A radar image processing method and processing system

Abstract

The present invention provides a radar image processing method and processing system, the processing method comprising: sorting the radar image in time series to obtain a data set; calculating and analyzing the pixel value change rate between the same positions in the data set to determine whether it is noise; deleting the data determined to be noise and returning the radar image after noise reduction; and continuing to determine the image at the next moment in time series order until the radar image is completely updated. The radar image processing method of the present invention utilizes the time dimension attribute of the radar image, constructs the algorithm based on the sequence image, extracts the noise characteristics of the image and eliminates it, and can obtain a better noise removal effect.

Description

Technical Field

[0001] The present invention relates to the field of radar image processing, and in particular to a radar image processing method and a radar image processing system. Background Art

[0002] Doppler weather radar is an active remote sensing detection tool, which has important applications in measuring the internal factors of the occurrence and development of clouds, rainfall and various severe convective weather. Its working principle is based on the Doppler effect, which can measure the speed of the scatterer relative to the radar and invert the atmospheric wind field, the distribution of the vertical velocity of the airflow and the turbulence under certain conditions. This is of great significance for warning against severe convective weather.

[0003] Weather radar detects cloud and rain echoes by emitting electromagnetic waves and receiving electromagnetic wave signals reflected from the detection space. Any target within the detection beam range will generate a reflected echo signal. When the radar echo signal caused by non-meteorological targets on the ground is processed into the base data, ground clutter is generated. The echo detected by the radar in the cloudless area of ​​the atmosphere or in the cloud area composed of very small particles that cannot be detected is called a clear sky echo. When in the evening or after rain, the temperature of the low-altitude atmosphere may be lower than that of the high-altitude atmosphere, and inversion occurs, resulting in super refraction, and ground clutter will appear on the radar. If a flying object flies by, the radar will generate radial clutter. Various radar clutters have a great impact on the further accurate analysis of the base data. Since all radar products and algorithms are based on base data, radar clutter affects both basic products and derived products. A particularly sensitive example is the impact on quantitative precipitation estimates.

[0004] When the radar position is determined, under standard atmospheric conditions, the position of ground clutter should be relatively fixed and the edges should be relatively clear. The ground clutter can be eliminated or suppressed by deducting the ground echo component recorded on sunny days and the filter of the radar signal processor. However, under normal circumstances, especially in precipitation, the atmosphere is not a standard atmosphere. The radar beam often refracts due to changes in temperature, humidity, air pressure and other factors, which leads to great changes in the position, shape, range, intensity, etc. of ground clutter, making it difficult to eliminate or suppress ground clutter with automatic and quick methods.

[0005] Radar data quality control is an important part of radar quantitative precipitation estimation (Quantitative Precipitation Estimation, QPE) and numerical weather prediction (NWP). By using appropriate data processing methods to remove radar clutter, the accuracy of radar observation data can be improved, and the impact of radar data problems on radar-derived data algorithms and product accuracy can be reduced, which has a strong practical significance for the monitoring and forecasting of ground-truth precipitation information. However, there is no better method for removing radar clutter in the existing technology.

[0006] In view of this, the present invention is proposed. Summary of the invention

[0007] In view of this, the present invention discloses a processing method and a processing system for radar images. The time dimension attribute of radar images is utilized, and the algorithm is constructed based on sequence images to extract the noise features of the images and eliminate them, which can obtain a better noise removal effect.

[0008] Specifically, the present invention is achieved through the following technical solutions:

[0009] In a first aspect, the present invention discloses a radar image processing method, comprising the following steps:

[0010] The radar images are sorted in time series to obtain a data set;

[0011] Calculating and analyzing the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise;

[0012] The data determined as noise is deleted and the denoised radar image is returned;

[0013] Continue to judge the image at the next moment in time sequence until all the radar images are updated.

[0014] In a second aspect, the present invention discloses a radar image processing system, comprising:

[0015] Sorting module: used to sort radar images in time series to obtain a data set;

[0016] A judgment module is used to calculate and analyze the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise;

[0017] Return module: used to delete the data determined as noise and return the radar image after noise reduction;

[0018] Update module: used to continue to judge the image at the next moment in time sequence until all radar images are updated.

[0019] In a third aspect, the present invention discloses a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the processing method described in the first aspect.

[0020] In a fourth aspect, the present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the processing method described in the first aspect when executing the program.

[0021] In summary, the radar image processing method and system proposed in the present invention are based on the algorithm of sequential images and reduce the noise of sequential images at the level of digital image processing. They have good universality and accuracy, reduce manpower consumption, and overcome the difficulties of traditional radar echo image denoising algorithms, such as cumbersome processing steps and the inability to process multiple noises simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0023] Figure 1 A schematic diagram of a process flow of a radar image processing method provided by an embodiment of the present invention;

[0024] Figure 2 A basic principle diagram of a sequence image radar denoising algorithm provided by an embodiment of the present invention;

[0025] Figure 3 A schematic diagram of the structure of a radar image processing system provided by an embodiment of the present invention;

[0026] Figure 4 A flowchart of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0028] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0029] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0030] The present invention discloses a radar image processing method, comprising the following steps:

[0031] The radar images are sorted in time series to obtain a data set;

[0032] Calculating and analyzing the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise;

[0033] The data determined as noise is deleted and the denoised radar image is returned;

[0034] Continue to judge the image at the next moment in time sequence until all the radar images are updated.

[0035] Reference Figure 1 FIG. 1 is a flowchart showing a specific radar echo image denoising algorithm for time series image processing in an embodiment of the present invention. For a specific schematic diagram, see Figure 2 In this embodiment, the specific steps of the radar echo image denoising algorithm based on time series image processing are implemented as follows:

[0036] Step 1: Sort the radar echo images read from the base data in time series:

[0037] After reading the radar base data, the generated sequence image data is sorted according to the time series to obtain the sequence radar data set P = {P1, P2, ..., P n}, where the larger the P subscript is, the later the corresponding time is.

[0038] Step 2: Calculate and analyze the change rate of pixel values ​​at the same position of continuous radar image data:

[0039] Select 2k+1 images centered at time t from the arranged sequence radar images for pixel value change rate analysis. Let the image P at time t be t The pixel with pixel coordinates (x, y) is P t(x,y) , analysis P t Left P t-k(x,y) ,P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) The rate of change of pixel value combinations between .

[0040] Step 3: Determine whether it is noise by the pixel value change rate:

[0041] Calculate P t Left P t-k(x,y) ,P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) The pixel value combination change rate is X t , when X t When it changes dramatically in a short period of time or remains almost unchanged for a long period of time, it is judged to be radial noise or clear sky clutter and ground clutter.

[0042] Step 4: Delete the noise data in step 3 and return the radar image after noise reduction:

[0043] By X t After the pixel is judged to be noise by the size of t Left P t-k(x,y) ,P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) Delete the pixel values ​​of the corresponding (x,y) points in a total of 2k+1 images, and return the deleted image.

[0044] Step 5: Move the time series backward and repeat steps 1 to 4 to continue judging the image at the next moment until all radar images are updated:

[0045] The sequence image filtering algorithm starts from the moment t=k+1 of the entire sequence, and t moves to the right by z moments each time until t+k equals the length of the sequence image, which means that the noise reduction operation is completed for the entire sequence.

[0046] In summary, the present invention specifically designs a radar echo image denoising algorithm based on time series image processing for radar images, and by analyzing the pixel value change rate characteristics of the sequence image, it can identify the general interference physical quantities of radar echoes such as ground clutter, clear sky clutter, and radial clutter. Different from the traditional radar echo image denoising algorithm, the present invention reduces the noise of the sequence image at the level of digital image processing, has good universality and accuracy, and overcomes the difficulties of the traditional radar echo image denoising algorithm, such as cumbersome processing steps and the inability to process multiple noises at the same time.

[0047] In addition, the present invention also provides a radar image processing system, such as Figure 3 As shown, specifically including:

[0048] Sorting module 101: used to sort radar images in time series to obtain a data set;

[0049] The judgment module 102 is used to calculate and analyze the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise;

[0050] Return module 103: used to delete the data determined as noise and return the radar image after noise reduction;

[0051] The updating module 104 is used to continue to determine the image at the next moment in a time series order until all the radar images are updated.

[0052] The system is mainly composed of the above four modules, and the construction of this system can well solve the noise problem of the image.

[0053] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above units can refer to the previous method embodiments, which will not be repeated here.

[0054] Figure 4 This is a schematic diagram of the structure of a computer device disclosed in the present invention. Figure 4 As shown, the computer device 400 includes at least a memory 402 and a processor 401; the memory 402 is connected to the processor via a communication bus 403, and is used to store computer instructions executable by the processor 401, and the processor 401 is used to read computer instructions from the memory 402 to implement the steps of the computing service processing method described in any of the above embodiments.

[0055] For the above-mentioned device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0056] Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM and flash memory devices), magnetic disks (e.g., internal disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory may be supplemented by, or incorporated in, special purpose logic circuits.

[0057] Finally, it should be noted that although this specification contains many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of a sub-combination.

[0058] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or requiring that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0059] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0060] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A radar image processing method, characterized in that: The steps include: The radar images are sorted in time series to obtain a data set; Calculating and analyzing the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise; The data determined as noise is deleted and the denoised radar image is returned; Continue to determine the image at the next moment in time sequence until all radar images are updated; The method for calculating and analyzing the pixel value change rate between the same positions in the data set comprises the following steps: Select 2k+1 images centered at time t to analyze the pixel value change rate. Let the image P at time t be t The pixel with pixel coordinates (x, y) is P t(x,y) , analysis P t Left P t-k(x,y) ,P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) The rate of change of pixel value combinations between ; The method for judging noise includes the following steps: calculating P t Left P t-k(x,y) , P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) The pixel value combination change rate is X t , when X t When it changes dramatically in a short period of time or remains almost unchanged for a long period of time, it is judged to be radial noise, clear sky clutter or ground clutter; The method for returning the radar image after noise reduction includes the following steps: after determining that the pixel point is noise, t Left P t-k(x,y) ,P t-k+1(x,y) ... Pt-1(x，y) and right P t-1(x，y) ...P t+k-1(x,y) , P t+k(x,y) With P t(x,y) The pixel values ​​of the corresponding (x, y) points in a total of 2k+1 images are deleted, and the radar image after deletion is returned.

2. The processing method according to claim 1, characterized in that: The method for time series sorting includes: sorting the sequence image data generated by the read radar base data according to the time series to obtain a sequence radar data set P = {P1, P2, ..., P n }, where the larger the P subscript is, the later the corresponding time is.

3. The processing method according to claim 1, characterized in that: The method for determining the image at the next moment includes: the sequence image filtering algorithm starts from the moment t=k+1 of the entire sequence, and t moves to the right by z moments each time until t+k equals the length of the sequence image.

4. A processing system using the radar image processing method according to any one of claims 1 to 3, characterized in that: include: Sorting module: used to sort radar images in time series to obtain a data set; A judgment module is used to calculate and analyze the change rate of pixel values ​​between the same positions in the data set to determine whether it is noise; Return module: used to delete the data determined as noise and return the radar image after noise reduction; Update module: used to continue to judge the image at the next moment in time sequence until all radar images are updated.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed, the steps of the radar image processing method according to any one of claims 1 to 3 are implemented.

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