Method for determining and displaying anomaly feature maps from measured (MULTI)spectral image data

Abstract

The invention relates to a method for determining and displaying anomaly feature data AMD:= [xn; A(λ, xn)] from spectral image data BD:= [xn; S(λ, xn)], comprising the following steps: determining and providing (201) the spectral image data BD=[xn; S(λ, xn)]; in the image data BD for all N pixels PIX(xn) in each case: in a predefined spatial environment U(xn) of the pixel PIX(xn) determining (202) neighbouring pixels PIX(xk,n) lying in this environment U(xn), where: k = 1, Kn; xk,n ϵ {x1, x2,...,xN}; xk,n≠xn; Kn < N; determining (203), from the spectra S(λ, xk,n assigned to the determined Kn neighbouring pixels PIX(xk,n), in each case those Kn* spectra S(λ, xk,n*), whose spectral deviation from the spectrum S(λ, xn) is smaller than a predefined spectral maximum deviation DS0; where Kn* ≤ Kn and xk,n* ϵ {xk,n}; determining (204) a local reference spectrum J(λ, xn) from the Kn* spectra S(λ, xk,n*); determining (205) an anomaly value A(λ, xn) as a function F of S(λ, xn) and J(λ, xn): A(λ, xn) = F(S(λ, xn); J(xn))-, and outputting and / or displaying (106) the N anomaly values A(λ, xn) as anomaly feature data AMD: = [(xn; A(λ, xn)] = [(xn); F(S(λ, xn), J(λ, xn))].

Description

[0001] Methods for determining and displaying anomaly feature maps from measured (multi-)spectral image data

[0002] The invention relates to a method for determining and displaying anomaly feature maps from measured (multi-)spectral image data, in particular from spectral image data of a multispectral or hyperspectral remote sensing sensor. This (multi-)spectral image data is typically composed of several spectral channels. The invention further relates to a computer system, a digital storage medium, a computer program product, and a computer program with program code for carrying out this method.

[0003] The object of the invention is to provide a robust and computationally time-saving method that allows, in particular in multispectral or hyperspectral image data, such as hyperspectral remote sensing data, the detection and representation of spectral anomalies.

[0004] The invention is defined by the features of the independent claims. Advantageous further developments and embodiments are the subject of the dependent claims. Further features, applications, and advantages of the invention will become apparent from the following description and the explanation of exemplary embodiments of the invention illustrated in the figures.

[0005] Note: the terms “multi-spectral image data” and “spectral image data” are used synonymously here and refer to image data that is composed of one or more spectral channels.

[0006] The task involves a method for determining and representing anomaly feature data AMD:= [x"; A(x n)] from spectral image data BD:= [x"; S(X, x^J, with x": two-dimensional position of the nth pixel PIX(x^) in the spectral image data BD, with n = 1, 2, N, where N is the total number of pixels PIX(x") in the spectral image data BD,

[0007] S(X, xj: the pixel associated (multispectral or hyperspectral) spectrum, with A:= wavelength,

[0008] A(X, xj): one- or multi-dimensional anomaly value assigned to pixel P / ATAJ, solved.

[0009] Advantageously, the spectrum S(k, x") is a spectrum assigned to the pixel PIX(x) with measured values ​​X. m in M spectral bands / channels, with m = 1, 2, A and dimX = M. Therefore, the spectrum can be S(X, xj) = S(X m=1 , X m=2 , X m=3 , X m=M , xj is to be interpreted as an M-dimensional vector! which corresponds to the pixel PIX(x n ) is assigned.

[0010] The proposed procedure comprises the following steps:

[0011] • Determine and / or provide spectral image data BD=[x"; S(X, x")J;

[0012] • in the image data BD for all N pixels PlXfxJ each:

[0013] • in a given spatial environment U(x") of the respective pixel P / A7A;

[0014] Determining neighboring pixels PIX(x) located in this neighborhood U(x") k ,“) with: k = 1, K“; x k “ c {x lt x2, ...,x N}; x k “px“; K“ < N;

[0015] • from the determined K“ neighboring pixels PIX(x kn ) assigned spectra S(X, x k , n ) Determining those K„* spectra S(X, x k “*), whose spectral deviations (in the spectral domain) from the spectrum S(l, x“) are each smaller than a given maximum spectral deviation DS0', with K“* < K n and x kn * c {x kn} , x kn * px n ,

[0016] • from the K„* spectra S(X, xk “*) Determining a local reference spectrum

[0017] J(2, x);

[0018] • Determining an anomaly value A(2, x") as a function F of S(X, x") and

[0019] J(2, x"), ie A(X, x") = F(S(2, x"), J(x n ))', and

[0020] • Output and / or display the N anomaly values ​​A(x) as anomaly feature data AMD:= [(x n ); A(X, x")J = [(xj; F(S(X, x"), J(X, xj)].

[0021] The spectral image data BD:= [x"; S(X, x")] initially provided for the proposed method are preferably generated by acquiring / measuring / simulating spectra S(X, x") for assigned irregularly or preferably regularly arranged pixels PIX(x"), where x" typically represents a two-dimensional position of the respective pixel PIX(x) in the image data. The term "pixel" here refers to the smallest addressable picture element in the spectral image data BD. Preferably, the image data BD are acquired by a (two-dimensional) multispectral or hyperspectral image sensor that has a plurality of sensor elements arranged in a preferably regular (especially orthogonal) sensor matrix. Such image sensors are particularly useful in Earth-observing remote sensing systems (satellite- or aircraft-borne). For example, the image data BD for the present method can be...The data can be generated by the hyperspectral satellite EnMAP, with each sensor pixel acquiring intensity measurements in M=224 spectral channels in the spectral range from 420 nm to 2450 nm with a spectral sampling of 6.5 nm (VNIR) and 10 nm (SWIR). Of course, multispectral image data from commercially available digital cameras, smartphones, etc., are also suitable for applying the proposed method.

[0022] The proposed method for determining and displaying anomaly feature data proves to be a very robust, reliable, and computationally time-saving method. Furthermore, it enables a clear visualization of anomalies in spectral image data.

[0023] After the spectral image data BD:= [x"; S(X, x")J are provided, the following steps 1 to 4 are carried out for each pixel PZ¥(x^) or for the spectrum S(X, x") of the spectral image data BD assigned to the pixel PIXCxj:

[0024] Step 1: In a given environment U(x^) of the respective pixel PIX(x„), determine the properties in this environment. lying neighboring pixels PIX(x hn ) with: k = 1,..., K; x k “ e {x h x2, ...,x N}; x k “px“; K“ < N. The environment to be examined is specified, e.g. as a radius around the respective pixel PZ¥(x^), such that all pixels PIX(x mkr! ), which (e.g., completely (100%) or at least to, for example, 1%, ..., 50%, ..., 99% within this environment to x n are arranged, whose neighboring pixels are PIX(x hn Advantageously, the neighborhood f7(x") is defined by positions x in the spatial domain for which a given distance metric Dx holds: |x - x"| < d0, where d0 is a spatial metric distance limit. Advantageously, the given distance metric Dx is a Euclidean distance metric.

[0025] Step 2: From the determined K“ neighboring pixels PIX(x kn ) assigned spectra S(X, x kn ) in the spectral domain Determine those K„* spectra S(X, x) kn *), whose spectral deviation from the spectrum S(X, x") of the respective pixel PZ¥(x^ is smaller than a given maximum spectral deviation DS0', with K"* <K„ und x kn * c {x kn In this step, a maximum spectral deviation DS0 from the spectrum S(F x") is specified in the spectral domain. Advantageously, the K"* spectra are determined S(X, x). kn *) based on a given spectral distance metric: DS = < DS0, where DS0 is a spectral metric distance limit. The spectral distance metric is advantageous: DS := \S(F x ) kn *) -S(F

[0026] < DS0 a Euclidean spectral distance metric, where:

[0027] Step 3: From the K„* spectra S(X, x k“*) Determining a local reference spectrum J(X, x"). Advantageously, the local reference spectrum J(X, x) for the pixel PIX(xJ) is determined from the neighboring pixels PIX(x) determined by K“*. kn *) assigned spectra S(Ä, x kn *) by averaging the spectra S(X, x kn *) :

[0028] (2)

[0029] Thus, for each pixel PIXCxj under consideration, after this step, the originally provided spectrum S(F x„) and the respective reference spectrum J(F x„) are available.

[0030] Step 4: Determining an anomaly value A(X, x^) as a function F of S(F x^) and J(X, x„):

[0031] A(X, x") = F(S(F x"); J(X, x")). Statistical functions and combinations of such functions are generally suitable as such functions F for evaluating and comparing data sets: S(F x") and J(X, x"), such as functions for determining means, differences, medians, quantiles, and measures of dispersion (variances, standard deviations, coefficients of variation, errors of estimate, confidence intervals, correlations, coefficients of determination, etc.).

[0032] Determining the anomaly value A(F x„) = F(S(F x^; J(X , x„)) advantageously includes, in particular: a normalization of the spectrum S(F x„) to the assigned reference spectrum

[0033] J(F x„): A(F x„) = Ai(X, x„)= F1(X, x„) = S(F x„) / J(F x„) and / or determining a difference spectrum:

[0034] A(A, x") = A( / .. x") = F F. x") = S( / .. x") - J( / ..x.,) and / or a spectral integration, advantageously: or

[0035] A( xj = A4( xj = F4( xj = N~' j F2(Ä, X„)M , with normalization N advantageously implemented by N=A2-AI where alternatively an average over the entire respective spectrum A or an average over several sections [A x , A y ] of the respective spectrum can be formed, and / or a spectral Fourier transformation: where A=\A\e ,(p becomes complex-valued, so that its amplitude |^| can advantageously be considered as an anomaly measure, and / or the steps 1 to 4 described above can be performed sequentially in time for the pixels / VATAJ or in parallel in time for all pixels PIX(x") or for groups of pixels PIX(x").

[0036] After the anomaly values ​​A(A, x") as a function F of S(A, x") and J(A, x") as

[0037] A(A, x") = F(S(A, x"); J(xJ) for all N pixels PIX(x nOnce steps 1 to 4 have been determined for all pixels PZY / xJ, the resulting TV anomaly values ​​or functions A(x"), A(A, xj, A(l, x") are considered anomaly feature data.

[0038] AMD:= [(x"); A(A, x^)] = [(x^; F(S(A, x"), J(A, x^)] is output and / or displayed. Output is advantageously done on a visual human-machine interface (e.g., a monitor, especially a color monitor) or as a printout, particularly a color printout from a printer. Of course, the anomaly feature data AMD can also be output digitally as a file.

[0039] In further training, it becomes advantageous to generate a pseudo-RGB color coding of the anomaly values ​​A(A, x^) three-dimensional anomaly values ​​ArfA, x" based on F3(A, x^) = S(A, Xn) / J(A,x") according to:

[0040] (3) with

[0041] A i > A2 > A3 > A4 > A5 > A6: wavelength values ​​in the spectrum F t(X, x“) determined.

[0042] An alternative training method for generating a pseudo-RGB color coding of the anomaly values ​​A2(x") offers the advantage of three-dimensional anomaly values ​​A(X, x") based on a difference spectrum F2(Fx"). according to: with

[0043] A i > X2> / .3> A 4 > z5> A6: Wavelength values ​​in the spectrum F2(F x„) determined.

[0044] The pairs of values ​​[z 1 , z2] [A3, A4] and [z5, z6] each define limits of spectral ranges for which the pseudo-RGB color coding of the assigned spectral anomaly values ​​A(X, x„) R:G:Bis representative. Preferably, [z1, z2] represents "red", [z3, A4] represents "green", and [z5, 16] represents "blue". By appropriately narrowing the selection of, for example, one of these spectral ranges, spectral anomalies associated with an absorption wavelength characteristic of a particular atom / molecule can be assigned a color from the pseudo-RGB color coding. For example, two absorption windows are known for methane: center 1 at 1500 nm and center 2 at 2300 nm. If at least one of the spectral ranges is chosen to include a center wavelength of, for example, 1500 nm, then the pseudo-color assigned to that spectral range is indicative of the presence and concentration of methane. Furthermore, certain spectral bands can represent characteristics of materials and objects on the Earth's surface. For example, solar modules typically absorb in certain wavelength ranges.

[0045] An alternative training method for generating a pseudo-RGB color coding of the anomaly values ​​A(x") offers the advantage of three-dimensional anomaly values ​​A(x) based on the Fourier transform. according to:

[0046] (3) with

[0047] / -I > / 2> / 3> / 4> / 5> / 6: determined. The Fourier transform F5 transforms the difference spectrum F2(A, x") from the spectral domain into its harmonic components l.

[0048] The preceding explanations regarding the pseudo-RGB color coding of the anomaly values ​​A(x") can, of course, also be applied to color codings of the anomaly values ​​A(x) in color spaces with color dimensions > 2. Thus, corresponding color codings in 4- or 5-dimensional color spaces are likewise encompassed by the inventive concept.

[0049] Are the anomaly values ​​A(x^ RIf, as described above, the RGB color codes are used, then for each pixel PIX(x") a color value A(x) results from the mixture of the RGB color values. RrGB generated RGB mixed color. The following distinctions can be made: a) A(x n ) R = A(x n ) G = A(x n ) B \ results in a grey hue b) A(X^) R = A(X^) G = A(X) B = 0: results in a black hue c) A(x r! ) R = A(x n ) G = A(X) B = maximum value in each case: results in a white hue. Accordingly, a gray or black hue indicates that there is no spectral anomaly for the considered pixel PIX(x) in the context of the spatial environment of the pixel PIX(x). n ) gives. Case d) A(X, x") R # A(X, x") G and / or A(X, x") R # A(X, x") B and / or A(X, x") G t A(k, x„) BHowever, it indicates a local anomaly in the respective spectral range.

[0050] The proposed method can thus be used to generate spatial-spectral anomaly maps, for example, to identify and display spectral disturbances / anomalies on the surface of an object depicted in the image data (BD) and / or spectral disturbances / anomalies in the image data (BD) caused by distortions in the air between a multispectral or hyperspectral sensor and the detected object. Such spectral anomalies in the image data (BD), detectable and displayable with this method, can, for example, originate from the tilt of a material surface depicted in the image data (BD) from which light is reflected. Shadows of objects in the vicinity of the object depicted in the image data (BD) can affect the reflected spectrum and thus cause a spectral anomaly.Spectral anomalies in image data (BD) also result from a different, spatially varying chemical composition on a surface depicted in the image data, to name just a few causes of spectral anomalies in image data.

[0051] The proposed method makes particular use of the spectral distance:

[0052] \S(Ä, x kn *) -S(X, x„)\ (in the spectral domain) to generate an aggregated local reference spectrum J(X, x^) for physically based downstream analyses to identify subtle variations of spatially and spectrally close spectra.

[0053] Another aspect of the invention relates to a computer system with a data processing device, wherein the data processing device is configured such that a method as described above is carried out on the data processing device.

[0054] Another aspect of the invention relates to a digital storage medium with electronically readable control signals, wherein the control signals can interact with a programmable computer system to execute a method as described above. A further aspect of the invention relates to a computer program product comprising program code stored on a machine-readable medium for carrying out the method as described above when the program code is executed on a data processing device.

[0055] Another aspect of the invention relates to a computer program with program code for carrying out the method as described above when the program runs on a data processing device. For this purpose, the data processing device can be configured as any computer system known from the prior art.

[0056] Further advantages, features, and details will become apparent from the following description, in which – possibly with reference to the drawings – at least one embodiment is described in detail. Identical, similar, and / or functionally equivalent parts are identified by the same reference numerals.

[0057] They show:

[0058] Fig. 1.1 shows an exemplary, schematic two-dimensional arrangement of N = 30 pixels PIX(x") of the spectral image data BD in the spatial domain.

[0059] Fig. 1 .2 a schematic representation of the spectra S(X, x") assigned to the pixels PZY / xJ in a three-dimensional spectral domain, and

[0060] Fig. 2 shows a schematic sequence of a proposed procedure.

[0061] Fig. 1.1 shows an exemplary two-dimensional spatial arrangement of thirty (N = 30) pixels PIXCxj of the spectral image data BD in the spatial domain. Let us first consider the pixel PIX(x) 101.1 in the center. All other pixels shown are located in the given spatial environment of the considered pixel PIX(x). n ) 101.1.

[0062] This considered pixel PIX(x) 101.1 thus has eight first (5 x 102.1 and 3 x 103.1), sixteen second 104.1 and five represented third 105.1 neighbor pixels PIX(x) k-n ), with k = 1, 2, ..., K = 29.

[0063] Note: “.1” indicates that it is the spatial domain, while in Fig. 1.2 “.2” indicates that it is the spectral domain for the associated spectra.

[0064] Each of the pixels PIX(x") shown in Fig. 1.1 is assigned a spectrum S(X, x") in the image data BD, with n = 1, 2, N = 30. A spatial distance, for example, can be used to define the given environment.

[0065] Fig. 1.2 shows a schematic representation of the spectra S(X, x'') assigned to the pixels PIX(xJ) of Fig. 1.1 in a three-dimensional spectral domain. The spectrum S(X, x'') 101.2 assigned to pixel PIX(x'') 101.1 is represented in the spectral domain as a three-dimensional point (circle). The maximum specified spectral deviation DS0 from the spectrum S(X, x'') 101.2 is represented in Fig. 1.2 by a sphere (indicated as a circle) with radius DS0 around the spectrum S(X, x'') 101.2.

[0066] Within this sphere, in addition to the spectrum S(l, x") 101.2, there are also three spectra S(2, xj) 103.2 of the three pixels PIX(x") 103.1 of Fig. 1.1, as well as a spectrum S(X, x") 104.2* of the pixel PIX(x^) 104.1* of Fig. 1.1.

[0067] From the three spectra S(X, xj 103.2) and the spectrum S(X, x„) 104.2* lying within the sphere, the reference spectrum J(2, x„) 106 for the pixel PIX(x„) 101.1 is now determined. All spectra lying outside the sphere are disregarded.

[0068] It is essential to note that the spectra of pixels adjacent in the spatial domain can be further apart spectrally in the spectral domain, as shown in Fig.

[0069] 1.2 clearly expresses this. Thus, in the spatial domain (Fig. 1.1), the five pixels PIX(x") 102.1 are immediate neighbors of the considered pixel PIX(x") 101.1, but their spectra lie in the spectral domain outside the sphere defined by the maximum spectral distance DS0 to the spectrum S(X, xj) 101.2.

[0070] Fig. 2 shows a schematic procedure flow of a proposed method for determining and displaying anomaly feature data.

[0071] AMD:= [x"; A(X, x^)] from spectral image data BD:= [x"; S(X, x^)J, with x": two-dimensional position of the nth pixel PIX(x^) in the spectral

[0072] Image data BD, with n = 1, 2, N, where N is the number of pixels PIX(x n ) in the spectral image data BD is,

[0073] S(X, xj): spectrum assigned to pixel P / ATAJ, with2:= wavelength, A (A, x„)'- one- or multi-dimensional anomaly value assigned to pixel PIX(x„).

[0074] In step 201, the spectral image data BD=[x] is determined and provided. n ; S(X, x„)].

[0075] In the image data BD, the following steps 202 to 205 are executed sequentially or in parallel for all N pixels PIX(x"):

[0076] In step 202, in a given spatial environment U(x) n) of pixel PIX(x) (see pixel 101.1 in Fig. 1.1 ) a determination of in this neighborhood U(x n ) adjacent neighboring pixels PIX(x hn ), with: k = 1, K n ; x kn € {x h x2, ...,XN}; x kn + x n ; K n < N.

[0077] In step 103, the K determined is used to calculate n Neighbor pixels PIX(x kn ) assigned spectra Sf / , x kn ) a determination of each of those K n * Spectra S(F x kn *), whose spectral deviation DSF, x“) from the spectrum Sf / , x n ) is smaller than a given maximum spectral deviation DS0; with K n * < K n and x kn * € {x kn}.

[0078] In step 204, S(F x) is calculated from the K„* spectra. kn *) Determining a local reference spectrum J(X, x“) cf. spectrum 106 in Fig. 1 .2.

[0079] In step 205, anomaly values ​​A(X, x") are determined as a function F of S(X, x") and J(X, x"): A(X, x") = F(S(X, x"); J(x")).

[0080] After steps 102 to 105 for all pixels PIX(x) n Since the processes are carried out, there are consequently N potentially multi-dimensional anomaly values ​​A(X, x„).

[0081] Finally, in step 206, the TV anomaly values ​​AfA, x^ are output and / or displayed as anomaly feature data AMD:= [(x"); AfA, xj] = [(x"); F(S(X, x"), J(F x"))].

[0082] Although the invention has been further illustrated and explained in detail by means of preferred embodiments, the invention is not limited by the disclosed examples, and other variations can be derived from them by a person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that a multitude of possible variations exist. It is also clear that the embodiments mentioned as examples are truly only examples and are not to be understood in any way as limiting, for example, the scope of protection, the possible applications, or the configuration of the invention.Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without leaving the scope of protection defined by the claims and their legal equivalents, such as a further explanation in the description.

[0083] Reference symbol list

[0084] 101.1 pixels in the spatial domain

[0085] 102.1 pixels in the spatial domain

[0086] 103.1 pixels in the spatial domain

[0087] 104.1 pixels in the spatial domain

[0088] 105.1 pixels in the spatial domain

[0089] 101.2 Spectrum in the spectral domain

[0090] 102.2 Spectrum in the spectral domain

[0091] 103.2 Spectrum in the spectral domain

[0092] 104.2 Spectrum in the spectral domain

[0093] 105.2 Spectrum in the spectral domain

[0094] 106 Reference spectrum J(X, xj of pixels 101 .1

[0095] 201 - 206 Procedural steps

Claims

Patent claims 1. Procedure for determining and presenting anomaly feature data AMD:= [x"; Aß, x^)] from spectral image data BD:= [x"; Sß, x^)J, with x": two-dimensional position of the nth pixel PIX(x") in the spectral Image data BD, with n = 1, 2, ..., N, where N is the number of pixels PIX(x") in the spectral image data BD, Sß, x„)\ the pixel assigned spectrum, with 2:= wavelength, Ate, xj: the pixel associated one- or multi-dimensional Anomaly value, with the following steps: Determine and provide (201 ) the spectral image data BD=[x„; Sß, x^J; in the image data BD for all N pixels PlXßJ each: - in a given spatial environment t7(x") of pixel P / ATAJ Determine (202) of in this environment adjacent neighboring pixels PIX(xk,„) with. - from the determined K“ neighboring pixels PIX(x krn ) assigned spectra Sß, x kn ) Determine (203) each of those K„* spectra Sß, x kn *), whose spectral deviation from the spectrum Sß, x„) is smaller than a given maximum spectral deviation DS0', with K„* <K„ und x k , n * e ßk,n} > - from the K„* spectra Sß, x kn *) Determining (204) a local reference spectrum Jβ, x“); and - Determining (205) an anomaly value Aß, x„) as a function F of Sß, x„) and Jß, x„): Aß, x„) = F(Sß, x„); J(x n ))', and Output and / or display (206) the N anomaly values ​​Aß, xj as anomaly feature data AMD:= [(x„); Aß, xj] = [(xj; F(Sß, xß Jß, x„))].

2. Method according to claim 1, wherein Sß, x„) is a spectrum assigned to the pixel PIX(x^) with in M spectral bands, with m = 1, 2, ..., A / and dimX = M.

3. Method according to claim 1 or 2, wherein the environment t7(x") is defined by positions x for which a predetermined The distance metric Dx holds true: |xx„| < d0, with d0\ distance limit.

4. Method according to any one of claims 1 to 3, wherein the determination (203) of the K„* spectra S(F x k “*) based on a given spectral distance metric: DS = \S(F x kn *) -S(F x^| < DS0 occurs.

5. Method according to any one of claims 1 to 4, wherein the determination (204) of the local reference spectrum J(X, x") for the pixel PIX(x") from the neighboring pixels PIX(x) determined by the K** k , n *) assigned spectra S{X, x kn *) by averaging the spectra S(F x kn *) is done: (2) 6. Method according to any one of claims 1 to 5, wherein determining (205) the anomaly values ​​A(X, x") = F(S(X, x"); J(X , x")) - a normalization of the spectra S(F x„) to the assigned reference spectra J(F x„) includes: A(X, Xr) =A1(X, x") = F](X, Xr) = S(X, Xn) / J(F x") or - determining a difference spectrum A(F x„) =A2(xn) = F2(FX„) = S(A, x„) - J( / ..x.,) includes or a spectral integration - a spectral Fourier transform A(l, xj =A5(l, X") = F5( / ,x") = jF (2,xJe _ ' / Ä 6 / 2 includes, and / or an averaging over the entire respective spectrum, e.g., S(F x„), Fj(F x„), F2(F Xr), F3(FX„), F4(X, x„) or over one or more sections [X Xr X y ] of the respective spectrum.

7. Method according to any one of claims 1 to 6, wherein, to generate a pseudo-RGB color coding of the anomaly values ​​A(X, x"), three-dimensional anomaly values ​​Aj(F x") based on F2(FX) = S(A, x) are used. n ) / J(Fxn) according to: with Ai >A2>A3>A4>A5>A e : Wavelength values ​​in the spectrum F2(X, x") are determined.

8. Method according to any one of claims 1 to 6, wherein, to generate a pseudo-RGB color coding of the anomaly values ​​A(x"), three-dimensional anomaly values ​​A2(x^) are based on a difference spectrum F2(F x") = S(F x n )-J(X,x^ according to: (4) with Ai >A2^^3>^4^^5>^6- Wavelength values ​​in the spectrum F2(F x„) are determined.

9. Computer system comprising a data processing device, wherein the data processing device is configured such that a method according to one of claims 1 to 8 is executed on the data processing device.

10. Digital storage medium with electronically readable control signals, wherein the Control signals can interact with a programmable computer system in such a way that a method according to one of claims 1 to 8 is carried out.

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