A method for evaluating the extreme perception of tiny signals caused by changes in positive and negative low temperature differences

Abstract

The present invention discloses a method for evaluating the extreme perception of tiny signals caused by changes in positive and negative low temperature differences, comprising the following steps: establishing a full-link timing simulation model based on target parameters, atmospheric transmission paths, and environmental background simulation to obtain image simulation sequences under different radiation levels in the imaging system; correcting the simulation sequence, obtaining the point target position through template matching with the actual differential image sequence, achieving sub-pixel-scale target scale resolution and background and target energy separation; and performing extreme perception evaluation of tiny signal targets using a weighted approach of the signal-to-noise ratio, speed ratio, and camera energy concentration ratio of the target's central pixel. The method of the present invention overcomes the problem of being unable to perceive a single tiny signal under low signal-to-noise ratios, integrates multiple target parameters, and establishes a more robust evaluation method for tiny signal extreme perception.

Description

Technical Field

[0001] The present invention belongs to the field of target detection, and in particular relates to a method for evaluating the extreme perception of tiny signals caused by changes in positive and negative temperature differences. Background Art

[0002] In target detection, space-based infrared remote sensing systems can effectively detect heat sources from various vehicles on the ground. Target detection and identification based on these systems has become a key focus in the detection field. For aircraft targets, in the long-wave infrared band, only the spontaneous heat radiation from the aircraft's skin can be detected. If the target is flying at low speed, the spontaneous radiation generated by friction with the air is limited. In the presence of high background noise and atmospheric radiation, the infrared image signal is close to the ambient noise, resulting in a very low signal-to-noise ratio, making it difficult to perceive such target signals.

[0003] Traditional remote sensing infrared target detection methods mainly rely on the significant difference between point high-temperature targets and background noise, and detect targets through background subtraction and threshold segmentation.

[0004] For example, Chinese patent document CN1606033A discloses a method for detecting and tracking small targets in infrared image sequences. First, the image background is obtained through morphological filtering, and the image background is subtracted from the original image to obtain a background-removed image containing the target and noise. Then, image whitening preprocessing is performed. In the tracking stage, the projection transformation method is used to initiate tracking. In the tracking maintenance stage, an auxiliary particle filter is used to estimate the target state. In the detection stage, the likelihood ratio hypothesis test method is used to determine whether the target exists. Finally, the target's track and detection probability are obtained.

[0005] Chinese patent document CN115937275A discloses a processing method for infrared image recognition, including acquiring an infrared image; preprocessing the infrared image to generate a score image; performing multi-scale segmentation processing on the score image to generate a high signal-to-noise ratio image; performing local thresholding processing on the high signal-to-noise ratio image to generate a target area; and filtering the target area to generate a target contour.

[0006] However, the above method requires the target to have an obvious high-energy response. When the target energy is insufficient and the target is in the form of a point image, it is difficult to detect the target.

[0007] In general, traditional methods have limited ability to perceive weak targets, and cannot effectively meet the needs of target detection and subsequent tracking and identification when the infrared signal generated by the target is small. Summary of the Invention

[0008] The present invention provides a method for evaluating the extreme perception of tiny signals caused by changes in positive and negative low temperature differences, which can improve the perception ability of weak targets and effectively meet the needs of target detection and subsequent tracking and identification when the infrared signal generated by the target is small.

[0009] A method for evaluating the extreme perception of tiny signals caused by changes in positive and negative temperature differences comprises the following steps:

[0010] (1) Establish a full-link timing simulation model based on target parameters, atmospheric transmission path, and environmental background simulation to obtain image simulation sequences under different radiation levels in the imaging system;

[0011] (2) For the detector output image, each pixel is divided into sub-pixel scales, and a basic model of the sensitivity within the pixel is established based on the camera parameters. The point spread function of the imaging system used is obtained through testing;

[0012] (3) According to the preset working environment, under the image simulation sequence of step (1) and the point spread function convolution of step (2), the sub-pixel scale sequence of the target signal is calculated; after further differential processing, a simulated differential image template sequence is obtained;

[0013] (4) Obtaining an image sequence collected during the actual operation of the remote sensing observation equipment, performing differential analysis on two images with a fixed frame difference in the image sequence to obtain the actual differential image sequence to be detected;

[0014] (5) For the differential image sequence obtained in step (4) and the differential image template sequence obtained in step (3), the position of the coarsely positioned target point is obtained by template matching;

[0015] (6) Based on the point spread function value obtained by the test in step (2), target positioning and energy extraction are performed on the coarse positioning target point obtained by template matching in step (5);

[0016] (7) Based on the sub-pixel position and energy information of the target obtained in step (6), the signal-to-noise ratio, velocity ratio, and camera energy concentration ratio of the target center pixel are weighted to perform an extreme perception evaluation of the small signal target.

[0017] The specific process of step (1) is:

[0018] (1-1) Simulation of the original radiation state of the target: Based on the expected parameters of the small signal target (area, temperature, etc.), its emission characteristics are simulated to obtain the target's outgoing radiance in the observed environment;

[0019] (1-2) Simulation of environmental background radiation: Based on the ground reflectivity model and solar radiation model, simulation is performed according to the average statistical values ​​of public meteorological data to obtain the environmental background radiation, including the uniform atmospheric thermal radiation in the transmission path and the radiation value of the surrounding environment where the target is located;

[0020] (1-3) Accumulate the original radiation state of the target and the environmental background radiation state to obtain the theoretical radiation state of the point target under different environmental background radiation states;

[0021] The relative position of the target in the environmental background is changed based on the preset target speed, and image simulation sequences with different target radiation levels, different environmental backgrounds and different motion states are obtained.

[0022] The specific process of step (2) is:

[0023] For the detector output image, each pixel is divided into N×N sub-pixels. Through intra-pixel micro-scanning of a fixed point source in the laboratory, the point spread function of the imaging system used is calculated by fitting the convolution relationship based on the point spread function of the imaging system and the energy distribution of the fixed point source.

[0024] In step (2), the point spread function model is expressed as follows:

[0025]

[0026] Where IPS is the intra-pixel sensitivity of the detector, which is represented by a model based on the detector's charge diffusion and capacitive coupling mechanisms and their test parameters. PSF is the point spread function of the optical system. (Δx, Δy) is the distance between the position of an ideal point light source incident on the detector's focal plane and the center of the target pixel.

[0027] The specific process of step (3) is:

[0028] According to the actual working distance of the imaging system, the response data sequence of the preset target is calculated under the convolution of the image simulation sequence obtained in step (1) and the point spread function obtained in step (2) based on the sub-pixel scale division of the target signal; at the same time, a random noise component is added according to the noise model of the detector used for imaging; the image data of two adjacent frames are differentially processed to suppress the background signal and retain the target signal, thereby obtaining a new differential image template sequence.

[0029] In step (6), target positioning and energy extraction are performed on the coarse positioning target point obtained by template matching in step (5), that is, the optimization solution expressed as follows is performed:

[0030]

[0031] Among them, P i′ ,j is the grayscale value of the actual pixel response, B′ is the background estimation value, I′ is the target energy intensity to be determined, (x c ′ ,y c ′ ) is the center of mass position of the target to be determined; ePSF is the point spread function model established in step (2).

[0032] The specific process of step (7) is:

[0033] For the target point with a small signal, the signal-to-noise ratio, speed, and energy concentration of the target are calculated based on the sub-pixel position and energy information of the target. After comparing with the actual differential image sequence to be detected obtained in step (4), the signal-to-noise ratio, speed ratio, and energy concentration ratio are obtained, and the confidence of the detection of the small signal target is evaluated after weighting.

[0034] The weighted formulas for signal-to-noise ratio, speed ratio, and energy concentration ratio are as follows:

[0035]

[0036] Among them, a1, a2, a3 are weighting coefficients, SNR is the signal-to-noise ratio of the system, v r With v s are the motion speeds of the actual target and the simulated target in the image sequence, d r with d s are the energy concentrations of the actual target and the simulated target in the image sequence, respectively.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. The present invention adopts differential image sequence matching based on target radiation and point spread function simulation to replace the traditional threshold detection method, which improves the detection capability of weak signals, reduces the requirements for detection, has high sensitivity and strong timeliness.

[0039] 2. The present invention constructs an intensity database of weak aerospace targets in different environments, which can be applied in tracking and identifying targets such as aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of a method for evaluating the tiny signal limit perception caused by changes in positive and negative low temperature differences according to the present invention;

[0041] Figure 2 A schematic diagram of a target differential image obtained by a simulated detector in an embodiment of the present invention;

[0042] Figure 3Schematic diagram of obtaining a target differential image under an actual test environment in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on the present invention.

[0044] like Figure 1 As shown, a method for evaluating the extreme perception of small signals caused by changes in positive and negative low temperature differences includes the following steps:

[0045] S1, establish a full-link timing simulation model based on target parameters, atmospheric transmission path, environmental background simulation and camera imaging parameters to obtain image simulation sequences under different target radiation levels, different environmental backgrounds and different motion states in the imaging system.

[0046] S101, simulation of the original radiation state of the target. In this embodiment, the target is positioned as an ordinary aerospace observation target, and its surface can be regarded as a Lambertian radiation source, that is, the emission of the surface (such as the aircraft skin) in all directions is uniform, and its radiation characteristics obey Planck's law, that is,

[0047]

[0048] Among them, M tar is the target spectral radiant emittance, ε tar is the spectral emissivity, c1 and c2 are the first and second radiation constants respectively. Based on the known temperature data of the target, the ideal target radiation characteristics can be obtained.

[0049] S102, simulation of environmental background radiation status.

[0050] The ambient background radiation primarily includes the uniform atmospheric thermal radiation along the transmission path and the radiation value of the target's surrounding environment. Based on a ground reflectance model and a solar radiation model, and using the average statistical values ​​of publicly available meteorological data, simulation software is used as the input for background estimation. In this example, MODTRAN software was used to simulate a mid-latitude summer marine atmosphere model. Under these conditions, including a mid-latitude summer atmosphere model, rural aerosols, a visibility of 23 km, and a solar altitude of 45°, the spectral radiance of the target against a standard water background at an altitude of 40,000 km was used as the background radiance before the pupil.

[0051] S103, simulating image simulation sequences under different radiation levels.

[0052] Based on the calculation results in S101 and S102, the original radiation state of the target and the background radiation state are accumulated to obtain the theoretical radiation situation of the point target under different background radiation states. The relative position of the target in the background is changed based on the preset target speed, and an image simulation sequence under different radiation levels can be simulated.

[0053] S2, based on testing, obtains models of the detector's pixel response, capacitive coupling, and other parameters. Using spatiotemporal noise simulation and detector model correction, a sub-pixel simulation sequence of the target signal is obtained. Point target positions are determined using template matching of the actual differential image sequence. Under the constraint that the target scale remains constant across multiple frames, sub-pixel target scale resolution and background-target energy separation are achieved.

[0054] S201, obtain a fixed point spread function of the detector through laboratory testing.

[0055] In this embodiment, each pixel of the detector output image is divided into 5×5 sub-pixels. Micro-scanning is performed within the pixel using a fixed point source in a laboratory. Based on the imaging system's point spread function and the energy distribution of the fixed point source, the point spread function value of the imaging system is calculated by fitting the convolution relationship. The point spread function model in this embodiment can be expressed as follows:

[0056]

[0057] IPS is the detector's intra-pixel sensitivity, expressed using a model based on the detector's charge diffusion and capacitive coupling mechanisms and their test parameters. PSF is the optical system's point spread function, expressed using a Gaussian function with a full width at half maximum of one pixel.

[0058] S202: Acquire a sub-pixel scale sequence of a target signal according to a preset working environment.

[0059] According to the actual working distance of the imaging system, the response data sequence of the preset target obtained in S1 under the convolution of the target and background image simulation sequence and the detection system point spread function is calculated based on the sub-pixel scale division described in S201. At the same time, according to the noise model of the detector used for imaging, a certain random noise component is added. In this embodiment, random noise with a signal-to-noise ratio of 20 is added. A simulated target image obtained by difference after simulation according to the method in steps S1-S2 is as follows Figure 2 shown.

[0060] S203, obtaining an image sequence collected by the remote sensing observation equipment in actual operation, performing a differential operation on two images with a fixed frame difference in the time domain image sequence, using the same method as S202, to form a target sequence to be detected.

[0061] S204, the differential image obtained in S203 is matched with the differential image template sequence obtained in S202 to obtain the possible target point position. For the image sequence, a certain rough positioning target point position can be obtained in each frame. In order to reduce the processing burden, the differential image within a certain range around the target is intercepted as the region of interest to be evaluated. For the actual point source target, the single frame differential image is obtained as follows: Figure 3 shown.

[0062] S205 , based on the equivalent point spread function obtained by the test in S201 , target positioning and energy extraction are performed on the coarse positioning target point obtained by template matching in S204 , that is, an optimization solution expressed by the following formula is performed.

[0063]

[0064] Among them, P i ′ ,j is the grayscale value of the actual pixel response, B′ is the background estimation value, I′ is the target energy intensity to be determined, (x c ′ ,y c ′ ) is the target center of mass position. Figure 3 The center of mass of the target is [0.5, 0.5].

[0065] S3, based on the sub-pixel position and energy of the target, constructs a multi-parameter weighted small signal limit perception evaluation method based on the central pixel signal-to-noise ratio, speed, camera energy concentration, etc.

[0066] Evaluate the possible weak signal target point, calculate the target's response signal-to-noise ratio, target movement speed, and target sub-pixel scale, and compare them with the differential image sequence obtained in S2. Based on the difference between the two, evaluate the confidence level of the weak target detection. In this embodiment, the weighted formula including the signal-to-noise ratio, speed ratio, and energy concentration ratio is as follows:

[0067]

[0068] Among them, a1, a2, and a3 are weighting coefficients, which are related to the specific goals and scenarios in the actual implementation of this method. SNR is the signal-to-noise ratio of the system, as calculated in the formula, v r With v s are the motion speeds of the actual target and the simulated target in the image sequence, d r with d s are the energy concentrations of the actual target and the simulated target in the image sequence, respectively.

[0069] The embodiments described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the extreme perception of small signals caused by changes in positive and negative low temperature differences, characterized in that: The following steps are involved: (1) Establish a full-link timing simulation model based on target parameters, atmospheric transmission path, and environmental background simulation to obtain image simulation sequences under different radiation levels in the imaging system; (2) For the detector output image, each pixel is divided into sub-pixel scales, and a basic model of the sensitivity within the pixel is established based on the camera parameters. The point spread function of the imaging system used is obtained through testing; (3) According to the preset working environment, under the image simulation sequence of step (1) and the point spread function convolution of step (2), the sub-pixel scale sequence of the target signal is calculated; after further differential processing, a simulated differential image template sequence is obtained; (4) Obtaining an image sequence collected during the actual operation of the remote sensing observation equipment, performing differential analysis on two images with a fixed frame difference in the image sequence to obtain the actual differential image sequence to be detected; (5) For the differential image sequence obtained in step (4) and the differential image template sequence obtained in step (3), the position of the coarsely positioned target point is obtained by template matching; (6) Based on the point spread function obtained by the test in step (2), target positioning and energy extraction are performed on the coarse positioning target point obtained by template matching in step (5); (7) Based on the sub-pixel position and energy information of the target obtained in step (6), the signal-to-noise ratio, velocity ratio, and camera energy concentration ratio of the target center pixel are weighted to perform an extreme perception evaluation of the small signal target.

2. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: The specific process of step (1) is: (1-1) Simulation of the original radiation state of the target: Based on the expected parameters of the small signal target, its emission characteristics are simulated to obtain the target's emitted radiance in the observed environment; (1-2) Simulation of environmental background radiation: Based on the ground reflectivity model and solar radiation model, simulation is performed according to the average statistical values ​​of public meteorological data to obtain the environmental background radiation, including the uniform atmospheric thermal radiation in the transmission path and the radiation value of the surrounding environment where the target is located; (1-3) Accumulate the original radiation state of the target and the environmental background radiation state to obtain the theoretical radiation state of the point target under different environmental background radiation states; The relative position of the target in the environmental background is changed based on the preset target speed, and image simulation sequences with different target radiation levels, different environmental backgrounds and different motion states are obtained.

3. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: The specific process of step (2) is: For the detector output image, each pixel is divided into N×N sub-pixels. Through intra-pixel micro-scanning of a fixed point source in the laboratory, the point spread function of the imaging system used is calculated by fitting the convolution relationship based on the point spread function of the imaging system and the energy distribution of the fixed point source.

4. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: In step (2), the point spread function model is expressed as follows: Where IPS is the intra-pixel sensitivity of the detector, which is represented by a model based on the detector's charge diffusion and capacitive coupling mechanisms and their test parameters. PSF is the point spread function of the optical system. (Δx, Δy) is the distance between the position of an ideal point light source incident on the detector's focal plane and the center of the target pixel.

5. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: The specific process of step (3) is: According to the actual working distance of the imaging system, the response data sequence of the preset target is calculated under the convolution of the image simulation sequence obtained in step (1) and the point spread function obtained in step (2) based on the sub-pixel scale division of the target signal; at the same time, a random noise component is added according to the noise model of the detector used for imaging; the image data of two adjacent frames are differentially processed to suppress the background signal and retain the target signal, thereby obtaining a new differential image template sequence.

6. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: In step (6), target positioning and energy extraction are performed on the coarse positioning target point obtained by template matching in step (5), that is, the optimization solution expressed as follows is performed: Among them, P i ′ ,j is the grayscale value of the actual pixel response, B′ is the background estimation value, I′ is the target energy intensity to be determined, (x c ′ ,y c ′ ) is the center of mass position of the target to be determined, and ePSF is the point spread function model established in step (2).

7. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 1, characterized in that: The specific process of step (7) is: For the target point with a small signal, the signal-to-noise ratio, speed, and energy concentration of the target are calculated based on the sub-pixel position and energy information of the target. After comparing with the actual differential image sequence to be detected obtained in step (4), the signal-to-noise ratio, speed ratio, and energy concentration ratio are obtained, and the confidence of the detection of the small signal target is evaluated after weighting.

8. The method for evaluating the tiny signal limit perception caused by the positive and negative low temperature difference according to claim 7, characterized in that: The weighted formulas for signal-to-noise ratio, speed ratio, and energy concentration ratio are as follows: Among them, a1, a2, a3 are weighting coefficients, SNR is the signal-to-noise ratio of the system, v r With v s are the motion speeds of the actual target and the simulated target in the image sequence, d r with d s are the energy concentrations of the actual target and the simulated target in the image sequence, respectively.

Images