A multi-slit hyperspectral image data image motion detection correction method

Abstract

The application discloses a kind of multi-slit hyperspectral image data's image shift detection correction method, it has not seen the detection method for the difference of each pixel image shift between multi-slit hyperspectral data in prior art, the problem that the advantage of multi-slit hyperspectral imaging system is restricted and its data high quality application, the image shift amount of each pixel between multi-slit hyperspectral data is regarded as the image shift amount of each image in the application, there is continuity between each pixel image shift amount, the image shift continuity of different longitudinal and transverse directions is used to constrain HS dense optical flow method to realize the detection correction of each pixel image shift amount, and then based on the distortion image shift deviation of multi-slit hyperspectral imaging system, the imaging image shift deviation caused by platform motion is corrected to the optical flow value calculated by HS dense optical flow method, and the abnormal optical flow value is removed, the optical flow value of pixel position that is removed is supplemented by linear difference calculation result, and the final image shift amount of each pixel of multi-slit hyperspectral data is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to multi-slit hyperspectral image data, in particular to a multi-slit hyperspectral image data image motion detection correction method. BACKGROUND

[0002] The multi-slit hyperspectral imaging system obtains the overall image data of the entire ground element by along-track push-broom. Because the multi-slit hyperspectral imaging system adopts multi-slit spectrometry, it can obtain the hyperspectral image data of different column elements at a single time, and then obtain multiple groups of hyperspectral image data of the same ground element at different times through along-track push-broom after different slit spectrometry, so the multi-slit hyperspectral image data has correlation; at the same time, due to the influence of optical distortion, imaging conditions and other factors, there is a small image motion difference between each slit hyperspectral image data, so the multi-slit hyperspectral image data also has difference. As a new emerging hyperspectral imaging technology system, the detection method of the independent pixel motion amount of the multi-slit hyperspectral imaging technology is currently still relatively lacking, which restricts the embodiment of the advantages of the multi-slit hyperspectral imaging system and the high-quality application of the data.

[0003] The image motion detection of multi-slit hyperspectral image data is the basis for supporting the high-quality application of multi-slit hyperspectral data, and is of great significance to the high-precision reconstruction of multi-slit data, the super-resolution reconstruction based on the image motion amount, and the embodiment of the advantages of the multi-slit hyperspectral imaging system. However, at present, there is no detection method for the image motion difference of each pixel between multi-slit hyperspectral data. SUMMARY

[0004] In order to solve the technical problem that there is no detection method for the image motion difference of each pixel between multi-slit hyperspectral data in the prior art, which restricts the embodiment of the advantages of the multi-slit hyperspectral imaging system and the high-quality application of the data, the present application provides a multi-slit hyperspectral image data image motion detection correction method.

[0005] The inventive concept of the present application is:

[0006] Because the image motion amount of each pixel between multi-slit hyperspectral image data can be regarded as the movement amount of each image pixel, the HS dense optical flow method (i.e. Horn-Schunck dense optical flow method) can be used for the detection and correction of independent pixel motion. The HS dense optical flow method is a classical dense optical flow calculation method, which can calculate the optical flow value of each pixel. In the calculation, the smoothing term coefficient factor is used to represent and adjust the smoothness of the optical flow field. The multi-slit hyperspectral imaging system is an along-track push-broom imaging system, and in the push-broom process, there is a great difference in the smoothness of the along-track and vertical-track optical flow fields, but the vertical-track optical flow smoothness of each pixel is better than the along-track optical flow smoothness, so different smoothing term coefficient factors can be used for optical flow calculation in the two directions.

[0007] The pixel shift amount can also be obtained by superimposing the distortion shift deviation of the known multi-slit hyperspectral imaging system and the imaging shift deviation caused by platform movement, and the pixel shift amount is relatively rough, but it can reflect the distribution rule of the pixel shift, and the optical flow calculation result is corrected by using the pixel shift amount, and the abnormal optical flow value is removed, so that a more accurate pixel shift amount is obtained.

[0008] Therefore, the present application regards the pixel shift amount of each pixel between multi-slit hyperspectral data as the pixel shift amount of each image, and the continuity exists between the pixel shift amounts, and the HS dense optical flow method is used to realize the detection and correction of the pixel shift amount in the longitudinal and transverse directions, and then the optical flow value calculated by the HS dense optical flow method is corrected based on the distortion shift deviation of the multi-slit hyperspectral imaging system and the imaging shift deviation caused by platform movement, and the abnormal optical flow value is removed, and the optical flow value of the removed pixel position is supplemented by linear difference calculation result, so that the final pixel shift amount of each pixel of the multi-slit hyperspectral data is obtained.

[0009] In order to achieve the above purpose, the above application concept is completed, and the technical scheme is as follows:

[0010] A pixel shift detection and correction method for multi-slit hyperspectral image data, which is characterized by comprising the following steps:

[0011] Step 1, obtaining a plurality of hyperspectral image data of a multi-slit hyperspectral imaging system for scanning the same target, and pre-processing the hyperspectral image data, setting the hyperspectral image data obtained by one slit as a reference, and recording it as the first hyperspectral image data, and the rest as the second hyperspectral image data;

[0012] Step 2, taking the average of the gray values of each spectral segment in the first hyperspectral image data to determine the spectral segment T1 with the maximum average, and finding the spectral segment T2 corresponding to the spectral segment T1 in each second hyperspectral image data;

[0013] Step 3, using the HS dense optical flow method, setting different smoothing term coefficient factors along the track direction and the vertical track direction, calculating the optical flow value of each pixel in the spectral segment T2 relative to each pixel in the spectral segment T1, and recording all the optical flow values as the first pixel shift array;

[0014] Step 4, superimposing the distortion shift deviation and the imaging shift deviation to obtain the second pixel shift array; the distortion shift deviation is the system deviation of the multi-slit hyperspectral imaging system; the imaging shift deviation is the imaging deviation caused by imaging at different times of scanning in each pixel in the spectral segment T1 and each corresponding pixel in the spectral segment T2;

[0015] Step 5, correcting the first pixel shift array by the second pixel shift array;

[0016] 5.1) calculating the pixel deviation of the first and second image motion array respectively;

[0017] 5.2) comparing each pixel deviation with a preset threshold value respectively:

[0018] if the pixel deviation is less than or equal to the preset threshold value, the corresponding optical flow value is retained;

[0019] if the pixel deviation is greater than the preset threshold value, the corresponding optical flow value is removed, and the remaining and adjacent optical flow values in the front and rear positions in the vertical direction are used for linear interpolation calculation, and the calculation result is used as the optical flow value of the removed pixel position;

[0020] After the correction of the first image motion array is completed, the corrected first image motion array is the image motion detection and correction result of the multi-slit hyperspectral image data.

[0021] Further, in step 3, the vertical direction smoothing term coefficient factor a is set to be 2 times of the along-track direction smoothing term coefficient factor β.

[0022] Further, in step 3, the smoothing term coefficient factor β is set to be (0, 0.5].

[0023] Further, in step 5.2, the preset threshold value is 1-2 times of the average deviation of each corresponding pixel in the first and second image motion arrays.

[0024] Further, in step 5.2, the preset threshold value is 1.5 times of the average deviation of each corresponding pixel in the first and second image motion arrays.

[0025] Further, in step 1, the hyperspectral image data obtained by the slit with the best imaging quality is set as the reference, and is recorded as the first hyperspectral image data.

[0026] Further, in step 1, the preprocessing includes dark current correction, relative radiation correction and bad pixel correction.

[0027] Further, in step 3, the HS dense optical flow method is solved by using the Gauss-Seidel iteration method.

[0028] The beneficial effects of the present application are:

[0029] The present application is directed to the characteristics of multi-slit hyperspectral image data, and provides a multi-slit hyperspectral image data image motion detection correction method by using the HS dense optical flow method and the distortion image motion deviation and the imaging image motion deviation, the method can obtain the high-precision image motion amount of each pixel in the multi-slit hyperspectral image data, and provides technical support for the high-precision reconstruction of multi-slit data, the super-resolution reconstruction based on the image motion amount, and the high-quality application of multi-slit hyperspectral data, and has important significance for embodying the advantages of the multi-slit hyperspectral imaging system. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 is a schematic diagram of a three-slit hyperspectral imaging system push-broom imaging;

[0031] Figure 2 is a flowchart of a multi-slit hyperspectral image data image motion detection correction method. DETAILED DESCRIPTION

[0032] The technical solutions of the present application will be described clearly and completely in combination with the drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0033] A multi-slit hyperspectral image data image motion detection correction method, in combination with Figure 1 and Figure 2 As shown in the figures, in this embodiment, a double-slit hyperspectral imaging system is selected, the spatial dimension is 20x10 along the track direction and perpendicular to the track direction, and the spectral dimension is 5 spectral bands. In other embodiments, a three-slit hyperspectral imaging system or the like can also be selected.

[0034] The method comprises the following steps:

[0035] Step 1, obtaining multiple hyperspectral image data of a multi-slit hyperspectral imaging system for scanning the same target, selecting the hyperspectral image data obtained by one of the slits as a reference, denoted as first hyperspectral image data, and the rest as second hyperspectral image data; independently and sequentially performing the following data preprocessing on the first hyperspectral image data and the second hyperspectral image data: dark current correction, relative radiation correction, and bad pixel correction.

[0036] Among them, the first hyperspectral image data is selected as the hyperspectral image data obtained by one of the slits with the best imaging quality.

[0037] Step 2, taking the average of the gray scale values of each spectral band (5 spectral bands) in the first hyperspectral image data to determine the spectral band T1 with the maximum average value, and finding the spectral band T2 corresponding to the spectral band T1 in each second hyperspectral image data.

[0038] The mean value in this embodiment is the spectrum segment 3, so the corresponding spectrum segment 3 in the second highest spectral image data is found.

[0039] Step 3, using the HS dense optical flow method, and setting different smoothing term coefficient factors in the along-track direction and the cross-track direction, the optical flow values of each pixel in the spectrum segment T2 relative to each pixel in the spectrum segment T1 are calculated, and all the optical flow values are recorded as a first image motion array; each value in the first image motion array corresponds to a corresponding pixel in the spectrum segment T1 and the spectrum segment T2, respectively.

[0040] The HS dense optical flow method uses the Gauss-Seidel iteration method for solving. The optical flow smoothing term coefficient factor of the traditional HS dense optical flow method takes the same value in the along-track direction and the cross-track direction. In this embodiment, due to the particularity of multi-slit data acquisition, the cross-track direction smoothing term coefficient factor a is taken as 2 times of the along-track direction smoothing term coefficient factor b, specifically a = 1, b = 0.5.

[0041] Step 4, the distortion image motion deviation is superimposed with the imaging image motion deviation to obtain a second image motion array.

[0042] Wherein:

[0043] The distortion image motion deviation is the system deviation of the multi-slit hyperspectral imaging system, that is, a known quantity.

[0044] The imaging image motion deviation is the imaging deviation of each pixel in the spectrum segment T1 and each corresponding pixel in the spectrum segment T2 due to imaging at different times of push-broom.

[0045] Therefore, each value in the second image motion array also corresponds to a corresponding pixel in the spectrum segment T1 and the spectrum segment T2, respectively.

[0046] Step 5, the first image motion array is corrected through the second image motion array;

[0047] 5.1) the pixel deviation of the first image motion array and the second image motion array is calculated;

[0048] 5.2) each pixel deviation is compared with a preset threshold value, respectively:

[0049] If the pixel deviation is less than or equal to the preset threshold value, the corresponding optical flow value is retained;

[0050] If the pixel deviation is greater than the preset threshold value, the corresponding optical flow value is removed, and the remaining and adjacent optical flow values in the cross-track direction are used for linear interpolation calculation, and the calculation result is used as the optical flow value of the removed pixel position.

[0051] The preset threshold is 1-2 times, preferably 1.5 times, of the average deviation of each pixel in the first and second image motion arrays.

[0052] After the correction of the first image motion array is completed, the corrected first image motion array is the image motion detection and correction result of the multi-slit hyperspectral image data.

[0053] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any change or replacement within the technical range disclosed by the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for shift detection and correction of multi-slit hyperspectral image data, characterized in that, The method comprises the following steps: Step 1, obtaining multiple hyperspectral image data of the same target pushed by a multi-slit hyperspectral imaging system, and pre-processing the multiple hyperspectral image data, setting the hyperspectral image data obtained by one slit as a reference, and recording the hyperspectral image data as first hyperspectral image data, and recording the rest as second hyperspectral image data; Step 2, taking the average of the gray values of each spectral band in the first hyperspectral image data to determine the spectral band T1 with the maximum average value, and finding the spectral band T2 corresponding to the spectral band T1 in each second hyperspectral image data; Step 3, using the HS dense optical flow method, setting different smoothing term coefficient factors in the along-track direction and the cross-track direction, calculating the optical flow values of each pixel in the spectral band T2 relative to each pixel in the spectral band T1, and recording all the optical flow values as a first image motion array; Step 4, superimposing the distortion image motion deviation and the imaging image motion deviation to obtain a second image motion array; the distortion image motion deviation is the system deviation of the multi-slit hyperspectral imaging system; the imaging image motion deviation is the imaging deviation caused by imaging at different times of the push scan of each pixel in the spectral band T1 and each corresponding pixel in the spectral band T2; Step 5, modifying the first image motion array through the second image motion array; 5.1) calculating the pixel deviation of the first image motion array and the second image motion array; 5.2) comparing each pixel deviation with a preset threshold value respectively: if the pixel deviation is less than or equal to the preset threshold value, the corresponding optical flow value is retained; if the pixel deviation is greater than the preset threshold value, the corresponding optical flow value is removed, and the remaining and adjacent optical flow values in the cross-track direction are used for linear interpolation calculation, and the calculation result is used as the optical flow value of the removed pixel position; after the modification of the first image motion array is completed, the modified first image motion array is the image motion detection and correction result of the multi-slit hyperspectral image data.

2. The method of slits misregistration correction for multi-slit hyperspectral image data according to claim 1, characterized in that: In step 3, the cross-track direction smoothing term coefficient factor α is set to be 2 times the along-track direction smoothing term coefficient factor β.

3. The method of slits misregistration correction for multi-slit hyperspectral image data according to claim 2, wherein: In step 3, the smoothing term coefficient factor β is set to be (0, 0.5].

4. The method of shift detection correction of multi-slit hyperspectral image data according to claim 2 or 3, characterized in that: In step 5.2, the preset threshold value is 1-2 times the average deviation of each corresponding pixel in the first image motion array and the second image motion array.

5. The method of slits misregistration correction for multi-slit hyperspectral image data according to claim 4, wherein: In step 5.2, the preset threshold value is 1.5 times the average deviation of each corresponding pixel in the first image motion array and the second image motion array.

6. The method of slits image motion correction for multi-slit hyperspectral image data according to claim 5, wherein: In step 1, the hyperspectral image data obtained by one slit with the best imaging quality is set as the reference, and recorded as the first hyperspectral image data.

7. The method of slits misregistration correction for multi-slit hyperspectral image data according to claim 1, wherein: In step 1, the pre-processing includes dark current correction, relative radiation correction and bad pixel correction.

8. The method of slits image motion correction for multi-slit hyperspectral image data according to claim 7, characterized in that: In step 3, the HS dense optical flow method uses the Gauss-Seidel iteration method for solution.

Images