Geostationary orbit target detection method based on minimum pixel superposition

Abstract

The invention discloses a geostationary orbit target detection method based on minimum pixel superposition, and solves the problem of how to accurately and quickly detect a geostationary orbit target. Belongs to the field of space target detection. Comprising the following steps: superposing minimum pixel values of each frame of image shot by a telescope in a staring mode at the same pixel position to generate a superposed image; respectively calculating a pixel mean value and a standard deviation of the superposed image, setting a threshold value based on a Pauta criterion, carrying out threshold value segmentation on the superposed image according to the threshold value, and carrying out connected domain marking by using a connected domain marking algorithm to obtain a binary image without outlier noise; searching a target marking a connected domain in the binary image by using a connected domain marking algorithm; and calculating the pixel coordinates and pixel values of the targets by using a Gaussian centroid positioning method to obtain the centroid coordinates of each target so as to obtain the position of the geostationary orbit target. According to the invention, the apparent motion difference between the geostationary orbit target and the fixed star is amplified, and accurate and rapid detection of the geostationary orbit target is realized.

Description

Technical Field

[0001] The invention belongs to the technical field of space target detection and relates to a geostationary orbit target detection method based on minimum pixel superposition. Background Art

[0002] The orbital eccentricity and orbital inclination of the geostationary orbit are both zero, and the motion period of space targets in this orbit is the same as the rotation period of the Earth. Since the sub-satellite point trajectory of targets moving in the geostationary orbit is a point, high-value communication satellites and remote sensing satellites mostly use the geostationary orbit. Therefore, it is necessary to detect and monitor geostationary orbit targets to ensure safe operation.

[0003] At present, the detection of geostationary orbit targets is mainly based on large-field optical telescopes, which are low-cost and high-efficiency, and usually observe in staring mode. However, large-field telescopes have the problem of dim target star images, a large number of stars in the field of view, and serious interference from noise and stray light, which brings great difficulties to detection. Commonly used detection methods include superposition methods. By using multiple images for superposition, the signal of the geostationary orbit target in the superimposed image is enhanced, and the noise will not be accurately added due to random errors, so the signal-to-noise ratio of the target can be significantly improved, thereby improving the detection success rate.

[0004] Although the superposition method can effectively improve the signal-to-noise ratio of the target, a large number of star images in the field of view will form tails after superposition, causing obstruction to the target; in addition, when the image is affected by stray light and presents a non-uniform background, the detection effect after superposition is also poor; in addition, the strip-like star images after superposition need to be removed in order to detect the point-like geostationary orbit targets. Summary of the invention

[0005] In order to solve the technical problem of how to remove the superimposed strip-shaped star images in the image taken by the telescope in the staring mode so as to accurately and quickly detect the point-shaped geostationary orbit targets in the image, the present invention provides a geostationary orbit target detection method based on minimum pixel superposition. The method amplifies the apparent motion difference between the geostationary orbit target and the star through minimum pixel superposition, realizes the detection of the geostationary orbit target, effectively copes with the occlusion of the strip-shaped stars, has a high detection rate and a fast detection speed for images under different conditions, can quickly and accurately detect and locate the geostationary orbit target in the telescope field of view, and ensures the safe operation of the geostationary orbit satellite.

[0006] The purpose of the present invention is specifically achieved through the following technical solutions:

[0007] The present invention discloses a method for detecting geostationary orbit targets based on minimum pixel superposition, the method comprising:

[0008] Step 1: receiving all images taken by the telescope in the staring mode, and superimposing the minimum pixel value at the same pixel position of each frame of the image to generate a superimposed image;

[0009] Step 2: Calculate the pixel mean and standard deviation of the superimposed image respectively, set a threshold value according to the pixel mean and standard deviation of the superimposed image based on the Laida criterion, perform threshold segmentation on the superimposed image according to the threshold value to obtain a segmented image, use a connected domain labeling algorithm to label the connected domain of the segmented image, and obtain a binary image with outlier noise removed;

[0010] Step 3: Use the connected domain labeling algorithm to find the targets in the marked connected domain in the binary image, count the number of targets and number them, and record the pixel coordinates of each target; use the Gaussian centroid positioning method to calculate the pixel coordinates and pixel values ​​of each target to obtain the centroid coordinates of each target, thereby obtaining the positions of all geostationary orbit targets in all images.

[0011] In step 1, the method of superimposing the minimum pixel value at the same pixel position of each frame image to generate a superimposed image includes:

[0012]

[0013] Among them, I min (x, y) is the pixel value of the superimposed image at the same pixel position, N is the total number of images taken by the telescope, and t is the index of the image frame, t is 1 to N; I t (x,y) is the minimum pixel value at the same pixel position in each frame of the image, and (x,y) is the pixel position.

[0014] In step 2, the pixel mean of the superimposed image is calculated as:

[0015]

[0016] Among them, μ is the pixel mean of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

[0017] In step 2, the standard deviation of the superimposed image is calculated as:

[0018]

[0019] Wherein, σ is the standard deviation of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

[0020] In step 2, the method of setting the threshold based on the Laida criterion and the pixel mean and standard deviation of the superimposed image includes:

[0021] threshold = μ + 3σ;

[0022] Among them, threshold is the threshold, μ is the pixel mean of the superimposed image, and σ is the standard deviation of the superimposed image.

[0023] In step 2, the method of performing threshold segmentation on the superimposed image according to the threshold to obtain the segmented image includes:

[0024]

[0025] Among them, I binary (x, y) is the segmented image, I min (x, y) is the pixel value of the superimposed image at the same pixel position, and threshold is the threshold.

[0026] In step 2, a method for marking the connected domain of the segmented image using a connected domain marking algorithm to obtain a binary image with outlier noise removed includes:

[0027] Use the connected domain marking algorithm to traverse all pixels row by row from the upper left corner of the segmented image; if the pixel value of the current pixel is 1, check the eight neighboring pixels of the current pixel. If no domain pixel among the eight neighboring pixels is marked as a connected domain, create a connected domain for the current pixel and use the current pixel as the target;

[0028] If there is a target with a connected domain marked among the eight neighboring pixels, the current pixel with a pixel value of 1 is marked as part of the connected domain of the target;

[0029] After completing the connected domain labeling of all pixels in the segmented image using the connected domain labeling algorithm, the wild value noise with a connected domain area of ​​1 is removed to generate a binary image.

[0030] Among them, the connected domain labeling algorithm is used to identify and mark the connected areas in the image, which is completed through the bwconncomp function in MATLAB.

[0031] In step 3, the pixel coordinates and pixel values ​​of the target are calculated using the Gaussian centroid positioning method. The method for obtaining the centroid coordinates of each target includes:

[0032]

[0033] Among them, (C x ,C y ) is the center of mass coordinate of the target, (x i ,y i ) is the i-th pixel coordinate in the target connected area, p i is the pixel coordinate (x i ,yi ); i is the pixel coordinate number of the target connected area.

[0034] The beneficial effects of the present invention are:

[0035] The present invention adopts a minimum pixel superposition method, which effectively amplifies the apparent motion difference between stars and geostationary orbit targets by superimposing the minimum pixel values ​​of multiple frames of images taken by the telescope in the staring mode at the same pixel position, and can also handle the situation where the stars block the targets. Because the geostationary orbit targets are stationary in the image, and the stars have a moving speed in the image, they show obvious trajectories in the continuous multi-frame images. Through the minimum pixel superposition method, each pixel in the multi-frame image can be screened for the minimum value, thereby retaining the information of the geostationary orbit target, while eliminating the motion trajectory of the star, significantly improving the detection effect of the geostationary orbit target; including:

[0036] 1. Improve the detection rate of geostationary orbit targets:

[0037] Through the minimum pixel superposition method, the apparent motion difference between geostationary orbit targets and stars can be effectively amplified; in multi-frame images, the motion trajectory of the stars is eliminated. Compared with the traditional superposition method, the present invention has a significant reduction in the missed detection rate of target detection, a higher detection rate, and more reliable detection results.

[0038] 2. Avoid star occlusion problems:

[0039] Star occlusion is a common problem in geostationary orbit target detection. When the target is on the star's motion track, the target will be occluded by the traditional stacking method. The minimum pixel stacking method eliminates the star's motion track by selecting the minimum pixel value for stacking, ensuring that the target is clearly visible in the final stacked image.

[0040] 3. Improve the anti-interference ability of abnormal bright spots:

[0041] The traditional superposition method is easily disturbed by abnormal bright spots in some frames, resulting in deviations in target detection results. The minimum pixel superposition method suppresses the influence of abnormal bright spots by screening the pixel values ​​in each frame of the image for the minimum value, thereby improving the algorithm's anti-interference ability. In the presence of abnormal bright spots, the present invention can still stably detect targets.

[0042] 4. Improve detection efficiency:

[0043] The present invention improves detection efficiency by optimizing the algorithm structure while maintaining high accuracy. Compared with the traditional superposition method, the subsequent detection and positioning method of the present invention is simpler and significantly improves detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The present invention is further described in detail below based on the accompanying drawings and embodiments.

[0045] Figure 1 It is an original image frame among all the images taken by the telescope in the staring mode received by the present invention.

[0046] Figure 2 In the present invention, the minimum pixel value at the same pixel position of each frame image is superimposed to generate a superimposed image. DETAILED DESCRIPTION

[0047] An embodiment of the present invention provides a method for detecting geostationary orbit targets based on minimum pixel superposition, the method comprising:

[0048] Step 1: Receive all images taken by the telescope in the staring mode, and superimpose the minimum pixel value of each frame of the image at the same pixel position to generate a superimposed image; through the minimum pixel superposition method disclosed in the present invention, a new superimposed image can be generated, and the superimposed image retains the minimum pixel information in multiple frames of images at each pixel position.

[0049] Step 2: Calculate the pixel mean and standard deviation of the superimposed image respectively, set a threshold value according to the pixel mean and standard deviation of the superimposed image based on the Laida criterion, perform threshold segmentation on the superimposed image according to the threshold value to obtain a segmented image, use a connected domain labeling algorithm to label the connected domain of the segmented image, and obtain a binary image with outlier noise removed;

[0050] Step 3: Use the connected domain labeling algorithm to find the targets in the marked connected domain in the binary image, count the number of targets and number them, and record the pixel coordinates of each target; use the Gaussian centroid positioning method to calculate the pixel coordinates and pixel values ​​of each target to obtain the centroid coordinates of each target, thereby obtaining the positions of all geostationary orbit targets in all images.

[0051] In step 1, the method of superimposing the minimum pixel value at the same pixel position of each frame image to generate a superimposed image includes:

[0052]

[0053] Among them, I min (x, y) is the pixel value of the superimposed image at the same pixel position, N is the total number of images taken by the telescope, and t is the index of the image frame, t is 1 to N; I t (x,y) is the minimum pixel value at the same pixel position in each frame of the image, and (x,y) is the pixel position.

[0054] In step 2, the pixel mean is the sum of all pixel values ​​in the image divided by the total number of pixels. The pixel mean of the superimposed image is calculated as:

[0055]

[0056] Among them, μ is the pixel mean of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

[0057] In step 2, the pixel standard deviation is the square root of the mean of the squares of the differences between all pixel values ​​and the mean in the image. The standard deviation of the superimposed image is calculated as:

[0058]

[0059] Wherein, σ is the standard deviation of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

[0060] In step 2, the method of setting the threshold based on the Laida criterion and the pixel mean and standard deviation of the superimposed image includes:

[0061] threshold = μ + 3σ;

[0062] Among them, threshold is the threshold, μ is the pixel mean of the superimposed image, and σ is the standard deviation of the superimposed image.

[0063] In step 2, the method of performing threshold segmentation on the superimposed image according to the threshold to obtain the segmented image includes:

[0064]

[0065] Among them, I binary (x, y) is the segmented image, I min (x, y) is the pixel value of the superimposed image at the same pixel position, and threshold is the threshold.

[0066] In step 2, a method for marking the connected domain of the segmented image using a connected domain marking algorithm to obtain a binary image with outlier noise removed includes:

[0067] Use the connected domain marking algorithm to traverse all pixels row by row from the upper left corner of the segmented image; if the pixel value of the current pixel is 1, check the eight neighboring pixels of the current pixel. If no domain pixel among the eight neighboring pixels is marked as a connected domain, create a connected domain for the current pixel and use the current pixel as the target;

[0068] If there is a target with a connected domain marked among the eight neighboring pixels, the current pixel with a pixel value of 1 is marked as part of the connected domain of the target;

[0069] After completing the connected domain labeling of all pixels in the segmented image using the connected domain labeling algorithm, the wild value noise with a connected domain area of ​​1 is removed to generate a binary image.

[0070] Among them, the connected domain labeling algorithm is mainly used to identify and mark the connected areas in the image, which is completed through the bwconncomp function in MATLAB.

[0071] In step 3, the pixel coordinates and pixel values ​​of the target are calculated using the Gaussian centroid positioning method: the coordinates of each pixel of the target are multiplied by its corresponding pixel value, and then all weighted coordinates are summed and then divided by the sum of the target pixel values. The method for obtaining the centroid coordinates of each target includes:

[0072]

[0073] Among them, (C x ,C y ) is the center of mass coordinate of the target, (x i ,y i ) is the i-th pixel coordinate in the target connected area, p i is the pixel coordinate (x i ,y i ) pixel value; i is the pixel coordinate number of the target connected area. This method can finally obtain the position of all geostationary orbit targets in the image.

[0074] Verify the instance

[0075] Based on the motion characteristics of geostationary orbit satellites and stars, since geostationary orbit targets are stationary relative to the Earth, when the telescope works in staring mode, they are stationary points in the telescope's field of view. The angular velocity of the star in the telescope's field of view is related to its declination. The specific formula is:

[0076] |ω|=Ω·cosθ

[0077] Among them, Ω is the angular velocity of the earth's rotation, and θ is the declination of the star. That is, the angular velocity of the star in the field of view is determined by the declination of the telescope, and the value range is [0, Ω]. When the telescope points to the north and south celestial poles, the angular velocity of the star in the field of view is 0; when the telescope points to the celestial equator, the angular velocity of the star in the field of view is Ω. The present invention can use the angular velocity ω of the target relative to the telescope and the angular resolution θ of the telescope P The correlation between them can be used to obtain the target's motion speed v in the pixel coordinate system. T size,

[0078]

[0079] Among them, θ Pis the angular resolution of the telescope, which refers to the minimum angle that the telescope can resolve. The calculation formula is as follows:

[0080]

[0081] Among them, Θ FOV represents the field of view of the telescope, and R represents the image resolution. Therefore, the stars appear to be moving on the image.

[0082] Based on the above research, the present invention amplifies the apparent motion difference between geostationary orbit targets and stars by the minimum pixel superposition method to achieve the detection of geostationary orbit targets. The experimental steps are:

[0083] Receive all images taken by the telescope in the staring mode: the telescope works in the staring mode, the exposure time is 2s, the telescope points to a declination of 0°, and the total number of frames N of the sequence image is 29.

[0084] The minimum pixel value at the same pixel position of each frame image is superimposed to generate a superimposed image: perform minimum pixel superposition on all 29 frames of images, one of which is the original image, such as Figure 1 As shown, the generated overlay image is as follows Figure 2 shown.

[0085] The pixel mean and standard deviation of the superimposed image were calculated respectively: the pixel mean μ was 863.2, and the standard deviation σ was 44.5;

[0086] According to the Laida criterion, the threshold is calculated to be 996.7;

[0087] Based on this threshold, the image is segmented, the noise points are removed after binarization, and the number of targets and the pixel position index corresponding to each target are recorded.

[0088] Target detection and positioning: Process the detected targets in turn, extract the pixel value of the corresponding position from the superimposed image according to the pixel position index of the target, calculate the target center of mass according to the Gaussian center of mass positioning method, and complete the positioning of the target. Finally, the number of geostationary orbit target satellites detected is 5, and the center of mass positioning positions are (1552.0, 2146.6), (1598.5, 2236.2), (2208.5, 1960.6), (2899.9, 1776.5), (3477.5, 1582.2).

[0089] The beneficial effects of the embodiments of the present invention are:

[0090] The present invention adopts a minimum pixel superposition method, which effectively amplifies the apparent motion difference between stars and geostationary orbit targets by superimposing the minimum pixel values ​​of multiple frames of images taken by the telescope in the staring mode at the same pixel position, and can also handle the situation where the stars block the targets. Because the geostationary orbit targets are stationary in the image, and the stars have a moving speed in the image, they show obvious trajectories in the continuous multi-frame images. Through the minimum pixel superposition method, each pixel in the multi-frame image can be screened for the minimum value, thereby retaining the information of the geostationary orbit target, while eliminating the motion trajectory of the star, significantly improving the detection effect of the geostationary orbit target; including:

[0091] 1. Improve the detection rate of geostationary orbit targets:

[0092] Through the minimum pixel superposition method, the apparent motion difference between geostationary orbit targets and stars can be effectively amplified; in multi-frame images, the motion trajectory of the stars is eliminated. Compared with the traditional superposition method, the present invention has a significant reduction in the missed detection rate of target detection, a higher detection rate, and more reliable detection results.

[0093] 2. Avoid star occlusion problems:

[0094] Star occlusion is a common problem in geostationary orbit target detection. When the target is on the star's motion track, the target will be occluded by the traditional stacking method. The minimum pixel stacking method eliminates the star's motion track by selecting the minimum pixel value for stacking, ensuring that the target is clearly visible in the final stacked image.

[0095] 3. Improve the anti-interference ability of abnormal bright spots:

[0096] The traditional superposition method is easily disturbed by abnormal bright spots in some frames, resulting in deviations in target detection results. The minimum pixel superposition method suppresses the influence of abnormal bright spots by screening the pixel values ​​in each frame of the image for the minimum value, thereby improving the algorithm's anti-interference ability. In the presence of abnormal bright spots, the present invention can still stably detect targets.

[0097] 4. Improve detection efficiency:

[0098] The present invention improves detection efficiency by optimizing the algorithm structure while maintaining high accuracy. Compared with the traditional superposition method, the subsequent detection and positioning method of the present invention is simpler and significantly improves detection efficiency.

[0099] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting geostationary orbit targets based on minimum pixel superposition, characterized in that: The method includes: Step 1: receiving all images taken by the telescope in the staring mode, and superimposing the minimum pixel value at the same pixel position of each frame of the image to generate a superimposed image; Step 2: Calculate the pixel mean and standard deviation of the superimposed image respectively, set a threshold value according to the pixel mean and standard deviation of the superimposed image based on the Laida criterion, perform threshold segmentation on the superimposed image according to the threshold value to obtain a segmented image, use a connected domain labeling algorithm to label the connected domain of the segmented image, and obtain a binary image with outlier noise removed; Step 3: Use the connected domain labeling algorithm to find the targets in the marked connected domain in the binary image, count the number of targets and number them, and record the pixel coordinates of each target; use the Gaussian centroid positioning method to calculate the pixel coordinates and pixel values ​​of each target to obtain the centroid coordinates of each target, thereby obtaining the positions of all geostationary orbit targets in all images.

2. The method according to claim 1, characterized in that In step 1, the method of superimposing the minimum pixel value at the same pixel position of each frame image to generate a superimposed image includes: Among them, I min (x, y) is the pixel value of the superimposed image at the same pixel position, N is the total number of images taken by the telescope, and t is the index of the image frame, t is 1 to N; I t (x,y) is the minimum pixel value at the same pixel position in each frame of the image, and (x,y) is the pixel position.

3. The method according to claim 2, characterized in that In step 2, the pixel mean of the superimposed image is calculated as: Among them, μ is the pixel mean of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

4. The method according to claim 2, characterized in that In step 2, the standard deviation of the superimposed image is calculated as: Wherein, σ is the standard deviation of the superimposed image, m×n is the size of the superimposed image, m is the maximum number of pixels of the superimposed image in the x-axis direction, and n is the maximum number of pixels of the superimposed image in the y-axis direction.

5. The method according to claim 1, 3 or 4, characterized in that: In step 2, the method of setting the threshold based on the Laida criterion and the pixel mean and standard deviation of the superimposed image includes: threshold = μ + 3σ; Among them, threshold is the threshold, μ is the pixel mean of the superimposed image, and σ is the standard deviation of the superimposed image.

6. The method according to claim 5, characterized in that In step 2, the method of performing threshold segmentation on the superimposed image according to the threshold to obtain the segmented image includes: Among them, I binary (x, y) is the segmented image, I min (x, y) is the pixel value of the superimposed image at the same pixel position, and threshold is the threshold.

7. The method according to claim 6, characterized in that In step 2, a method for marking the connected domain of the segmented image using a connected domain marking algorithm to obtain a binary image with outlier noise removed includes: Use the connected domain marking algorithm to traverse all pixels row by row from the upper left corner of the segmented image; if the pixel value of the current pixel is 1, check the eight neighboring pixels of the current pixel. If no domain pixel among the eight neighboring pixels is marked as a connected domain, create a connected domain for the current pixel and use the current pixel as the target; If there is a target with a connected domain marked among the eight neighboring pixels, the current pixel with a pixel value of 1 is marked as part of the connected domain of the target; After completing the connected domain labeling of all pixels in the segmented image using the connected domain labeling algorithm, the wild value noise with a connected domain area of ​​1 is removed to generate a binary image.

8. The method according to claim 1 or 7, characterized in that The connected domain labeling algorithm is used to identify and label connected regions in an image, and is completed by the bwconncomp function in MATLAB.

9. The method according to claim 1, characterized in that In step 3, the pixel coordinates and pixel values ​​of the target are calculated using the Gaussian centroid positioning method. The method for obtaining the centroid coordinates of each target includes: Among them, (C x ,C y ) is the center of mass coordinate of the target, (x i ,y i ) is the i-th pixel coordinate in the target connected area, p i is the pixel coordinate (x i ,y i ); i is the pixel coordinate number of the target connected area.

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