A satellite intelligence data and combat system instant feedback analysis method and system

Abstract

The present invention discloses a satellite intelligence data and combat system instant feedback analysis method and system, the method comprising: extracting satellite imagery from satellite intelligence data, obtaining the image intensity of each position in the satellite imagery, and dividing the satellite imagery into multiple scales according to the image size; setting a satellite imagery blur degree judgment model, calculating the blur degree of each position on the satellite imagery according to the image intensity and scale of each position, and taking the position corresponding to the blur degree exceeding a preset blur threshold as an image blur region, and performing feedback; obtaining an image of the position and scale corresponding to the image blur region on a training scene image in a combat system, and replacing the image blur region.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite intelligence data processing, and more specifically, relates to a satellite intelligence data and combat system instant feedback analysis method and system. Background Art

[0002] Image restoration technology has been widely studied and applied in the field of computer vision. The following are some of the main image restoration technologies and their current status:

[0003] Generative Adversarial Networks (GANs): GANs are widely used for image inpainting by training a generator and a discriminator to generate high-quality missing parts. Conditional GANs (cGANs) and variants such as Pix2Pix are often used for image inpainting tasks.

[0004] Autoencoder: Autoencoder is a type of unsupervised learning neural network that can be used for image denoising and restoration. Variational Autoencoders (VAEs) are a common variant that can also generate restoration results with a certain degree of randomness.

[0005] Interpolation methods: Bilinear interpolation, cubic spline interpolation and other methods are used to estimate the pixel values ​​of the missing areas.

[0006] Statistical-based methods: using neighborhood information and prior knowledge, such as mean filtering, median filtering, etc.

[0007] However, there is no technology for determining blurred areas of satellite images and no method for quickly repairing blurred areas in the prior art. Summary of the invention

[0008] In order to solve the above technical problems, the present invention proposes a satellite intelligence data and combat system instant feedback analysis method, comprising:

[0009] Extracting satellite images from satellite intelligence data, obtaining image intensity at each position in the satellite images, and dividing the satellite images into multiple scales according to image size;

[0010] A satellite image blur degree judgment model is set to calculate the blur degree of each position on the satellite image according to the image intensity and scale of each position, and the position corresponding to the blur degree exceeding the preset blur threshold is taken as the image blur area, and feedback is provided;

[0011] An image of a training scene image in a combat system having a position and scale corresponding to the blurred image area is obtained, and the blurred image area is replaced.

[0012] Furthermore, the satellite image blur degree judgment model includes:

[0013]

[0014] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0015] Furthermore, the probability density P(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0016]

[0017] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0018] Furthermore, the first probability density P′(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0019]

[0020] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0021] Further, the second probability density P″(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0022]

[0023] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0024] The present invention also proposes a satellite intelligence data and combat system instant feedback analysis system, comprising:

[0025] A data acquisition module is used to extract satellite images from satellite intelligence data, obtain the image intensity of each position in the satellite images, and divide the satellite images into multiple scales according to the image size;

[0026] A model module is set up to set a satellite image blur degree judgment model, and the blur degree of each position on the satellite image is calculated according to the image intensity and scale of each position, and the position corresponding to the blur degree exceeding the preset blur threshold is taken as the image blur area, and feedback is given;

[0027] The repair module is used to obtain an image of a position and scale corresponding to the blurred area of ​​the image on the training scene image in the combat system, and replace the blurred area of ​​the image.

[0028] Furthermore, the satellite image blur degree judgment model includes:

[0029]

[0030] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0031] Furthermore, the probability density P(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0032]

[0033] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0034] Furthermore, the first probability density P′(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0035]

[0036] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0037] Further, the second probability density P″(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes:

[0038]

[0039] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0040] Compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0041] The present invention extracts satellite imagery from satellite intelligence data, obtains the image intensity of each position in the satellite imagery, and divides the satellite imagery into multiple scales according to the image size; sets a satellite imagery blurring judgment model, calculates the blurring degree of each position on the satellite imagery according to the image intensity and scale of each position, and uses the position corresponding to the blurring degree exceeding a preset blur threshold as the image blurring area, and provides feedback; obtains an image of the position and scale corresponding to the image blurring area on the training scene image in the combat system, and replaces the image blurring area. Through the above technical scheme, the present invention can first mark the blurring area in the satellite imagery, and then optimize the blurring area in combination with the training scene image, thereby repairing the satellite imagery. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a flow chart of the method of embodiment 1 of the present invention;

[0043] Figure 2 It is a system structure diagram of embodiment 2 of the present invention. DETAILED DESCRIPTION

[0044] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0046] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts in the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and calling data stored in the storage medium.

[0047] The storage medium may include a random access memory (RAM) or a read-only memory (ROM). The storage medium may be used to store instructions, programs, codes, code sets or instructions.

[0048] The display screen is used to display the user interface of each application.

[0049] All subscripts in the formulas of the present invention are only used to distinguish parameters and have no actual meaning.

[0050] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0051] Example 1

[0052] like Figure 1 As shown, an embodiment of the present invention provides a satellite intelligence data and combat system instant feedback analysis method, wherein the combat system is mainly used to simulate the war situation and provide training scene images. The combat system in the present invention is a prior art and is not limited to the system. The present invention only obtains the training scene images in the conventional combat system as the sample data of the present invention, including:

[0053] Step 101, extracting satellite images from satellite intelligence data, obtaining image intensity (e.g., pixel value) of each position in the satellite images, and dividing the satellite images into multiple scales according to image size;

[0054] Step 102, setting a satellite image blur degree judgment model, calculating the blur degree of each position on the satellite image according to the image intensity and scale of each position, and taking the position corresponding to the blur degree exceeding the preset blur threshold as the image blur area, and providing feedback;

[0055] Specifically, the satellite image blur degree judgment model includes:

[0056]

[0057] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0058] Specifically, the probability density P(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0059]

[0060] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0061] Specifically, the first probability density P′(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0062]

[0063] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0064] Specifically, the second probability density P″(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0065]

[0066] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0067] Step 103, obtaining an image of a position and scale corresponding to the blurred image area on the training scene image in the combat system, and replacing the blurred image area.

[0068] Example 2

[0069] like Figure 2 As shown, the embodiment of the present invention also provides a satellite intelligence data and combat system instant feedback analysis system, including:

[0070] A data acquisition module is used to extract satellite images from satellite intelligence data, obtain the image intensity of each position in the satellite images, and divide the satellite images into multiple scales according to the image size;

[0071] A model module is set up to set a satellite image blur degree judgment model, and the blur degree of each position on the satellite image is calculated according to the image intensity and scale of each position, and the position corresponding to the blur degree exceeding the preset blur threshold is taken as the image blur area, and feedback is given;

[0072] Specifically, the satellite image blur degree judgment model includes:

[0073]

[0074] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0075] Specifically, the probability density P(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0076]

[0077] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0078] Specifically, the first probability density P′(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0079]

[0080] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0081] Specifically, the second probability density P″(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0082]

[0083] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0084] The repair module is used to obtain an image of a position and scale corresponding to the blurred area of ​​the image on the training scene image in the combat system, and replace the blurred area of ​​the image.

[0085] Example 3

[0086] The embodiment of the present invention further proposes a storage medium storing a plurality of instructions, wherein the instructions are used to implement the satellite intelligence data and combat system instant feedback analysis method.

[0087] Optionally, in this embodiment, the above storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0088] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: Step 101, extracting a satellite image from satellite intelligence data, obtaining an image intensity of each position in the satellite image, and dividing the satellite image into multiple scales according to image size;

[0089] Step 102, setting a satellite image blur degree judgment model, calculating the blur degree of each position on the satellite image according to the image intensity and scale of each position, and taking the position corresponding to the blur degree exceeding the preset blur threshold as the image blur area, and providing feedback;

[0090] Specifically, the satellite image blur degree judgment model includes:

[0091]

[0092] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0093] Specifically, the probability density P(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0094]

[0095] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0096] Specifically, the first probability density P′(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0097]

[0098] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0099] Specifically, the second probability density p″(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0100]

[0101] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0102] Step 103, obtaining an image of a position and scale corresponding to the blurred image area on the training scene image in the combat system, and replacing the blurred image area.

[0103] Example 4

[0104] An embodiment of the present invention also proposes an electronic device, including a processor and a storage medium connected to the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a method for instant feedback analysis of satellite intelligence data and combat systems.

[0105] Specifically, the electronic device of this embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.

[0106] Among them, the storage medium can be used to store software programs and modules, such as a satellite intelligence data and combat system instant feedback analysis method in an embodiment of the present invention, and the corresponding program instructions / modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the storage medium, that is, realizing the above-mentioned satellite intelligence data and combat system instant feedback analysis method. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely arranged relative to the processor, and these remote storage media may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0107] The processor may call the information and application stored in the storage medium through the transmission system to execute the steps: Step 101, extracting a satellite image from the satellite intelligence data, obtaining the image intensity of each position in the satellite image, and dividing the satellite image into multiple scales according to the image size;

[0108] Step 102, setting a satellite image blur degree judgment model, calculating the blur degree of each position on the satellite image according to the image intensity and scale of each position, and taking the position corresponding to the blur degree exceeding the preset blur threshold as the image blur area, and providing feedback;

[0109] Specifically, the satellite image blur degree judgment model includes:

[0110]

[0111] Where H(x, y, σ) is the blur degree of the satellite image at the scale σ at the position (x, y), P(I|x, y, σ) is the probability density of the image intensity I at the scale σ at the position (x, y), and I is the image intensity at the scale σ at the position (x, y). The scale σ is used to define the distribution range of the image intensity.

[0112] Specifically, the probability density P(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0113]

[0114] Among them, P′(I|x, y, σ) is the first probability density of image intensity I at scale σ at position (x, y), and P″(I|x, y, σ) is the second probability density of image intensity I at scale σ at position (x, y).

[0115] Specifically, the first probability density P′(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0116]

[0117] Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution.

[0118] Specifically, the second probability density P″(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes:

[0119]

[0120] Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

[0121] Step 103, obtaining an image of a position and scale corresponding to the blurred image area on the training scene image in the combat system, and replacing the blurred image area.

[0122] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0123] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0124] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0127] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only storage medium (ROM, Read-Only Memory), random access storage medium (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0128] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A satellite intelligence data and combat system instant feedback analysis method, characterized in that: include: Extracting satellite images from satellite intelligence data, obtaining image intensity at each position in the satellite images, and dividing the satellite images into multiple scales according to image size; A satellite image blur degree judgment model is set to calculate the blur degree of each position on the satellite image according to the image intensity and scale of each position, and the position corresponding to the blur degree exceeding the preset blur threshold is taken as the image blur area, and feedback is provided; Acquire an image of a position and scale corresponding to the blurred image area on a training scene image in a combat system, and replace the blurred image area; Specifically, the satellite image blur degree judgment model includes: Where H(x, y, σ) is the blur level of the satellite image at the location (x, y) with a scale of σ, P(I|x, y, σ) is the probability density of the image intensity I at the location (x, y) with a scale of σ, and I is the image intensity at the location (x, y) with a scale of σ, where the scale σ is used to define the distribution range of the image intensity; Specifically, the probability density P(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes: Wherein, P′(I|x, y, σ) is the first probability density of the image intensity I at the position (x, y) and the scale σ, and P″(I|x, y, σ) is the second probability density of the image intensity I at the position (x, y) and the scale σ; Specifically, the first probability density P′(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes: Where K′ is the number of Gaussian distributions, w k is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution; Specifically, the second probability density P″(I|x, y, σ) of the image intensity I at the scale σ at the position (x, y) includes: Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

2. A satellite intelligence data and combat system instant feedback analysis system, characterized in that: include: A data acquisition module is used to extract satellite images from satellite intelligence data, obtain the image intensity of each position in the satellite images, and divide the satellite images into multiple scales according to the image size; A model module is set up to set a satellite image blur degree judgment model, and the blur degree of each position on the satellite image is calculated according to the image intensity and scale of each position, and the position corresponding to the blur degree exceeding the preset blur threshold is taken as the image blur area, and feedback is given; A repair module, used for acquiring an image of a position and scale corresponding to the blurred image area on a training scene image in a combat system, and replacing the blurred image area; Specifically, the satellite image blur degree judgment model includes: Where H(x, y, σ) is the blur level of the satellite image at the location (x, y) with a scale of σ, P(I|x, y, σ) is the probability density of the image intensity I at the location (x, y) with a scale of σ, and I is the image intensity at the location (x, y) with a scale of σ, where the scale σ is used to define the distribution range of the image intensity; The probability density P(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes: Wherein, P′(I|x, y, σ) is the first probability density of the image intensity I at the position (x, y) and the scale σ, and P″(I|x, y, σ) is the second probability density of the image intensity I at the position (x, y) and the scale σ; The first probability density P′(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes: Where K′ is the number of Gaussian distributions, wk is the weight of the kth Gaussian distribution, σ′ k is the standard deviation of the kth Gaussian distribution, μ k is the mean of the kth Gaussian distribution; The second probability density P″(I|x, y, σ) of the image intensity I at the position (x, y) and the scale σ includes: Where N is the number of real-time training scene images in the combat system, I i is the image intensity of the i-th training scene image, and h is the bandwidth parameter, which is used to control the degree of smoothness in the estimation process.

Images