R is2Representing the degree of fitting, a rangeIs enclosed in [0,1 ]]R is2Closer to 1 indicates higher fitness, yiRepresenting the spectral data after the continuum has been removed,
is yiIs determined by the average value of (a) of (b),
are fit values.
When the fitting degree is larger than the set threshold value, based on the values of the absorption positions of the image spectrum and the standard spectrum (the absorption position of the image spectrum is obtained by the above calculation, and the absorption position of the standard spectrum is obtained by analyzing the standard spectrum, as shown in table 1, for example, the muscovite mineral has absorption characteristics at 2190/2210/2225nm position in the 2145-2285nm interval in the vicinity of 2348nm in the 2285-2405nm interval and at the 2440nm in the 2405-2528nm interval), it can be determined whether the absorption characteristics in the spectral interval match with the characteristics of the target mineral, if the absorption position in the spectral interval is consistent with the absorption position of the standard spectrum of the target mineral, the absorption characteristics in the spectral interval are matched with the characteristics of the target mineral, spectral information meeting the extraction conditions can be extracted, and finally, secondary checking is carried out on the extracted result based on expert knowledge to finish the extraction of mineral information.
Step four: and calculating a singularity index.
Assuming that a certain mineral density in a region of size L and area a can be expressed as < ρ (a) >, according to the singular theory, the area and density have the following relationship:
<ρ(A)>＝cA-Δα
wherein c is the fractal density; Δ α represents a singularity index.
In two-dimensional space, the value of Δ α is 2- α, and the value of Δ α can reflect the degree of mineral density in the current area relative to the surrounding enrichment or depletion.
A series of sliding windows are set, the window size L is increased in sequence, in the embodiment of the invention, the window size is set by taking picture elements as units, namely 2pix, 6pix, 10pix, 14pix, 18pix, 22pix and 26 pix. And sliding a window at a certain point on the image, calculating the average mineral density in each window, and fitting a functional relation between the mineral density and the window size L to obtain the singularity index delta alpha and the fractal density c of the point. And (4) calculating the singularity index of the whole image pixel by pixel on the mineral information image obtained in the third step to obtain a mineral abnormal information distribution map.
The extent of the spatial distribution of regions with Δ α > 0 is limited, with fractal dimensions often less than 2, whereas the spatial distribution of regions with relatively unchanged or depleted mineral density (i.e., Δ α ≦ 0) is broad, with fractal dimensions often close to 2, and regions with no significant enrichment of the mineral (Δ α ≦ 0) are background mineral regions. When Δ α > 0, it indicates that the mineral density increases sharply with decreasing area, and such an enriched region is a mineral information abnormal region. Then a mineral anomaly information distribution map can be obtained according to the singularity index.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained: the method for extracting the abnormal information of the hyperspectral mineral based on the singularity theory comprises the steps of firstly obtaining a characteristic absorption position of a target mineral on a spectral image, then comparing the characteristic absorption position on the image with a characteristic absorption position on a standard spectrum, and if the characteristic absorption position on the image is consistent with the characteristic absorption position on the standard spectrum and the matching degree between hyperspectral image data and the standard spectrum data reaches a set threshold value, determining that a pixel of the spectral image contains the target mineral, and thus finishing mineral information extraction; and finally, calculating the singularity index on the whole spectral image pixel by pixel according to the extracted mineral information to obtain a mineral abnormal information distribution map. Therefore, the method provided by the invention enhances the extraction of the mineral weak information, and compared with a large amount of background mineral information, the mineral abnormal information with the mutation characteristic is more closely related to mineralization, so that the method is more beneficial to extracting the mineral information.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (5)
1. A hyperspectral mineral abnormal information extraction method based on singularity theory is characterized by comprising the following steps:
s1, acquiring hyperspectral image data and standard spectrum data, and respectively intercepting characteristic interval reflectivity data of a hyperspectral image and a standard spectrum according to a mineral spectrum characteristic range to obtain hyperspectral image reflectivity data and standard spectrum reflectivity data;
s2, performing continuum removal on the reflectivity data obtained in the S1 to obtain spectral data after the continuum is removed;
s3, performing Gaussian fitting on the image spectrum data obtained in the S2 and subjected to continuum removal to obtain a fitting curve;
s4, obtaining the characteristic absorption position of the image spectrum according to the lowest point of the fitting curve;
s5, calculating the fitting degree of the image spectrum data after Gaussian fitting in S3 and the standard spectrum data after continuous unification removal in S2;
s6, if the characteristic absorption position of the image spectrum obtained in S4 is consistent with the characteristic absorption position of the standard spectrum, and the fitting degree obtained by calculation in S5 is larger than a set threshold value, determining that the image contains the target mineral;
s7, calculating singularity indexes pixel by pixel on the whole spectral image containing the target mineral information obtained in S6 to obtain a mineral abnormal information distribution map;
the formula adopted in S2 is:
Rcr＝R/Rc
wherein Rcr is the spectrum data after removing the continuum, R is the original spectrum data, and Rc is the continuum of the original spectrum;
s7 specifically includes: setting a series of sliding windows, wherein the size L of the windows is increased gradually in sequence, and L ismin＝L1<L2…<Ln＝LmaxSliding a window at a point over the entire image of the mineral information and calculating the average mineral density within each window by fitting a function between the mineral density and the window size LA numerical relationship, calculating the singularity index according to the following formula:
<ρ(A)>＝cA-Δα
wherein c is the fractal density; Δ α is a singularity index; < ρ (a) > is a certain mineral density in a region of size L and area a.
2. The singularity theory-based hyperspectral mineral anomaly information extraction method according to claim 1, wherein in S5, the fitting degree is calculated according to the following formula:
wherein R is2Representing the degree of fitting, and the value range is [0,1 ]]R is2Closer to 1 indicates higher fitness, yiIndicating standard spectral data after removing continuum,
is yiIs determined by the average value of (a) of (b),
the image spectrum data after Gaussian fitting.
3. The singularity theory-based hyperspectral mineral anomaly information extraction method according to claim 1, wherein S4 further comprises a step of calculating the absorption depth of the feature absorption position by using the following formula:
Depthmin＝1-Rcrmin
wherein, DepthminIndicating the depth of absorption, RcrminThe value of the lowest point of the fitted curve is shown.
4. The singularity theory-based hyperspectral mineral anomaly information extraction method according to claim 3, wherein S6 further comprises a step of determining that the pixel contains the target mineral if the characteristic absorption position of the image spectrum obtained in S4 is consistent with the characteristic absorption position of the standard spectrum and the fitting degree obtained by calculation in S5 is greater than a set threshold, and the absorption depth corresponding to the absorption position represents the relative abundance of the target mineral.
5. The singularity theory-based hyperspectral mineral anomaly information extraction method according to claim 1, wherein S6 further comprises the steps of: and performing secondary check on the image spectrum pixels based on expert knowledge, finishing extraction of target mineral information if the image spectrum pixels pass the expert knowledge, and otherwise, continuously adjusting the threshold until the extraction result meets the application requirement.
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