A scene-based adaptive non-uniformity correction method for push-broom hyperspectral images

Abstract

The present invention discloses a scene-based push-broom hyperspectral image adaptive non-uniformity correction method, which automatically sorts each scan line of a band of a hyperspectral image from small to large according to the DN values ​​of different spatial dimension pixels, and adaptively selects at least one dark uniform area and bright uniform area of ​​the spatial dimension to obtain the correction coefficient of each spatial dimension pixel, thereby completing the non-uniformity correction of each spatial dimension; based on the spectral dimension characteristics, the spectral dimension non-uniformity correction is completed based on the non-uniformity correction result of the spatial dimension, and the spatial dimension non-uniformity correction is repeated based on the non-uniformity correction result of the spectral dimension, thereby completing the non-uniformity correction of the hyperspectral image. This method is fully automated and can break through the limitations of the scene. It uses spatial and spectral joint information to perform non-uniform correction, retaining the spatial structure information and spectral information of the hyperspectral image to the greatest extent, effectively improving the quality of the hyperspectral image, and is suitable for adaptive non-uniform correction of massive hyperspectral images in various complex scenes, with good versatility.

Description

Technical Field

[0001] The present invention relates to the technical field of hyperspectral remote sensing imaging, and in particular to a scene-based push-broom hyperspectral image adaptive non-uniformity correction method. Background Art

[0002] Hyperspectral imagers can simultaneously collect geometric, radiometric, and spectral information about a target, forming an image cube. While imaging a target, they acquire hundreds or even thousands of continuous and precise radiation intensity data at different wavelengths across a wide spectral range, forming a "fingerprint" of the target's spectral characteristic curve, enabling effective target identification. Consequently, these instruments have found widespread application in energy resource exploration, environmental disaster monitoring, agricultural and forestry remote sensing, greenhouse gas monitoring, urban surveys, and other areas, possessing significant scientific significance and application value. Existing hyperspectral imagers are primarily classified into three imaging modes: swing-broom imaging, push-broom imaging, and staring imaging. Currently, most spaceborne hyperspectral imagers, such as Hyperion, DESIS, AHSI, and EnMAP, employ push-broom scanning. Take the imaging process of the AHSI payload on the Gaofen-5 (GF-5) hyperspectral satellite as an example: The push-broom hyperspectral camera acquires images using an area array detector. One dimension of the area array detector is spatial (number of array elements is W), which images objects in the through-track direction; the other dimension is spectral (number of array elements is C), which corresponds to the spectral information of a row of objects in the through-track direction. As the satellite flies H rows along its orbit, the acquired hyperspectral image is a three-dimensional cube with dimensions H × W × C.

[0003] However, since the number of detector pixels reaches millions, it is difficult to achieve completely consistent photoelectric response for each pixel. Especially after the wide spectral range band is subdivided, the response non-uniformity by wavelength will be more significant. This non-uniformity is specifically manifested as a stripe distribution along the push-sweep direction in push-broom hyperspectral images, which has a great impact on the extraction and quantitative application of hyperspectral features of ground objects. Non-uniformity correction has become a major challenging task for remote sensing image data, especially three-dimensional cube hyperspectral image data.

[0004] Commonly used non-uniformity correction methods in existing technologies can be roughly divided into two categories: one is calibration-based methods, which can achieve high accuracy. However, due to the changes in the satellite's state during continuous operation, regular onboard calibration coefficient adjustments are required. The other is scene-based correction methods, which directly update the non-uniformity correction coefficients based on the scene. This method often requires certain assumptions about the image, and if the scene meets the assumptions, the correction effect is good. Scene-based methods are easier to implement because they do not require special hardware design, but their correction effect is heavily dependent on the method's applicability to the scene.

[0005] Traditional scene-based hyperspectral image non-uniformity correction methods mainly use the two-point method. For example, the paper "Research on Two-point Multi-segment Non-uniformity Correction Algorithm of Infrared Focal Plane Devices" published by Liu Yinnian, Wang Yueming and others proposed a two-point multi-segment non-uniformity correction method. However, this method requires manual interpretation to find uniform bright and dark areas in the image, and this area needs to cover the pixels of the entire spatial dimension (or select several small uniform areas to cover the entire spatial dimension). It is not applicable to complex scenes with many ground details and small and scattered uniform areas. In addition, patent ZL201610913590.6, "A Scene-Based Adaptive Image Non-Uniformity Correction Method," proposes automatically sorting the DN values ​​of an image by different pixels, determining at least one relatively dark uniform area and bright uniform area, and calculating the correction coefficient for each pixel according to the algorithm to complete the image non-uniformity correction. This method can adaptively correct the non-uniformity of a large number of images in various complex terrain scenes, and can also solve the problem of inconsistent uniformity characteristics of each spectral segment of the image, effectively improving the imaging quality and clarity of the image. However, although this method can adaptively correct the non-uniformity of a large number of images in various complex terrain scenes during application, it requires manual debugging of preset parameters before application, making it difficult to achieve true automation and adaptive correction. In addition, this method mainly completes non-uniformity correction based on the spatial information of hyperspectral images, and does not fully consider the distortion correction of the spectral information of hyperspectral images. Summary of the Invention

[0006] In response to the above problems, the present invention proposes a scene-based push-broom hyperspectral image adaptive non-uniformity correction method. This method does not require manual debugging of preset parameters and is fully automated. It can break through the limitations of the scene and use spatial spectral joint information for non-uniform correction. It can retain the spatial structure information and spectral information of the hyperspectral image to the greatest extent, effectively improve the quality of the hyperspectral image, and is suitable for adaptive non-uniform correction of massive hyperspectral images in complex scenes. It has good versatility.

[0007] For this reason, the present invention adopts the following technical solutions:

[0008] A scene-based push-broom hyperspectral image adaptive non-uniformity correction method is proposed. Each scan line of a band of a hyperspectral image is automatically sorted from small to large according to the DN values ​​of pixels in different spatial dimensions, and at least one dark uniform area and bright uniform area in the spatial dimension are adaptively selected to obtain a correction coefficient for each spatial dimension pixel, thereby completing the non-uniformity correction of each spatial dimension. Based on the spectral dimension characteristics, the spectral dimension non-uniformity correction is completed based on the non-uniformity correction result of the spatial dimension. Based on the spectral dimension non-uniformity correction result, the spatial dimension non-uniformity correction is repeated to complete the hyperspectral image non-uniformity correction. The method specifically includes the following steps:

[0009] 1) Select a hyperspectral image with a size of H×W×C where H is the row, W is the column, and C is the number of bands. Automatically sort each scan line of the kth band of the hyperspectral image according to the DN value of the pixels in different spatial dimensions from small to large, and calculate the average of each scan line. By the mean Derivative curve The derivative curve of the uniform DN value area of ​​the spatial pixel is obtained The maximum value of the median frequency corresponds to the row number L of the uniform area, where 1≤L≤H;

[0010] 2) Select the area L on the area with uniform DN value of spatial pixel 上 Behavior dark uniform area (L 上 ×W) k , lower area L 下 Behavior bright uniform area (L 下 ×W) k , where 1≤k≤C, 1≤L 上 ≤H,1≤L 下 ≤H; calculate the dark uniform area of ​​the kth band (L 上 ×W) k The mean pixel DN value P 暗 (k) and bright uniform area (L 下 ×W) k The mean pixel DN value P 亮 (k); then calculate the dark uniform area (L 上 ×W) k The mean DN value of the pixel in the jth column is Q 暗 (j,k) and bright uniform area (L 下 ×W) k The mean DN value of the pixel in the jth column is Q 亮 (j,k), where 1≤j≤W;

[0011] 3) Construct the following equation to obtain the non-uniformity correction coefficients a(j,k) and b(j,k) for the pixel in the jth column of the kth band:

[0012]

[0013] 4) Perform non-uniform correction on the original response value DN(j,k) of the j-th column pixel in the k-th band of the push-broom hyperspectral image to obtain the corrected response value, completing the non-uniform correction of the j-th column pixel in the k-th band of the image spatial dimension:

[0014] DN_spa(j,k)=a(j,k)×DN(j,k)+b(j,k);

[0015] 5) Follow the above steps to complete the non-uniform correction of the original response value DN(i, j, k) of all bands of each pixel in each spatial dimension, and obtain the corrected response value DN of all bands of each pixel in each spatial dimension. _spa The mean x of (i,j,k) mean_spa , calculate the original response mean x of all band spatial dimensions of each pixel mean with x mean_spa The ratio between them is used to obtain the non-uniform correction response value DN of the spectral dimension of the hyperspectral image. _spe (i,j,k):

[0016] DN _spe (i,j,k)=DN _spa (i,j,k)×ratio

[0017] Where i is the i-th row of the selected hyperspectral image, 1≤i≤H;

[0018] 6) Response value DN after spectral dimension correction _spe (i, j, k), repeat the above steps 1) to 4) to complete the hyperspectral image non-uniformity correction.

[0019] Furthermore, the mean in step 1) Derivative curve The equation is as follows:

[0020]

[0021] Furthermore, in step 2), the dark uniform area (L 上 ×W) k The mean pixel DN value P 暗 (k):

[0022]

[0023] Bright uniform area (L 下 ×W) k The mean pixel DN value P 亮 (k):

[0024]

[0025] Dark uniform area (L 上 ×W) k The mean DN value of the pixel in the jth column is Q 暗 (j,k):

[0026]

[0027] Bright uniform area (L 下 ×W) kThe mean DN value of the pixel in the jth column is Q 亮 (j,k):

[0028]

[0029] Where m is L 上 The specific row number, 1≤m≤L 上 ; n is L 下 The specific row number, 1≤n≤L 下 .

[0030] Furthermore, the response value DN after correction of all bands of each pixel in each spatial dimension in step 5) is _spa The mean x of (i,j,k) mean_spa :

[0031]

[0032] The original response mean x of all band spatial dimensions of each pixel mean :

[0033]

[0034] The original response mean x of all band spatial dimensions of each pixel mean The response value DN after correction of all bands of each pixel in each spatial dimension _spa The mean x of (i,j,k) mean_spa The ratio between:

[0035]

[0036] The present invention has the following advantages:

[0037] 1) No need to manually debug preset parameters, fully automated application can break through scene limitations, adaptively select uniform areas, and is suitable for adaptive non-uniform correction of massive hyperspectral images in complex scenes, avoiding the instability of processing results and time consumption problems. It has good versatility and universality, and greatly simplifies the complexity and cost of correction;

[0038] 2) Aiming at the inherent characteristics of the push-broom hyperspectral imager, a joint spatial-spectral non-uniformity correction is performed based on its spatial and spectral characteristics, which can maximize the retention of the spatial structure information and spectral information of the hyperspectral image, effectively improve the quality of the hyperspectral image, and lay the foundation for subsequent image analysis and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The figure is a flow chart of the scene-based push-broom hyperspectral image adaptive non-uniformity correction method in the present invention.

[0040] Figure 2 This is a schematic diagram of selecting dark uniform areas and bright uniform areas in the scene-based push-broom hyperspectral image adaptive non-uniform correction method of the present invention.

[0041] Where (a) is the original image; (b) sort the DN of each column in ascending order; (c) calculate the average of each row in (b); (d) take the derivative of the curve in (c); (e) draw a histogram of the curve in (d); (f) select the uniform area result (g) select the dark uniform area and the bright uniform area.

[0042] Figure 3 Schematic diagram showing abnormal values ​​of push-broom hyperspectral images in a specific embodiment of the present invention.

[0043] Where (a) is the original image; (b) sorts the DN of each column in ascending order; (c) draws the DN curve of each column in Figure (b).

[0044] Figure 4 is the original image of a hyperspectral image.

[0045] Figure 5 To use the method of the present invention Figure 4 The result obtained after non-uniformity correction of the original image in .

[0046] Figure 6 for Figure 4 and Figure 5 Spectral curve comparison chart. DETAILED DESCRIPTION

[0047] In order to make the purpose, features and advantages of the present invention clearer, a specific embodiment of the present invention is described in more detail. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from the description. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] The present invention provides a scene-based push-broom hyperspectral image adaptive non-uniformity correction method, which automatically sorts each scanning line of a band of a hyperspectral image from small to large according to the DN values ​​of pixels in different spatial dimensions, and adaptively selects at least one dark uniform area and bright uniform area in the spatial dimension to obtain a correction coefficient for each spatial dimension pixel, thereby completing the non-uniformity correction of each spatial dimension; based on the spectral dimension characteristics, the spectral dimension non-uniformity correction is completed based on the non-uniformity correction result of the spatial dimension, and the spatial dimension non-uniformity correction is repeated based on the spectral dimension non-uniformity correction result, thereby completing the non-uniformity correction of the hyperspectral image.

[0049] Specifically, an image with a size of 2688×2048×180 acquired by the visible shortwave infrared hyperspectral camera of the Gaofen-5 satellite is taken as an example to elaborate on the specific implementation of the scene-based push-broom hyperspectral image adaptive non-uniformity correction method of the present invention.

[0050] Among them, Figure 3 As shown in the figure, the hyperspectral image with the original size of 2688×2048×180 needs to be processed with outliers first. The outlier processing includes the following steps: k ( 1≤k≤180 ) bands, arrange the pixels in the scan lines from smallest to largest, with the smallest DN value in that column displayed at the top of each line and the largest DN value at the bottom. Dozens of rows with high or low DN values ​​correspond to a group of features with similar reflectivity. If the DN value of a pixel barely changes along the scan line, verify that the data matches the actual features. Even if the data may contain constant DN values ​​due to runways or other features, such completely consistent values ​​are still not suitable for effective calibration and correction. Instead, interpolate the values ​​of surrounding pixels to replace such pixels.

[0051] The scene-based push-broom hyperspectral image adaptive non-uniformity correction method specifically includes the following implementation steps:

[0052] 1) Select a hyperspectral image with a size of 2688×2048×180 after outlier processing, where 2688 is the row, 2048 is the column, and 180 is the number of bands. Automatically sort each scan line of the kth band of the hyperspectral image according to the DN value of the pixel in different spatial dimensions from small to large, and calculate the average of each scan line. By the mean Derivative curve The derivative curve of the uniform DN value area of ​​the spatial pixel is obtained The maximum value of the median frequency corresponds to the row number L of the uniform area, where 1≤L≤2688;

[0053] Among them, the mean Derivative curve The equation is as follows:

[0054]

[0055] 2) If Figure 2 As shown, select the area L on the area with uniform DN value of spatial dimension pixel 上 Behavior dark uniform area (L 上 ×2048) k , lower area L 下 Behavior bright uniform area (L 下 ×2048) k , where 1≤k≤180, 1≤L上 ≤2688, 1≤L 下 ≤2688, take L 上 =50,L 下 =50; calculate the dark uniform area of ​​the kth band (50×2048) k The mean pixel DN value P 暗 (k) and bright uniform area (50×2048) k The mean pixel DN value P 亮 (k); then calculate the dark uniform area (50×2048) k The mean DN value of the pixel in the jth column is Q 暗 (j,k) and bright uniform area (50×2048) k The mean DN value of the pixel in the jth column is Q 亮 (j,k), where 1≤j≤2048;

[0056] Among them, the dark uniform area of ​​the kth band (50×2048) k The mean pixel DN value P 暗 (k):

[0057]

[0058] Bright uniform area (50×2048) k The mean pixel DN value P 亮 (k):

[0059]

[0060] Dark uniform area (50×2048) k The mean DN value of the pixel in the jth column is Q 暗 (j,k):

[0061]

[0062] Bright uniform area (50×2048) k The mean DN value of the pixel in the jth column is Q 亮 (j,k):

[0063]

[0064] Where m is L 上 The specific row number, 1≤m≤L 上 ; n is L 下 The specific row number, 1≤n≤L 下 .

[0065] 3) Construct the following equation to obtain the non-uniformity correction coefficients a(j,k) and b(j,k) for the pixel in the jth column of the kth band:

[0066]

[0067] 4) Perform non-uniform correction on the original response value DN(j,k) of the j-th column pixel in the k-th band of the push-broom hyperspectral image to obtain the corrected response value, completing the non-uniform correction of the j-th column pixel in the k-th band of the image spatial dimension:

[0068] DN_spa(j,k)=a(j,k)×DN(j,k)+b(j,k);

[0069] 5) Follow the above steps to complete the non-uniform correction of the original response value DN(i, j, k) of all bands of each pixel in each spatial dimension, and obtain the corrected response value DN of all bands of each pixel in each spatial dimension. _spa The mean x of (i,j,k) mean_spa , calculate the original response mean x of all band spatial dimensions of each pixel mean with x mean_spa The ratio between them is used to obtain the non-uniform correction response value DN of the spectral dimension of the hyperspectral image. _spe (i,j,k):

[0070] DN _spe (i,j,k)=DN _spa (i,j,k)×ratio

[0071] Where i is the i-th row of the selected hyperspectral image, 1≤i≤2688;

[0072] Among them, the response value DN after correction of all bands of each pixel in each spatial dimension is _spa The mean x of (i,j,k) mean_spa :

[0073]

[0074] The original response mean x of all band spatial dimensions of each pixel mean :

[0075]

[0076] The original response mean x of all band spatial dimensions of each pixel mean The response value DN after correction of all bands of each pixel in each spatial dimension _spa The mean x of (i,j,k) mean_spa The ratio between:

[0077]

[0078] 6) Response value DN after spectral dimension correction _spe(i, j, k), repeat the above steps 1) to 4) to complete the hyperspectral image non-uniformity correction.

[0079] Figure 1 The figure is a flow chart of the scene-based push-broom hyperspectral image adaptive non-uniformity correction method in the present invention.

[0080] In addition, we selected another uncorrected hyperspectral image (i.e. Figure 4 ), the original image is corrected using the scene-based push-broom hyperspectral image adaptive non-uniformity correction method of the present invention, and the corrected result is shown in FIG. Figure 5 As shown. At the same time, Figure 4 and Figure 5 The spectral curve comparison diagram is as follows Figure 6 By comparison, it can be seen that the non-uniformity of the image has been significantly improved, the stripe noise phenomenon has been eliminated, and the spatial structure information and spectral information of the hyperspectral image have been retained to the greatest extent, greatly improving the quality of the hyperspectral image, and the correction effect is very significant.

[0081] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A scene-based push-broom hyperspectral image adaptive non-uniformity correction method, characterized in that: Each scan line of a band of a hyperspectral image is automatically sorted from small to large according to the DN value of pixels in different spatial dimensions, and at least one dark uniform area and bright uniform area in the spatial dimension are adaptively selected to obtain the correction coefficient of each spatial dimension pixel, thereby completing the non-uniform correction of each spatial dimension; based on the spectral dimension characteristics, the spectral dimension non-uniform correction is completed according to the non-uniform correction result of the spatial dimension, and the spatial dimension non-uniform correction is repeated according to the spectral dimension non-uniform correction result, thereby completing the non-uniform correction of the hyperspectral image, specifically including the following steps: 1) Select a hyperspectral image of size H×W×C where H is the row, W is the column, and C is the number of bands. Automatically sort each scan line of the kth band of the hyperspectral image according to the DN value of the pixel in different spatial dimensions from small to large, and calculate the average of each scan line. By the mean Derivative curve The derivative curve of the uniform DN value area of ​​the spatial pixel is obtained The maximum value of the median frequency corresponds to the row number L of the uniform area, where 1≤L≤H; 2) Select the area L on the area with uniform DN value of spatial pixel 上 Behavior dark uniform area (L 上 ×W) k , lower area L 下 Behavior bright uniform area (L 下 ×W) k , where 1≤k≤C, 1≤L 上 ≤H,1≤L 下 ≤H; calculate the dark uniform area of ​​the kth band (L 上 ×W) k The mean pixel DN value P 暗 (k) and bright uniform area (L 下 ×W) k The mean pixel DN value P 亮 (k); then calculate the dark uniform area (L 上 ×W) k The mean DN value of the pixel in the jth column is Q 暗 (j,k) and bright uniform area (L 下 ×W) k The mean DN value of the pixel in the jth column is Q 亮 (j,k), where 1≤j≤W; 3) Construct the following equation to obtain the non-uniformity correction coefficients a(j,k) and b(j,k) for the pixel in the jth column of the kth band: 4) Perform non-uniform correction on the original response value DN(j,k) of the j-th column pixel in the k-th band of the push-broom hyperspectral image to obtain the corrected response value, completing the non-uniform correction of the j-th column pixel in the k-th band of the image spatial dimension: DN_spa(j,k)=a(j,k)×DN(j,k)+b(j,k); 5) Follow the above steps to complete the non-uniform correction of the original response value DN(i, j, k) of all bands of each pixel in each spatial dimension, and obtain the corrected response value DN of all bands of each pixel in each spatial dimension. _spa The mean x of (i,j,k) mean_spa , and then calculate the original response mean x of all band spatial dimensions of each pixel mean with x mean_spa The ratio between them is used to obtain the non-uniform correction response value DN of the spectral dimension of the hyperspectral image. _spe (i,j,k): DN _spe (i,j,k)=DN _spa (i,j,k)×ratio Where i is the i-th row of the selected hyperspectral image, 1≤i≤H; 6) Response value DN after spectral dimension correction _spe (i, j, k), repeat the above steps 1) to 4) to complete the hyperspectral image non-uniformity correction.

2. The scene-based push-broom hyperspectral image adaptive non-uniformity correction method according to claim 1, characterized in that: The mean value in step 1) Derivative curve The equation is as follows:

3. The scene-based push-broom hyperspectral image adaptive non-uniformity correction method according to claim 1, characterized in that: The dark uniform area (L 上 ×W) k The mean pixel DN value P 暗 (k): Bright uniform area (L 下 ×W) k The mean pixel DN value P 亮 (k): Dark uniform area (L 上 ×W) k The mean DN value of the pixel in the jth column is Q 暗 (j,k): Bright uniform area (L 下 ×W) k The mean DN value of the pixel in the jth column is Q 亮 (j,k): Where m is L 上 The specific row number, 1≤m≤L 上 ; n is L 下 The specific row number, 1≤n≤L 下 .

4. The scene-based push-broom hyperspectral image adaptive non-uniformity correction method according to claim 1, characterized in that: The response value DN after correction of all bands of each pixel in each spatial dimension in step 5) _spa The mean x of (i,j,k) mean_spa : The original response mean x of all band spatial dimensions of each pixel mean : The original response mean x of all band spatial dimensions of each pixel mean The response value DN after correction of all bands of each pixel in each spatial dimension _spa The mean x of (i,j,k) mean_spa The ratio between:

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