Noctilucent remote sensing data correction method and system, computer equipment and storage medium

Abstract

The invention provides a noctilucent remote sensing data correction method and system, computer equipment and a storage medium, and belongs to the technical field of urban environment monitoring, and the method comprises the steps: carrying out the statistics of an impervious layer and a non-impervious layer of a target region through a multi-source remote sensing sensor, and calculating a multi-source data consistency index CI; checking an impervious layer classification result of the target area through a multi-source data consistency index CI of the target area; according to the corrected classification result of the impervious layer of the target area, the number Nb of impervious layer pixels and the number Nn of non-impervious layer pixels in the target area are counted respectively, and the proportion Pb of the impervious layer in a unit pixel is calculated through Nb and Nn; a correction model is constructed by using the noctilucent remote sensing brightness value NTL and the impervious layer proportion Pb in a unit pixel, so that light overflow interference of an impervious layer area is effectively reduced, the accuracy of impervious layer data is remarkably improved, and high-precision data support is provided for urban expansion monitoring, light pollution evaluation and social and economic activity analysis.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of urban environment monitoring, and particularly relates to a luminescent remote sensing data correction method and system, a computer device and a storage medium. BACKGROUND

[0002] With the acceleration of urbanization, luminescent remote sensing data plays an increasingly important role in urban environment monitoring, urban expansion research and light pollution assessment. The intensity of luminescence not only reflects the economic activities and population distribution of the city, but also is closely related to the energy consumption and environmental quality of the city. However, the luminescent remote sensing data has an overflow effect, which seriously affects the accuracy and reliability of the luminescent remote sensing data, and further affects the fine application of the luminescent remote sensing data.

[0003] In the prior art, although some methods have been used to correct the luminescent remote sensing data, the traditional correction methods often rely on complex models and a large amount of prior knowledge, and it is difficult to achieve efficient and accurate correction in practical applications. Moreover, the traditional correction methods are mostly focused on the correlation analysis between impervious layers and urban expansion, and the application of impervious layer data in the correction of luminescent remote sensing overflow effect is still in the exploratory stage. SUMMARY

[0004] In order to solve the above background problems, the present application quantifies the correlation between the spatial distribution characteristics of impervious layers and the overflow intensity of luminescent remote sensing, and establishes a luminescent correction model with physical meaning, thereby providing a luminescent remote sensing data correction method, system, computer device and storage medium for urban environment monitoring technology.

[0005] In order to achieve the above purpose, the present application provides the following technical scheme:

[0006] A luminescent remote sensing data correction method, specifically comprising:

[0007] dividing the ground surface of a target area into a uniform resolution of pixel grids, and counting the total number N of pixel grids in the target area total ; extracting the luminescent remote sensing brightness value NTL corresponding to each pixel grid in N total .

[0008] Obtaining impervious layer classification data of different resolutions in the target area, respectively mapping the impervious layer classification data of different resolutions to the same geographic coordinates, and aligning them one by one with the center of the pixel grid.

[0009] If the impervious layer classification data of different resolutions at the same pixel grid position are all determined as impervious layer, then the pixel is determined as impervious layer, otherwise the pixel is non-impervious layer.

[0010] Ntotal The total number of pixels determined as impervious layers in the target region is N agree The total number of pixels determined as impervious layers in the target region is N agree The total number of pixels determined as impervious layers in the target region is N total The total number of pixels determined as impervious layers in the target region is N The total number of pixels determined as impervious layers in the target region is N

[0011] According to the corrected impervious layer classification result of the target region, the number of impervious layer pixels N b and the number of non-impervious layer pixels N n in the target region are counted respectively. b and N n The impervious layer proportion P b per unit pixel is calculated.

[0012] The correction model is constructed using the night light remote sensing brightness value NTL and the impervious layer proportion P b per unit pixel to correct the night light remote sensing brightness value.

[0013] Preferably, the total number of pixels determined as impervious layers in the target region is N agree and N total The multi-source data consistency index CI is calculated, and the expression includes:

[0014]

[0015] Wherein, N total is the total number of pixel grids in the target region, N agree is the total number of pixel grids determined as impervious layers in the target region, and N total is the total number of pixel grids determined as impervious layers in the target region.

[0016] Preferably, the impervious layer classification result of the target region is corrected by the multi-source data consistency index CI of the target region, specifically including: correcting by weighted voting and visual comparison verification.

[0017] Preferably, the correcting by weighted voting and visual comparison verification includes:

[0018] If the multi-source data consistency index CI of the target region ranges from 0.5 to 0.8, the classification result is determined by weighted voting, and the formula is as follows:

[0019]

[0020] Wherein, P best is the best impervious layer classification result, k is the impervious layer or non-impervious layer, n is the impervious layer classification data of different resolutions, w i is the weight of the i-th data source, and δ(P ik) is an indicator function, which takes value 1 when the classification result of the ith data source is k, otherwise 0;

[0021] If the multi-source data consistency index CI of the target region is less than 0.5, the classification result is superimposed on the high-resolution image for manual correction of the classification result.

[0022] Preferably, the N b and N n are calculated by the data processing module. b The expression includes:

[0023]

[0024] Wherein, P b is the impervious layer ratio in the unit pixel grid, N b is the number of impervious layer pixel grids in the target region, and N n is the number of non-impervious layer pixel grids in the target region.

[0025] Preferably, the expression of the correction model is as follows:

[0026] NTL ENH = NTL*P b ;

[0027] Wherein, P b is the impervious layer ratio in the unit pixel, NTL is the night light remote sensing brightness value, and NTL ENH is the corrected night light remote sensing value.

[0028] A night light remote sensing data correction system, characterized in that it comprises:

[0029] A data processing module is configured to divide the ground surface space of a target region into a uniform resolution pixel grid, count the total number N total of pixel grids in the target region, extract the night light remote sensing brightness value NTL corresponding to each pixel grid in N total , obtain impervious layer classification data of different resolutions of the target region, and respectively map the impervious layer classification data of different resolutions to the same geographic coordinates and align with the center of the pixel grid one by one.

[0030] A data classification module is configured to determine that a pixel is an impervious layer if the impervious layer classification data of different resolutions at the same pixel grid position are all determined to be impervious layers, otherwise the pixel is a non-impervious layer; and record the total number of pixels determined to be impervious layers in the target region as N total , and the total number of pixels determined to be non-impervious layers as N agree . agree total ​Calculate the multi-source data consistency index (CI), and use the CI of the multi-source data consistency index of the target area to calibrate the classification results of the impermeable layer in the target area.

[0031] The correction module is used to count the number N of impermeable layer pixels in the target area based on the classification results of the impermeable layer in the calibrated target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0032] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement any of the steps in the nighttime light remote sensing data correction method.

[0033] The present invention also provides a computer-readable storage medium storing a computer program that, when loaded by a processor, can execute any of the steps in the nighttime light remote sensing data correction method.

[0034] The nighttime light remote sensing data correction method provided by this invention has the following beneficial effects:

[0035] The classification results of the impermeable layer in the target area are calibrated using the multi-source data consistency index (CI), significantly improving the accuracy of the impermeable layer data and providing a reliable foundation for subsequent calibration. The constructed calibration model can effectively reduce light spillover interference in low impermeable layer areas and improve the correction effect of spatial autocorrelation effect in nighttime light remote sensing data. The calibrated nighttime light remote sensing data can more accurately reflect the boundaries of urban built-up areas, providing high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis. Through automated calculation and calibration processes, manual intervention is reduced, data processing efficiency is improved, and it is suitable for large-scale urban environmental monitoring applications. Attached Figure Description

[0036] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a nighttime light remote sensing data correction method according to an embodiment of the present invention;

[0038] Figure 2 Figure 1 is a relationship diagram of impervious data and night light remote sensing brightness values of an embodiment of the present application; wherein, Figure 2 Figure 1(a) is a distribution diagram of impervious layer proportion in Zhejiang Province of an embodiment of the present application, Figure 2 Figure 1(b) is a distribution diagram of NDBI in Zhejiang Province, Figure 2 Figure 1(c) is a scatter diagram of impervious layer proportion and night light value correlation, Figure 2 Figure 1(d) is a scatter diagram of NDBI and night light value correlation;

[0039] Figure 3 Figure 2 is a spatial comparison result of night light change and impervious layer proportion of an embodiment of the present application;

[0040] Figure 4 Figure 3 is a proportion segmentation method of an embodiment of the present application;

[0041] Figure 5 Figure 4 is a result diagram of night light correction based on impervious layer index of an embodiment of the present application; wherein Figure 5 Figure 4(a) is an original NTL distribution diagram, Figure 5 Figure 4(b) is an NTL result after correction based on Pb index. DETAILED DESCRIPTION

[0042] In order to make the technical personnel of the present application better understand the technical solutions and can be implemented, the present application is described in detail below in conjunction with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot limit the protection scope of the present application.

[0043] The present application provides a night light remote sensing data correction method, specifically as Figure 1 shown, comprising:

[0044] S1, the target area ground space is divided into a uniform resolution pixel grid, and the total number N of the pixel grid in the target area is counted total ; extract the night light remote sensing brightness value NTL corresponding to each pixel grid in N total ; obtain impervious layer classification data of different resolutions in the target area, respectively map the impervious layer classification data of different resolutions to the same geographic coordinates, and align one by one with the center of the pixel grid.

[0045] Obtain multiple impervious layer data sources of different resolutions (10m-30m), and perform spatial superposition matching. The best impervious layer data is determined through spatial comparison. The impervious layer classification data of different resolutions is uniformly resampled to the same resolution and projected to the same coordinate system, so that the pixel grids of the impervious layer classification data of different resolutions are completely matched.

[0046] S2, if the same pixel grid position of different resolution impervious layer classification data, the classification of the same pixel grid is determined as impervious layer, otherwise the pixel is non-impervious layer; record the total number of N total impervious layer pixel grids determined as impervious layer in the target area is N agree , the total number of pixels in the target area is N agree , and the total number of pixels in the target area is N total Calculate the consistency index CI of multi-source data, and correct the impervious layer classification result of the target area through the consistency index CI of multi-source data of the target area.

[0047] The consistency of the multi-source impervious layer classification result of each pixel is analyzed, and the consistency index (Consistency Index, CI) is calculated:

[0048]

[0049] Where, N total is the total number of pixel grids in the target area, N agree is the total number of pixels in the target area, and N total is the total number of pixels determined as impervious layer in the target area.

[0050] When the identification results of multiple data sources are consistent, it indicates that the area is an accurate ground classification; when there are inconsistent classifications of multiple data sources, the result represented by the majority data source is used as the standard; when there are large differences in the results, the classification results are superimposed on the Google image for visual comparison to determine the final impervious layer classification.

[0051] When the consistency index CI of multi-source data is greater than or equal to 0.8, it indicates that the impervious layer classification result of the area is highly consistent, and the classification result is directly used as the best impervious layer data.

[0052] When the consistency index CI of multi-source data is 0.5 to 0.8, the weighted voting method is used to determine the best impervious layer classification:

[0053]

[0054] Where, P best is the best impervious layer classification result, k is impervious layer or non-impervious layer, n is the impervious layer classification data of different resolution, w i is the weight of the i-th data source, and δ(P i , k) is an indicator function, which takes the value of 1 when the classification result of the i-th data source is k, otherwise 0.

[0055] For areas with significant discrepancies (such as CI < 0.5), the classification results are overlaid onto high-resolution imagery (such as Google Imagery) for visual comparison, and the classification results are manually corrected to ensure the accuracy of the impermeable layer data.

[0056] S3. Based on the classification results of the impermeable layer in the target area after calibration, count the number of impermeable layer pixels N in the target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the proportion P of the impermeable layer within a unit cell grid. b Using the nighttime light remote sensing brightness value NTL and the proportion of impermeable layer P within a unit pixel grid. b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

[0057] Based on the integration of multiple impermeable layer data, the proportion P of the impermeable layer within a 500m grid cell is calculated. The specific calculation formula is as follows:

[0058]

[0059] Where, N b N represents the number of impermeable layer pixels within a single pixel. n This refers to the number of pixels in non-impermeable layers (such as water bodies, urban green spaces, wastelands, etc.).

[0060] The nighttime light remote sensing data is corrected based on the impermeable layer ratio, and the original nighttime light remote sensing brightness value NTL for the corresponding grid is extracted. A correction model is constructed using the product of the impermeable layer ratio P and NTL to obtain the corrected nighttime light remote sensing value. The calculation formula is as follows:

[0061] NTL ENH =BTL*P b ;

[0062] Among them, P b NTL represents the proportion of impermeable layer per unit pixel, and NTL represents the nighttime light remote sensing brightness value. ENH This is the corrected nighttime light remote sensing value.

[0063] Based on the above formula, for low impermeable layer areas such as water bodies and vegetation in urban areas, spatial autocorrelation effect correction of nighttime light remote sensing data can be achieved by reducing the interference value of light overflow from adjacent high-brightness areas.

[0064] To verify the effectiveness of the above methods, Zhejiang Province will be used as an example for verification and explanation. Figure 2 The results of extracting impermeable surfaces in Zhejiang Province at 500m resolution and their relationship with the nighttime light remote sensing brightness value NTL are presented. Figure 2(a) directly reflects the spatial distribution of impervious surface, mainly concentrated in the main urban area and central, southeastern region of Hangzhou, the distribution characteristics and Figure 1 The NTL in (b) is basically consistent with the impervious surface density data in (a). The impervious surface density data proposed in this study not only depicts the spatial range of impervious surface, but also quantifies its density level. The area with pixel value close to 1 represents a high proportion of impervious surface coverage, while the value in the suburban area is relatively low. Figure 2 (b) is the extraction result of NDBI (Normalized Difference Built-up Index) index. Although the overall distribution is similar to Figure 2 (a), the NDBI value distribution in the impervious area is more discrete, which is less intuitive than the method proposed in this study.

[0065] Figure 2 (c) shows the correlation between the impervious surface proportion index Pb and NTL, with a correlation coefficient of 0.74, indicating that the night light intensity is mainly derived from the impervious surface coverage area. However, there are still some spatial mismatches locally, such as some high NTL areas with almost no impervious surface, and some high-density impervious surface areas with low night light values. In addition, Figure 2 (d) shows that the correlation between NDBI and NTL is weak, only 0.37, indicating that it is not accurate to reflect the distribution and density of impervious surface. Therefore, it can be concluded that the Pb index can be better applied to night light correction.

[0066] Figure 3 (a), (b), (c), (d) respectively show the spatial distribution relationship between the night light intensity NTL and the impervious surface proportion Pb and the clustering results in the main urban area of Hangzhou. Among them Figure 3 (d) shows that the NTL value is generally positively correlated with the impervious surface proportion. When the pixel corresponds to impervious surface, the NTL value increases significantly, and vice versa, forming a high night light-high construction land clustering pattern. However, there are some anomalies in some water areas, although the Pb value is low, the NTL value is high, showing a high night light-low construction land clustering phenomenon. This situation is mainly affected by the night light "spillover effect", that is, the high-intensity night light in the urban area spreads to the adjacent water area, resulting in a virtual high NTL value in the water area. Therefore, it is necessary to correct the night light spillover effect based on the Pb index to improve the expression accuracy of night light data on the distribution characteristics of impervious surface.

[0067] According to the segmentation method of Figure 4 , the data processing of Figure 5 is carried out, Figure 5 shows the comparison results of night light remote sensing brightness value NTL before and after correction based on Pb index. Figure 5 (a) is the original NTL distribution map, which has the problem of virtual high night light intensity in water areas such as Qiantang River. Figure 5(b) shows the NTL results after correction based on the Pb index, which significantly corrects the nightlight spillover phenomenon in the Qiantang River area and more accurately reflects the true nightlight distribution.

[0068] By integrating multiple impermeable layer data sources, the optimal impermeable layer classification is determined, significantly improving the accuracy of impermeable layer data and providing a reliable foundation for subsequent correction. The correction model constructed based on the proportion of impermeable layers has physical significance and can effectively reduce light spillover interference in low impermeable layer areas, improve the spatial autocorrelation effect correction effect of nighttime light remote sensing data, and the corrected nighttime light remote sensing data can more accurately reflect the boundaries of urban built-up areas, providing high-precision data support for urban expansion monitoring, light pollution assessment, and socio-economic activity analysis. Through automated calculation and correction processes, manual intervention is reduced, data processing efficiency is improved, and it is suitable for large-scale urban environmental monitoring applications.

[0069] Based on the same inventive concept, the present invention also provides a nighttime light remote sensing correction system, comprising:

[0070] The data processing module is used to divide the surface space of the target area into a uniform resolution pixel grid and count the total number N of the pixel grids within the target area. total Extract N total The nighttime light remote sensing brightness value (NTL) corresponding to each pixel grid is obtained; impermeable layer classification data of different resolutions in the target area are obtained, and the impermeable layer classification data of different resolutions are mapped to the same geographic coordinates and aligned with the center of the pixel grid one by one.

[0071] The data classification module is used to classify impermeable layer data of different resolutions at the same pixel grid location as impermeable layers. If all classifications for the same pixel classify it as an impermeable layer, then the pixel is determined to be an impermeable layer; otherwise, the pixel is a non-impermeable layer. Let N be the target area. total The total number of pixels identified as impermeable layers is N. agree , by N agree With N total Calculate the multi-source data consistency index (CI), and use the multi-source data consistency index (CI) of the target area to calibrate the classification results of the impermeable layer in the target area.

[0072] The correction module is used to count the number N of impermeable layer pixels in the target area based on the classification results of the impermeable layer in the calibrated target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel b, and a correction model is constructed to correct the brightness values of the noctilucent remote sensing.

[0073] The application further provides a computer device, which comprises a processor, an internal bus, a network interface, a memory and a nonvolatile memory at a hardware level, and can further comprise other hardware required by a business. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs to implement the noctilucent remote sensing correction method provided above.

[0074] The application further provides a computer readable storage medium, which stores a computer program, and the computer program can be used to execute the noctilucent remote sensing correction method provided above.

[0075] The specific limitations of the noctilucent remote sensing correction system can be seen from the limitations of the noctilucent remote sensing correction method provided above, and will not be repeated here. The modules in the noctilucent remote sensing correction system can be realized by software, hardware and combinations thereof in whole or in part. The modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0076] It should be noted that the above specific embodiments can enable those skilled in the art to have a more comprehensive understanding of the present application, but do not limit the present application in any way. Therefore, although the present application has been described in detail in the specification and examples, those skilled in the art should understand that the present application can still be modified or replaced by equivalents; all technical solutions and improvements that do not deviate from the spirit and scope of the present application are covered in the protection scope of the present application. Any reference signs in the claims should not be considered as limiting the claims.

Claims

1. A luminescent remote sensing data correction method, characterized in that, The method comprises: Divide the target area ground surface space into a uniform resolution pixel grid, count the total number N of pixel grids in the target area total ; extract the noctilucent remote sensing brightness value NTL corresponding to each pixel grid in N total ; acquiring impervious layer classification data of different resolutions of a target area, respectively mapping the impervious layer classification data of different resolutions to the same geographic coordinates, and aligning with the center of the pixel grid one by one; if the impervious layer classification data of different resolutions at the same pixel grid position are all determined as impervious layer for the same pixel, the pixel is determined as impervious layer, otherwise the pixel is non-impervious layer; The total number of pixels in the target region is N total The total number of pixels in the target region is N agree The total number of pixels in the target region is N agree The total number of pixels in the target region is N total An index of consistency CI of multi-source data is calculated, and the impervious layer classification result of the target region is corrected through the index of consistency CI of multi-source data of the target region. Based on the classification results of the impermeable layer in the target area after calibration, the number N of impermeable layer pixels in the target area is counted. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b ; The night light remote sensing brightness value NTL and the impervious layer proportion P of a unit pixel are used b A correction model is constructed to correct the night light remote sensing brightness value.

2. The luminescent remote sensing data correction method of claim 1, wherein, The N agree With N total The multi-source data consistency index CI is calculated, and the expression includes: Wherein, N total is the total number of pixel grids in the target area, N agree is the total number of pixel grids in the target area, N total is the total number of pixel grids in the target area determined as impermeable layer.

3. The luminescent remote sensing data correction method of claim 1, wherein, the correction of the impervious layer classification result of the target area through the consistency index CI of multi-source data of the target area comprises: correction through weighted voting and visual comparison verification.

4. The luminescent remote sensing data correction method of claim 3, wherein, the correction through weighted voting and visual comparison verification comprises: if the consistency index CI of multi-source data of the target area is in the range of 0.5≤CI<0.8, the classification result is determined through weighted voting, and the formula is as follows: where P best is the optimal impervious surface classification result, k is impervious surface or non-impervious surface, n is the impervious surface classification data of different resolutions, w i is the weight of the i-th data source, δ(P i , k) is an indicator function that takes the value 1 when the classification result of the i-th data source is k, and 0 otherwise. if the consistency index CI of multi-source data of the target area is CI<0.5, the classification result is superimposed on the high-resolution image to manually correct the classification result.

5. The luminescent remote sensing data correction method of claim 1, wherein, The use of N b and N n to calculate the impervious layer proportion P b of a unit pixel, the expression including: wherein P b is the proportion of impervious layer within the unit pixel grid, N b is the number of impervious layer pixel grids within the target area, N n is the number of non-impervious layer pixel grids within the target area.

6. The luminescent remote sensing data correction method of claim 1, wherein, the expression of the correction model is as follows: NTL ENH = NTL * P b ; Wherein, P b is the proportion of impermeable layer within a unit pixel, NTL is the night light remote sensing brightness value, NTL ENH is the corrected night light remote sensing value.

7. A nighttime light remote sensing data correction system, characterized in that, comprises: A data processing module is configured to divide the ground surface space of the target area into a grid of pixels with a uniform resolution, and count the total number N of the grid of pixels in the target area total ; extract the noctilucent remote sensing brightness value NTL corresponding to each grid of pixels in N total ; obtain impervious layer classification data of different resolutions for the target area, map the impervious layer classification data of different resolutions to the same geographic coordinates, and align the center of each grid of pixels one by one; The data classification module is used for classifying data of impervious layers of different resolutions at the same grid position of pixels. When the classification of the same pixel is determined as impervious layer, the pixel is determined as impervious layer, otherwise, the pixel is non-impervious layer. The total number of pixels determined as impervious layer in the target region is N total The total number of pixels determined as impervious layer in the target region is N agree The total number of pixels determined as impervious layer in the target region is N agree The total number of pixels determined as impervious layer in the target region is N total The consistency index CI of multi-source data is calculated, and the consistency index CI of multi-source data of the target region is used to correct the classification result of the impervious layer of the target region. The correction module is used to count the number N of impermeable layer pixels in the target area based on the classification results of the impermeable layer in the calibrated target area. b Number of pixels N in impermeable layer n Using N b and N n Calculate the impermeable layer ratio P per unit pixel b Using the nighttime light remote sensing brightness value NTL and the impermeable layer ratio P per unit pixel b A calibration model was constructed to correct the brightness values ​​of nighttime light remote sensing.

8. A computer device, wherein a memory is stored with a computer program, and the computer device comprises a processor, wherein the computer device is configured to execute the computer program to perform the method according to any one of claims 1-7. when the processor executes the computer program, the method steps of any one of claims 1 to 6 are realized.

9. A computer readable storage medium storing a computer program, characterized in that, when the computer program is executed by the processor, the steps of the method as claimed in any one of claims 1 to 6 are realized.

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