CN103383348A - Method for extracting altered mineral at vegetation-covered areas by hyperspectral remote sensing - Google Patents
Method for extracting altered mineral at vegetation-covered areas by hyperspectral remote sensing Download PDFInfo
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Abstract
The invention relates to a method for extracting altered mineral at vegetation-covered areas by hyperspectral remote sensing. The method comprises the following steps: a detection gun is directed at leaves of broadleaved plants to directly measure reflectance spectrum of leaves, measures reflectance spectrum of herbaceous plant canopy and measure reflectance spectrum of fresh surface of soil; uncalibrated and steam-influeced waveband removal, absolute radiation value conversion, bad wire restoration and atmospheric correction are carried out on Hyperion data; two principal components which have large absolute values but are opposite in sign are found according to principal component analysis characteristic value and a 2-D point diagram is made according to the two principal components; and abnormity is delineated according to the point diagram. Through the comparison of spectral feature fitting method and mineral extraction model, multilayer information separation of background, interference and abnormal information is adopted, and the key is to select the best waveband for spatial feature analysis. Through analysis of hyperspectral data characteristics and vegetation-covered area geographical features and the best waveband selection used during spatial feature optimization, vegetation information is better inhibited, and alteration information characteristics are enhanced.
Description
Technical field
The present invention relates to a kind of remote sensing image in geologic application, especially utilize target in hyperspectral remotely sensed image to extract the method for altered mineral in the vegetation-covered area.
Background technology
High-spectrum remote-sensing has very continuous wave band, spectral resolution reaches nanoscale, can identify different types of mineral by the small spectrum difference between different minerals, the at present qualitative identification of high-spectrum remote-sensing mineral is by having obtained good checking based on the wave spectrum hourglass model of spectral similarity and wave band decorrelation in rock exposed area.But in the vegetation-covered area, rocks and minerals shows as weak information, little target on remote sensing images, is difficult to identification, and how utilizing remotely-sensed data to extract these weak information is difficult problems of modern remote-sensing geology application technology.
Technical method commonly used is mainly to utilize multiple regression analysis with the method for surface data and the combination of spectral signature parameter to high-spectrum remote-sensing in the vegetation-covered area, extract mineral information, the defective of this method is the random selection of topographical features parameter, there is no universality, need to carry out respectively statistical study in different regions, and because remotely-sensed data is all mixed pixel, utilize the Land Surface Parameters error of calculation very large.In addition, for the commonly used principal component analysis (PCA) of Remote Sensing Data Processing with go envelope wave spectrum coupling etc., the impact that all is subject to vegetation is larger.
Crosta A P, Review in Economic Geology, 2009,16:59-82.Crosta and Moore is for Brazilian tropical rainforest vegetation, soil development, the characteristics that geologic mapping is limited, and standard principal component method (Standard PCA) defective that mineral spectra and principal component transform can not be mapped, the major component that has developed the feature guiding is selected (Feature Oriented Principal Component Selection) technology.The method successful Application is in the alteration Information charting in the areas such as the charting of Brazilian tropical rainforest Extract Mineralized Alteration rock, the charting of Iranian vegetation region porphyry copper Extract Mineralized Alteration rock.
Zhang Tingting etc. " mineral journal " 2011,922-926, " application of high-spectrum remote-sensing in the porphyry deposit alteration Information is extracted ", utilizing during high spectrum extracts the porphyry deposit alteration Information, the method of utilizing study area measured spectra data to absorb location matches by spectral signature is carried out alteration Information to study area and is extracted, and the geology mineralized belt in binding district classifies to alteration Information and arranges, and completes the hyperspectral model of Deep Mining alteration.
Chang Ruichun etc. " land resources scientific and technological management " 2012,29 (6), 84-87 " altered mineral information be configured in high-spectrum remote-sensing look for application pre-test in the ore deposit " utilizes MNF conversion in the mineral extraction model, PPI index to calculate pure end member, classify through spectrum angle (SAM) in the HyMap remote sensing images and successfully extract mineral, the popularity that this method of sufficient proof is applicable.The method is fine at the regional extraction effect of naked rock, but in the vegetation-covered area, " pure pixel " quantity that the pure pixel of PPI calculates when calculating is few, and comprises vegetation information.
" geological sciences " 2002 such as Ma Chao flies, 37 (3): 365-371. " use multi-source data extract high vegetated terrain lithological information " is take the Hunan Qiyang area as example: by the corresponding relation research to the first order derivative of different minimum plant elements and plant spectral, in conjunction with rock and soil geochemistry data, the plant leaf blade imaging data, biochemical component data etc. are set up the composition that extracts, spectrum, relation between characteristic wave bands, in remote sensing images, the large tracts of land abnormal information being carried out in the vegetation-covered area divides, obtain charting effect preferably, defective need to be large-area study area actual measurement plant groundization data, the ground data accuracy of actual measurement is very large on the result impact in actual applications.
Luo Wenfei etc. " spectroscopy and spectral analysis " 2010(30) 1628-1633 " the independent component analysis technology that high-spectrum remote sensing spectrum solution is mixed ", at the high-spectrum remote sensing Data processing, furtherd investigate the principal mode that low probability exposure target exists, by the comparison to linear mixing spectral model and ICA model, proposition has realized the target rapid extraction based on the mixed pixel decomposition method of antinoise and partially restrained ICA.The method is for being fit to when mineral constituent is less.
" the land resources remote sensing " 2011 such as Zhang Yuan flies, 04,6-13, " the multi-level isolation technics model and application research of Alteration Information of Remote Sensing ", when the information extraction of research multi-spectrum remote sensing alteration, the multi-level isolation technics of primary study " background " and " interference " has been summed up the multiple rule about background, interference and Alteration anomaly.The method characteristic of article is, at first selects the best band in remotely-sensed data, and data projection in two-dimensional space, by the space distribution of different material in Spectral feature scale, is removed the Alteration anomaly information that stays of disturbing as much as possible.Go to disturb the mineral extracting method to use multispectral, high-spectral data, in low vegetation-covered area, there is reasonable effect in naked petrographic province territory.When using high-spectral data, high-spectral data wave band interval is closeer, and data are little at the characteristic difference of two-dimensional space, and extraction effect is on the contrary not as multispectral data.And in the vegetation-covered area, in the Alteration anomaly of extraction, wherein containing vegetation information can't effectively remove.Use high-spectral data in the vegetation area, the effect of extraction is relatively relatively poor.
To sum up, have many employings of method model experience and test one by one and select wave band, random strong, lack the foundation of band selection reference, and do not have so the thorough vegetation spectrum that suppresses just can not isolate well the weak information of mineral in height reflection vegetation background.
Summary of the invention
Purpose of the present invention is exactly for above-mentioned the deficiencies in the prior art, provides a kind of based on planting repressed vegetation-covered area high-spectrum remote-sensing altered mineral extracting method.
The objective of the invention is to be achieved through the following technical solutions:
1, a kind of vegetation-covered area high-spectrum remote-sensing altered mineral extracting method, is characterized in that, comprises the following steps:
A, in the time period of sun altitude greater than 45 °, at first measure the reflectivity of blank, and observe its wave spectrum curve, until curve approximation becomes near the straight line 100% reflectivity, proofread and correct complete;
B, the page of then aiming at wide leaf plant with detecting gun are directly measured the blade face reflectance spectrum, and herbaceous plant is measured the reflectance spectrum of canopy, its reflectance spectrum of fresh of soil testing, and preserve the wave spectrum curve of institute's measuring plants and soil;
C, plant reflectance spectrum and the soil reflective spectrum of measuring compared, select the poor larger a pair of different-waveband of plant reflectivity and spectral reflectance, and the plant reflectivity of these two wave bands is identical, every two wave bands of selecting are as a pair of;
D, the EO-1Hyperion data are carried out pre-service, comprise removal, the conversion of absolute radiation value, bad line reparation and the atmospheric correction of the Hyperion data not being calibrated and are subjected to steam image wave band;
E, find out in step c corresponding wave band from pretreated data, every a pair of wave band is all carried out the wave band Difference Calculation: computing formula: b=b
1-b
2
In formula: b is the new wave spectrum emissivity that the wave band difference calculates, b
1, b
2Every a pair ofly be used for planting repressed wave spectrum reflectivity for what select;
F, the wave band after suppressing take vegetation pass through formula as one group:
Ask eigenwert and the proper vector of covariance matrix
|λE-C|=0 ②
A=C
T ③
Y=AX ④
Wherein, x
i(k, l) be pixel place ranks number (κ, ι) in the value at i wave band place,
Be the data mean value of i wave band, C (i, j) is covariance matrix, and A is the transposed matrix (T) of the covariance matrix C in X space, and Y is new principal component matrix, and X is original matrix.
Do principal component analysis (PCA), obtain principal component.After trying to achieve principal component, according to formula:
Try to achieve the principal component analysis (PCA) eigenwert;
Wherein, α
iThe eigenwert of n major component of former variable X, λ
iN the eigenwert of expression covariance matrix C, these two n are the same;
G, according to the principal component analysis (PCA) eigenwert, find out that absolute value is large and two principal components of opposite in sign are done the 2-D scatter diagram according to two principal components;
H, select the data at scatter diagram flex point place to be the end member wave spectrum of delineation, the everywhere flex point is labeled as different colors, when a flex point need to be determined scope, needs at first first to draw a circle to approve that outermost extremely to be set as one-level abnormal, usually represent with redness, pass through formula:
Wherein,
Average wave spectrum value, q ... N is the pixel number, and Xq is that the wave spectrum curve of q pixel is obtained the abnormal average wave spectrum of one-level.
Abnormal toward interior delineation secondary and then, usually represent with green, 6. obtain the abnormal average wave spectrum of secondary by formula; Then two wave spectrums are carried out wave spectrum coupling, mark is to be judged to be the same type wave spectrum more than 0.8, illustrates that the one-level at this flex point place is abnormal extremely abnormal for same type with secondary, otherwise be dissimilar extremely.
After i, selected whole abnormal informations, carry out the wave spectrum coupling through USGS standard spectrum storehouse, process by Spectrum Analysis instrument in ENVI software during the wave spectrum coupling, can determine the mineral type of abnormal information.
Beneficial effect: due to the impact of vegetation, the Rocks, Soils of exposure mostly is the small size zone in the vegetation-covered area, and ratio shared in remote sensing images is very little, shows as little target, weak information.Therefore, in the vegetation-covered area, especially high vegetation-covered area, due to the impact of the covering vegetation high reflectance of Rocks, Soils, the altered mineral extracting method of directly copying naked rock area mechanically can not directly extract altered mineral usually.Relatively low areal coverage distributes along road, river etc. even the mineral information of extracting is also many, but is subject to closing on pixel high reflectance Vegetation Effect, and the relative exposed area of effect is relatively poor.Extract a difficult problem for vegetation-covered area high-spectrum remote-sensing altered mineral, of the present invention focusing on based on regional rock, the analysis of soil and vegetation measured spectra, provide one to select best high spectral band combination to carry out the method that vegetation spectrum suppresses, by selecting the spatial structure characteristic analysis of wave band, solve the technical matters that vegetation separates with abnormal information with interference as a setting, realized vegetation-covered area altered mineral information extraction in conjunction with Spectral matching.With existing mineral extraction model relatively, obtained should the zone the rocks and minerals type.The present invention selects the best band combination by vegetation-covered area situ measurements of hyperspectral reflectance signature analysis, suppresses better vegetation information, effectively strengthens and extracts the altered mineral information characteristics of vegetation under covering, for mineral resources searching and prediction provide evidence.
Description of drawings:
Fig. 1 is vegetation-covered area high-spectrum remote-sensing altered mineral extracting method process flow diagram.
Fig. 2 is actual measurement plant and actual measurement soil spectral curve.
Fig. 3 is plant spectral and the soil spectrum comparison diagram of actual measurement.
Fig. 4 is the wave spectrum curve comparison figure of vegetation before and after atmospheric correction
(a) before atmospheric correction; (b) after atmospheric correction.
Fig. 5 is scatter diagram anomaly analysis figure.
All extremely reach abnormal distribution range in Fig. 6 scatter diagram
(a) curve of spectrum corresponding to abnormal information; (b) whole abnormal informations of scatter diagram;
(c) position of abnormal information in remote sensing images.
Fig. 7 is that mineral extract figure as a result.
Embodiment:
Be described in further detail below in conjunction with drawings and Examples:
Vegetation-covered area of the present invention high-spectrum remote-sensing altered mineral extracting method workflow as shown in Figure 1, for illustrating better based on high-spectrum remote-sensing mineral recognition methods under improved vegetation inhibition method, the below carries out mineral identification take area, Huma, Heilungkiang high-spectral data as example, and concrete steps are as follows:
The herborization on the spot of area, Huma, Heilungkiang, pedotheque are carried out spectrum test.
A, instrumental correction: (wavelength band is 350～2500nm) to the portable spectroradio spectrometer of FieldSpec Pro that adopts U.S. ASD company to produce, in the time period of sun altitude greater than 45 °, measure the reflectivity of blank, and observe its wave spectrum curve, until curve approximation becomes near the straight line 100% reflectivity.
B, the curve of spectrum are measured: aim at plant with detecting gun after proofreading and correct, the reflectance spectrum curve of measurement target plant is preserved the wave spectrum curve at last.For the wide leaf plant of Mongolian oak, white birch and shrub lespedeza, directly measure the blade face reflectance spectrum; And cotton grass is herbaceous plant, measures the reflectance spectrum of canopy; Soil digs fresh, test spectral, as shown in Figure 2.
Then the whole plant spectrals that will measure (surveying altogether 10 points, 50 spectrum) are averaging, and all soil spectrum (surveying altogether 10 points, 12 spectrum) is averaging, as next step use spectrum.
C, band selection: plant spectral and the soil spectrum of measuring compared (Fig. 3), two wave bands selecting plant reflectivity and spectral reflectance to differ greatly, the plant reflectance value of these two wave bands is identical simultaneously; Every two wave bands of selecting like this are as a pair of, and plant spectral reflectivity and the wavelength that as: wavelength is 2314nm is that the plant reflectivity of 701nm is identical, but the spectral reflectance of these two wavelength differs greatly.Find out altogether six pairs of wave bands (as table 1) in this experiment.
Table 1 selects to plant repressed 6 pairs of wave bands
D, the pre-service of EO-1Hyperion data: the EO-1Hyperion data are carried out pre-service, and pretreated process comprises does not calibrate and is subjected to the processing procedures such as the removal of steam image wave band, the conversion of absolute radiation value, bad line reparation and atmospheric correction to the Hyperion data.Concrete pre-service is as follows:
1. the not removal of calibration and aqueous vapor image wave band
In 242 wave bands of Hyperion image, the wave band of radiation calibration is VNIR8～57, SWIR77～224; Do not have the wave band of calibration to be set to 0 value, they are wave bands 1～7, wave band 58～76, wave band 225～242.The calibration wave band is extracted the image that generates 198 wave bands.
Due to VNIR wave band 56～57 and SWIR wave band 77～78 overlapping, in fact only have 196 independently wave bands.And the noise of SWIR77～78 is larger than VNIR56～57, usually keeps VNIR56～57, and deletes SWIR77～78.Like this, generate the image of 196 wave bands, its corresponding original wave band is 8～57 and 79～224.
In high-spectrum remote sensing data, spectral range 1356～1417nm, 1820～1932nm and be subjected to influence of moisture larger greater than the wave band of 2395nm in these wave bands, seldom comprise terrestrial information.Therefore, need they are rejected.To the Hyperion data, be subjected to the wave band of influence of moisture to be: 121～127,167～178 and 224, totally 20 wave bands.After rejecting is subjected to the wave band of influence of moisture, remaining 176 wave bands, that is: 8～57,79～120,128～166, which wave band of 179～223(digitized representation).The wavelength coverage of disallowable wave band sees Table respectively 2.
The Hyperion wave band that table 2 is disallowable
| The original wave band of Hyperion | Wavelength coverage (nm) |
| 1～7 | 355～416 |
| 58～78 | 936～923 |
| 121～127 | 1356～1426 |
| 167～178 | 1820～1931 |
| 224～240 | 2395～2577 |
2. the pixel value is changed to the absolute radiation value
The L1 Product Data Set of Hyperion, so that the integer data recording of symbol to be arranged, numerical range is-32767～+ 32767.But actual atural object radiation value is very little, and the greatest irradiation of VNIR is 750W/m2/sr/Lm, and that SWIR is 350W/m2/sr/Lm.Therefore, when product generates, adopted expansion factor, the factor coefficient of VNIR and SWIR is respectively 40 and 80.
When analytical applications Hyperion data, the pixel value must be converted to the absolute radiation value.At first, all VNIR wave bands generate a new image file divided by 40, and all SWIR wave bands generate another new image file divided by 80.Then, two image files are merged, obtain absolute radiation value image.
3. bad line reparation
Because also there is certain mistake in the demarcation of sensor, in the L1 of Hyperion level product, still there are abnormal data.Usually countless certificates or the very little a row or column of data value are called bad line.The Hyperion image of 176 wave bands is pursued the inspection of wave band, and record wave band that bad line exists and corresponding row number.Then, with the mean value reparation of its adjacent row or column.
4. Data Format Transform and atmospheric correction
The purpose of atmospheric correction is to eliminate the factors such as atmosphere and illumination to the impact of clutter reflections, obtains the actual physical model parameter on earth's surface, as clutter reflections rate, radiance, surface temperature etc.Utilize the Hyperion image to carry out the extraction of altered mineral information, must obtain the real reflectivity in earth's surface, therefore, need to carry out atmospheric correction to data, atmospheric correction adopts the FLAASH module of ENVI to carry out.Be the wave spectrum curve comparison figure of vegetation before and after atmospheric correction as Fig. 4.
E, remote sensing images wave band Difference Calculation: the wave band of correspondence find out table 1 in the pretreated data of EO-1Hyperion in, the wave band that uses the c step to obtain carries out Difference Calculation, the formula of Difference Calculation is: B=b1-b2, in formula: B is the new wave spectrum albedo image that the wave band difference calculates, and b1, b2 are that select every a pair of is used for planting repressed wave spectrum albedo image.In example as: first group of Difference Calculation B1=B2314nm-B701nm, namely centre wavelength is the poor of the albedo image of 2314nm and albedo image that centre wavelength is 701nm.B2=B2203nm-B681nm, namely centre wavelength is the poor of the albedo image of 2203nm and albedo image that centre wavelength is 681nm.Wave band after in this experiment, new vegetation suppresses is 6.
Bringing formula into is calculated as:
B1=2314-701
B2=2203-681
B3=2183-671
B4=2072-548
B5=2213-691
B6=1336-742
F, principal component analysis (PCA): as one group, do principal component analysis (PCA) according to following formula take 6 new wave bands (6 wave bands obtaining in following formula) that the e step is obtained, obtain 6 new masters and divide, major component eigenwert (as table 3).Namely in (e), the value of required B1 image is row, and the value of B2 image is secondary series, the like, form altogether one take the value of image as row the matrixes of totally 6 row, be designated as X, with the X substitution,
The principal component analysis process is used principal component transform function realization in ENVI.
The table 3 principal component analysis (PCA) list of feature values
G, 2-D scatter diagram: according to principal component analysis (PCA) eigenwert (table 3), find out that absolute value is large and two principal components of opposite in sign are done the 2-D scatter diagram, wherein the value of PC1 is negative value, and the value of PC2 be on the occasion of, the absolute value of PC1 and PC2 is larger simultaneously, so do scatter diagram (Fig. 5) with PC1 and PC2.
1., in the 2-D scatter diagram delineation of h, abnormal ranges:, select the data at scatter diagram flex point place to be the end member wave spectrum of delineation, the everywhere flex point is labeled as different colors, and flex point is the abnormal information of a type.
2., when an abnormal information is determined range size, need to first draw a circle to approve the outermost one-level abnormal (red) that extremely is set as, obtain its average wave spectrum (red), pass through formula:
3. red green two wave spectrums are carried out the wave spectrum coupling, process by ENVI software during the wave spectrum coupling, step comprises that spectral feature fitting is processed and final marking; Mark is to be judged to be the same type wave spectrum more than 0.8, illustrates that the abnormal and secondary of the one-level at this flex point place is same type (as Fig. 5) extremely extremely.
4. in the 2-D scatter diagram, draw a circle to approve successively whole flex points (abnormal information), and the different flex points of mark are different colours, obtain the average wave spectrum of everywhere flex point.The scope of the PC2 by the delineation scatter diagram and the value of PC1 can show the distribution range of abnormal information simultaneously in remote sensing images.Obtain respectively the average wave spectrum at each flex point place, both show the average wave spectrum (Fig. 6) of abnormal information distribution pixel in remote sensing images.After irising out flex point on scatter diagram, scope corresponding on the remotely-sensed data image of scope after pre-service is completed of flex point in scatter diagram is all marked.Each pixel on remote sensing images has a wave spectrum curve, and the wave spectrum of the whole pixels in scope is averaging.The averaged curve of obtaining is exactly the mean wave spectral curve of flex point on scatter diagram.
i, standard wave spectrum storehouse coupling: the wave spectrum abnormal information of drawing a circle to approve in h, the averaged spectrum of obtaining, mineral spectrum in the standard wave spectrum storehouse (belong to USGS wave spectrum storehouse) is as reference spectra, (comprise spectrum angle SAM by Spectrum Analysis in ENVI software, spectral feature fitting SFF, binary-coding, weight is respectively 0.3, 0.4, 0.3) judgement, when the mean wave spectral curve more similar to that mineral curve in storehouse, USGS standard POP, score is higher, what the coupling mark was the highest is this abnormal mineral type, the final mineral type of determining abnormal information, obtain mineral and extract result (as Fig. 7).
Claims (1)
1. a vegetation-covered area high-spectrum remote-sensing altered mineral extracting method, is characterized in that, comprises the following steps:
A, in the time period of sun altitude greater than 45 °, at first measure the reflectivity of blank, and observe its wave spectrum curve, until curve approximation becomes near the straight line 100% reflectivity, proofread and correct complete;
B, the page of then aiming at wide leaf plant with detecting gun are directly measured the blade face reflectance spectrum, and herbaceous plant is measured the reflectance spectrum of canopy, its reflectance spectrum of fresh of soil testing, and preserve the wave spectrum curve of institute's measuring plants and soil;
C, plant reflectance spectrum and the soil reflective spectrum of measuring compared, select the poor larger a pair of different-waveband of plant reflectivity and spectral reflectance, and the plant reflectivity of these two wave bands is identical, every two wave bands of selecting are as a pair of;
D, the EO-1Hyperion data are carried out pre-service, comprise removal, the conversion of absolute radiation value, bad line reparation and the atmospheric correction of the Hyperion data not being calibrated and are subjected to the influence of moisture wave band;
E, find out in step c corresponding wave band from pretreated data, every a pair of wave band is all carried out the wave band Difference Calculation: computing formula: b=b
1-b
2
In formula: b is the new wave spectrum reflectivity that the wave band difference calculates, b
1, b
2Every a pair ofly be used for planting repressed wave spectrum reflectivity for what select;
F, the wave band after suppressing take vegetation pass through formula as one group:
Ask eigenwert and the proper vector of covariance matrix
|λE-C|=0 ②
A=C
T ③
Y=AX ④
Wherein, x
i(k, l) be pixel place ranks number (κ, ι) in the value at i wave band place,
Be the data mean value of i wave band, C (i, j) is covariance matrix, and A is the transposed matrix (T) of the covariance matrix C in X space, Y is new principal component matrix, and X is original matrix, does principal component analysis (PCA), obtain principal component, after trying to achieve principal component, according to formula:
Try to achieve the principal component analysis (PCA) eigenwert;
Wherein, α
iThe eigenwert of n major component of former variable X, λ
iN the eigenwert of expression covariance matrix C, these two n are the same;
G, according to the principal component analysis (PCA) eigenwert, find out that absolute value is large and two principal components of opposite in sign are done the 2-D scatter diagram according to two principal components;
H, select the data at scatter diagram flex point place to be the end member wave spectrum of delineation, the everywhere flex point is labeled as different colors, when a flex point need to be determined scope, at first needs first to draw a circle to approve the outermost one-level that extremely is set as abnormal, usually represents with redness.Pass through formula:
Obtain the abnormal average wave spectrum of one-level;
Abnormal toward interior delineation secondary and then, usually represent with green, 6. obtain the abnormal average wave spectrum of secondary by formula; Then two wave spectrums are carried out wave spectrum coupling, mark is to be judged to be the same type wave spectrum more than 0.8, illustrates that the one-level at this flex point place is abnormal extremely abnormal for same type with secondary, otherwise be dissimilar extremely.Wherein
Average wave spectrum value, q ... N is the pixel number, and Xq is the wave spectrum curve of q pixel;
After i, selected whole abnormal informations, carry out the wave spectrum coupling through USGS standard spectrum storehouse, process by Spectrum Analysis instrument in ENVI software during the wave spectrum coupling, determine the mineral type of abnormal information.
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