Multiband limb detection method for dark space target

Abstract

The invention belongs to the technical field of space target detection, and discloses a multi-band limb detection method for a dark space target, which comprises the following steps: S101, imaging a limb or a deep space region by using a multi-band infrared detection system for space target detection; s102, initializing system integral time according to an included angle between the detection system and the sun and the earth; s103, acquiring image data acquired by the multiband infrared detection system; s104, performing target detection on the multiband image; s105, performing registration transformation on a multi-band detection result; s106, associating and matching the target after multi-band registration, and outputting a target feature information calculation result; and S107, optimizing the integral time of the detection system according to the output target information, and continuously observing the target. The method solves the problems that an existing single-band detection system is limited in target information obtaining, weak in anti-interference capacity and weak in detection capacity for dark and weak targets.

Description

Technical Field

[0001] The present invention relates to the technical field of space target detection, and in particular to a multi-band limb detection method for dark and weak space targets. Background Art

[0002] In the space target observation scenario, the target detection distance is long, and the target radiation energy that the detector can receive is weak. At the same time, due to interference factors such as background fluctuations and cloud obstruction, the signal strength received by the detector varies greatly. Therefore, the observation of dark and weak space targets based on space requires extremely high sensitivity of the detection system.

[0003] In recent years, infrared long-wave detectors with low dark current levels in low-temperature environments have played a vital role in detecting and tracking low-temperature, long-distance moving targets due to their high sensitivity, high detection rate, and strong anti-interference ability. They are one of the ideal choices for space-based detection systems. However, the existing single-band detection system has limited target information acquisition, weak anti-interference ability, and insufficient detection ability for dim targets, which has hindered the development of infrared dim target search and tracking. Summary of the invention

[0004] The present invention aims to provide a multi-band limb detection method for dark space targets, which integrates multi-band target energy information, improves the detection capability and sensitivity of the detection system to the target, and solves the problem of target detection at a long range.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A multi-band limb detection method for a faint space target comprises the following steps: S101. Use a multi-band infrared detection system for space target detection to image the limb or deep space area, where the limb area refers to an area 50km-500km above the earth, and the deep space area refers to an area above 500km above the earth; S102, obtaining the angle between the optical axis of the multi-band infrared detection system and the earth, and the angle between the optical axis of the multi-band infrared detection system and the sun, and setting the initial imaging integration time of each band according to the angle; S103, using the multi-band infrared detection system whose integration time is initialized in step S102 to observe and image the edge scene, and obtain multi-band infrared image data; S104, performing target detection on the image data acquired in step S103, and detecting candidate targets in the images of each band; S105, matching candidate targets in each band to select valid targets, the inter-band target matching matrix is ​​calculated by observing stars through the multi-band infrared detection system, and the valid target is a target simultaneously observed in at least two bands among the multiple bands; S106, calculating the neighborhood signal-to-noise ratio and background mean of the effective target in each band, selecting the two bands with the highest signal-to-noise ratio to perform effective target feature fusion, and encapsulating and outputting them together with the location information and angle information of the effective target; S107, using the average neighborhood signal-to-noise ratio and background mean of the target observed in each band to optimize the observation integration time and continuously detect the target.

[0006] Further, in S101, in S101, the multi-band infrared detection system has the following characteristics: the optical system is cryogenically cooled to below 120K, the detection band is subdivided in the 5-16 micron band according to the target characteristics, and no less than two bands are used to construct a planar array multi-band infrared detection system, and the detection system shares a main optical system for imaging; the multi-band infrared detection system is a planar array image detector; the output frame rate of the detector is not less than 10 frames / second; and the number of infrared channels is not less than two.

[0007] Further, in S102, the initial imaging integration time of each band is set according to the angle, including recording the grayscale values ​​of different integration times of each band under different angle combinations in advance, establishing a mapping table, and selecting the nearest neighbor integration time in the mapping table for setting according to the current angle.

[0008] Furthermore, in S104, target detection is performed on the image data acquired in step S103, including extracting salient point targets in the image by using a local contrast or morphological filtering method.

[0009] Further, in S105, the candidate targets of each band are matched to select valid targets, including uniformly registering the candidate targets of each band to the image coordinate system of band 1, calculating the Euclidean distance between targets of different bands, determining targets with Euclidean distances less than a set threshold as valid targets, and determining the Euclidean distances between targets of different bands as valid targets. Calculated according to the following formula:

[0010] in, is the candidate target coordinate data in band 1, The coordinate data of the candidate target in band 2 is registered to the image coordinate system of band 1.

[0011] Further, in S106, the neighborhood signal-to-noise ratio of the candidate target in each band The calculation formula is as follows:

[0012] in, is the maximum value of pixels in the center area of ​​the candidate target, is the background mean of the target neighborhood, is the target neighborhood background standard deviation, The calculation formula is as follows:

[0013] in, Indicates the center block ; Background mean The calculation formula is as follows:

[0014] in, Represents the neighborhood background block , express The radius of in, The calculation formula is as follows:

[0015] and The regions are represented as follows:

[0016]

[0017] Further, in S106, the two bands with the highest effective target signal-to-noise ratio are selected for target feature fusion, including separately extracting the radiation intensity and energy concentration of the target, averaging the energy concentration and image plane position features of the two bands, using the intensities of the two bands to fuse and calculate the temperature and equivalent radiation area, and outputting multi-band target fusion features.

[0018] Further, in S107, the observation integration time is optimized by using the average neighborhood signal-to-noise ratio and background mean of the observed target in each band, including calculating the average background mean of multiple targets. If the ratio of the background grayscale mean to the image saturation grayscale value is less than the set threshold, the minimum unit is used. Increase the integral time, otherwise decrease it according to the smallest unit.

[0019] The beneficial effects of the technical solution are: A multi-band edge detection method for dark and weak space targets of the present invention initializes the integration time of the detection system according to the angle between the optical axis of the multi-band infrared detection system and the earth and the sun, and obtains the grayscale image output by the multi-band image sensor; performs target detection and extraction on the image to obtain multi-band candidate target information; performs registration transformation on the multi-band detection results, and matches and associates the registered transformed targets, fuses the multi-band target information to calculate and output the target feature information, and finally optimizes and adjusts the integration time of the detection system according to the grayscale feature information of the target area and the neighborhood, so as to achieve the optimal observation of dark and weak targets. In summary, this method obtains multi-dimensional target information with the help of a multi-band infrared detection system, fuses the observation results of the detection system to achieve target energy enhancement, improves the detection capability of dark and weak targets, and solves the problem of target detection at long ranges. The integration time of the detection system is adaptively adjusted according to the target detection results to optimize the system detection capability to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of a multi-band limb detection method for a dark space target according to the present invention; Figure 2 A schematic diagram of target detection results of a multi-band infrared detection system of a multi-band limb detection method for dark and weak space targets of the present invention; Figure 3 A schematic diagram of multi-band target detection result registration of a multi-band limb detection method for a dark space target of the present invention; Figure 4 It is a schematic diagram of multi-band target detection results after the integration time of a multi-band limb detection method for dark space targets of the present invention is optimized. DETAILED DESCRIPTION

[0021] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments: like Figure 1 As shown, a multi-band limb detection method for a faint space target comprises the following steps: S101. Use a multi-band infrared detection system for space target detection to image the limb or deep space area, where the limb area refers to the area at an altitude of 50km-500km from the earth, and the deep space area refers to the area above 500km from the earth; the multi-band infrared detection system has the following characteristics: the optical system is cryogenically cooled to below 120K, and two long-wave and medium-long-wave bands are used to construct a multi-band infrared detection system in a planar array, and the detection system shares the main optical system for imaging; the multi-band infrared detection system is a planar array image detector; the output frame rate of the detector is 100 frames / second; the detection band is subdivided within the range of 5-16 microns; S102, obtaining the angle between the optical axis of the multi-band infrared detection system and the earth, and the angle between the optical axis of the multi-band infrared detection system and the sun, and setting the initial imaging integration time of each band according to the angle; wherein, setting the initial imaging integration time of each band according to the angle includes recording the grayscale values ​​of different integration times of each band under different angle combinations in advance, establishing a mapping table, and selecting the nearest neighbor integration time in the mapping table according to the current angle for setting, and the initial setting is 3ms; the mapping table is shown in Table 1: Table 1 Mapping table of sun angle, earth angle and integration time Serial number Sun Angle Earth angle Integration time 1 60° 60° 3ms 2 90° 90° 5ms 3 120° 120° 10ms 4 150° 150° 20ms S103, using the multi-band infrared detection system whose integration time is initialized in step S102 to observe and image the edge scene, and obtain multi-band infrared image data; S104, performing target detection on the image data acquired in step S103 to detect candidate targets in each band image; performing target detection on the image data, including extracting salient point targets in the image by using local contrast or morphological filtering methods.

[0022] S105, matching candidate targets in each band to select valid targets, the inter-band target matching matrix is ​​calculated by observing stars through the multi-band infrared detection system, and the valid target is a target simultaneously observed in at least two bands among the multiple bands; Among them, the candidate targets of each band are matched to select valid targets, including uniformly registering the candidate targets of each band to the image coordinate system of band 1, calculating the Euclidean distance between targets in different bands, and judging the targets with Euclidean distance less than five pixels as valid targets. Calculated according to the following formula:

[0023] in, is the candidate target coordinate data in band 1, The coordinate data of the candidate target in band 2 is registered to the image coordinate system of band 1.

[0024] S106, calculating the neighborhood signal-to-noise ratio and background mean of the effective target in each band, selecting the two bands with the highest signal-to-noise ratio for effective target feature fusion, and encapsulating and outputting them together with the position information and angle information of the effective target; wherein, selecting the two bands with the highest signal-to-noise ratio of the effective target for target feature fusion includes separately extracting the radiation intensity and energy concentration of the target, averaging the energy concentration and image plane position features of the two bands, using the intensities of the two bands to fuse and calculate the temperature and equivalent radiation area, and outputting the multi-band target fusion features; Neighborhood signal-to-noise ratio of candidate targets in each band The calculation formula is as follows:

[0025] in, is the maximum value of pixels in the center area of ​​the candidate target, is the background mean of the target neighborhood, is the target neighborhood background standard deviation, The calculation formula is as follows:

[0026] in, Indicates the center block ; Background mean The calculation formula is as follows:

[0027] in, Represents the neighborhood background block , express The radius of .

[0028] in, The calculation formula is as follows:

[0029] and The regions are represented as follows:

[0030]

[0031] The test results obtained by the method of the present invention are as follows Figure 2 As shown, Figure 2 (a) is the target detection result of band 1. The signal-to-noise ratio of target 1 is 5.6, the signal-to-noise ratio of target 2 is 5.11, and the average background mean is 5010. Figure 2 (b) is the target detection result of band 2. The signal-to-noise ratio of target 1 is 4.86, and the average background value is 4570. The integration time is adjusted to 5ms based on the calculation results.

[0032] The schematic diagram of multi-band target detection result registration provided by the example of the present invention is as follows Figure 3 As shown, Figure 3 (a) is the target detection result of band 1. The coordinates of target 1 are (431, 204) and the coordinates of target 2 are (167, 454). Figure 3(b) is a schematic diagram of the position of the target registration transformation of band 2 to the coordinate system of band 1 image. The coordinates of target 1 are (434, 206). The Euclidean distance between target 1 in band 1 and target 1 in band 2 is 3.6, and they are judged to be the same target.

[0033] S107, using the average neighborhood signal-to-noise ratio and background mean of the target observed in each band to adjust the observation integration time and continuously detect the target; specifically, calculate the average background mean of multiple targets, if the ratio of the background grayscale mean to the image saturation grayscale value is less than the set threshold, then according to the minimum unit Increase the integral time, otherwise decrease it according to the smallest unit.

[0034] The schematic diagram of multi-band target detection results after integration time optimization provided by the example of the present invention is as follows Figure 4 As shown, Figure 4 (a) is the target detection result of band 1. The signal-to-noise ratio of target 1 is 11.105, the signal-to-noise ratio of target 2 is 10.187, and the average background mean is 7980. Figure 4 (b) is the target detection result of band 2. The signal-to-noise ratio of target 1 is 9.89 and the average background mean is 7370.

[0035] In summary, a multi-band edge detection method for dark and weak space targets of the present invention initializes the integration time of the detection system according to the angle between the optical axis of the multi-band infrared detection system and the earth and the sun, and obtains the grayscale image output by the multi-band image sensor; performs target detection and extraction on the image to obtain multi-band candidate target information; performs registration transformation on the multi-band detection results, and matches and associates the registered transformed targets, fuses the multi-band target information to calculate and output the target feature information, and finally optimizes and adjusts the integration time of the detection system according to the grayscale feature information of the target area and the neighborhood, so as to achieve the optimal observation of dark and weak targets. In summary, this method obtains multi-dimensional target information with the help of a multi-band infrared detection system, fuses the observation results of the detection system to achieve target energy enhancement, improves the detection capability of dark and weak targets, and solves the problem of target detection at long range. The integration time of the detection system is adaptively adjusted according to the target detection results to optimize the system detection capability to the greatest extent.

[0036] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the technical solution of the present invention, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A multi-band limb detection method for dim space targets, characterized in that: The following steps are involved: S101. Use a multi-band infrared detection system for space target detection to image the limb or deep space area, where the limb area refers to an area 50km-500km above the earth, and the deep space area refers to an area above 500km above the earth; S102, obtaining the angle between the optical axis of the multi-band infrared detection system and the earth, and the angle between the optical axis of the multi-band infrared detection system and the sun, and setting the initial imaging integration time of each band according to the angle; S103, using the multi-band infrared detection system whose integration time is initialized in step S102 to observe and image the edge scene, and obtain multi-band infrared image data; S104, performing target detection on the image data acquired in step S103, and detecting candidate targets in the images of each band; S105, matching candidate targets in each band to select valid targets, the target matching matrix between bands is calculated by observing stars through the multi-band infrared detection system, and the valid target is a target that is simultaneously observed in at least two bands among the multiple bands; S106, calculating the neighborhood signal-to-noise ratio and background mean of the effective target in each band, selecting the two bands with the highest signal-to-noise ratio to perform effective target feature fusion, and encapsulating and outputting them together with the location information and angle information of the effective target; S107, using the average neighborhood signal-to-noise ratio and background mean of the target observed in each band to optimize the observation integration time and continuously detect the target.

2. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S101, the multi-band infrared detection system has the following characteristics: the optical system is cryogenically cooled to below 120K, the detection band is subdivided in the 5-16 micron band according to the target characteristics, and no less than two bands are used to construct a planar array multi-band infrared detection system. The detection system shares the main optical system for imaging; the multi-band infrared detection system is a planar array image detector; the output frame rate of the detector is not less than 10 frames / second; the number of infrared channels is not less than two.

3. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S102, the initial imaging integration time of each band is set according to the angle, including recording the grayscale values ​​of different integration times of each band under different angle combinations in advance, establishing a mapping table, and selecting the nearest neighbor integration time in the mapping table for setting according to the current angle.

4. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S104, target detection is performed on the image data acquired in step S103, including extracting salient point targets in the image by using a local contrast or morphological filtering method.

5. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S105, the candidate targets of each band are matched to select valid targets, including uniformly registering the candidate targets of each band to the image coordinate system of band 1, calculating the Euclidean distance between targets in different bands, determining targets with Euclidean distances less than a set threshold as valid targets, and determining the Euclidean distances between targets in different bands as valid targets. Calculated according to the following formula: ； in, is the candidate target coordinate data in band 1, The coordinate data of the candidate target in band 2 is registered to the image coordinate system of band 1.

6. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S106, the neighborhood signal-to-noise ratio of the candidate target in each band The calculation formula is as follows: ； in, is the maximum value of pixels in the center area of ​​the candidate target, is the background mean of the target neighborhood, is the target neighborhood background standard deviation, The calculation formula is as follows: ； in, Indicates the center block ; Background mean The calculation formula is as follows: ； in, Represents the neighborhood background block , express The radius of in, The calculation formula is as follows: ； and The regions are represented as follows: ； 。 7. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S106, the two bands with the highest signal-to-noise ratio are selected for effective target feature fusion, including separately extracting the radiation intensity and energy concentration of the target, averaging the energy concentration and image plane position features of the two bands, using the intensities of the two bands to fuse and calculate the temperature and equivalent radiation area, and outputting multi-band target fusion features.

8. The multi-band limb detection method for a faint space target according to claim 1, characterized in that: In S107, the observation integration time is optimized by using the average neighborhood signal-to-noise ratio and background mean of the observed target in each band, including calculating the average background mean of multiple targets. If the ratio of the background grayscale mean to the image saturation grayscale value is less than the set threshold, then the minimum unit is used. Increase the integral time, otherwise decrease it according to the smallest unit.

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