Infrared cloud picture texture enhancement display method, device and equipment

Abstract

The invention provides an infrared cloud picture texture enhancement display method, device and equipment, and relates to the technical field of meteorology, and the method comprises the steps: obtaining infrared cloud picture data corresponding to a target region, and determining brightness temperature data corresponding to the target region; determining a first normal vector of a curved surface where each pixel vertex in the infrared cloud picture data is located by using the brightness temperature data and surface elevation data corresponding to the target area; determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud picture data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source; and generating a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity, so as to perform texture enhancement display on the infrared cloud picture data. According to the method, the texture of the infrared cloud picture data can be obviously enhanced, so that the observation of strong weather phenomena such as rapidly developed convection, mesoscale convection and typhoon is more interpretable, and a more effective basis is provided for the identification and early warning of the strong weather phenomena.

Description

Technical Field

[0001] The present invention relates to the field of meteorological technology, and in particular to a method, device and equipment for enhancing the display of infrared cloud image texture. Background Art

[0002] The functions and performance of the new generation of high-resolution geostationary satellites have achieved leapfrog development. The satellite's radiation imaging channels cover visible light, short-wave infrared, medium-wave infrared and long-wave infrared bands. The long-wave infrared channel provides all-weather high-temporal and spatial resolution observation capabilities relative to visible light data, providing continuous monitoring data and high-resolution cloud images for typhoons and severe convective weather.

[0003] Although satellite long-wave infrared cloud images are not affected by sunlight reflection and can reflect the temperature and height of cloud tops more realistically, weather processes such as convection and typhoons are often accompanied by changes in the cloud top brightness temperature gradient and changes in the divergence and curl of the cloud image optical flow vector. Therefore, compared with visible light, they contain less texture information that is helpful for analyzing the cloud top structure, and lack more intuitive information for monitoring, identifying and analyzing rapidly developing weather phenomena such as convection and typhoons. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an infrared cloud image texture enhancement display method, device and equipment, which can significantly enhance the texture of infrared cloud image data, thereby making the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoons more explainable, and providing a more effective basis for their identification and early warning.

[0005] In a first aspect, an embodiment of the present invention provides a method for enhancing the display of infrared cloud image texture, comprising:

[0006] Obtain infrared cloud image data corresponding to the target area, and determine the brightness temperature data corresponding to the target area, the brightness temperature data includes cloud top brightness temperature and surface brightness temperature;

[0007] Using the brightness temperature data and the surface elevation data corresponding to the target area, the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located is determined;

[0008] Determine the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source;

[0009] A shadow texture corresponding to the target area is generated based on the first normal vector and the light reflection intensity, and the shadow texture is used to perform texture enhancement display on the infrared cloud image data to obtain the target infrared cloud image data.

[0010] In one embodiment, the brightness temperature data and the surface elevation data corresponding to the target area are used to determine the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located, including:

[0011] Determine the cloud top height based on brightness temperature data to determine the pixel vertices belonging to the clear sky, sea and land surface and the pixel vertices belonging to the cloud body in the infrared cloud image data;

[0012] Determine the temperature gradient corresponding to the pixel vertices belonging to the clear sky, sea and land surface; and the temperature gradient corresponding to the pixel vertices belonging to the cloud body;

[0013] According to the temperature gradient and surface elevation data corresponding to each pixel vertex, the initial normal vector of the surface where each pixel vertex is located is determined;

[0014] For each pixel vertex, multiple adjacent pixel vertices corresponding to the pixel vertex are determined from the infrared cloud image data, and the average of the initial normal vectors corresponding to the adjacent pixel vertices is used as the first normal vector corresponding to the pixel vertex.

[0015] In one embodiment, determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source includes:

[0016] For each pixel vertex, based on the position information of the artificial light source and the position information of the pixel vertex in the infrared cloud image data, determine the light direction vector of the artificial light source relative to the pixel vertex;

[0017] The light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data is determined according to the first normal vector of the curved surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex.

[0018] In one embodiment, determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex includes:

[0019] For each pixel vertex, an inner product operation is performed between the first normal vector of the surface where the pixel vertex is located and the light direction vector of the artificial light source relative to the pixel vertex to obtain a second normal vector;

[0020] The second normal vector is used to represent the light reflection intensity of the artificial light source relative to the pixel vertex.

[0021] In one implementation, generating a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity includes:

[0022] For each pixel vertex, perform dot multiplication of the first normal vector and the second normal vector corresponding to the pixel vertex to obtain a dot multiplication result corresponding to the pixel vertex;

[0023] Normalize the dot product results corresponding to each pixel vertex;

[0024] Based on the normalized dot product result, the shadow texture corresponding to the target area is generated.

[0025] In one embodiment, the infrared cloud image data is texture enhanced and displayed using shadow texture to obtain target infrared cloud image data, including:

[0026] Perform color conversion on infrared cloud image data through the specified enhanced rendering strategy;

[0027] The shadow texture is used to perform texture enhancement display on the infrared cloud image data after color conversion to obtain the target infrared cloud image data.

[0028] In one embodiment, the infrared cloud image data after color conversion is texture enhanced and displayed using shadow texture to obtain target infrared cloud image data, including:

[0029] Based on a predetermined smoothing factor, the infrared cloud image data after shadow texture and color conversion is smoothed to obtain infrared cloud image data with shadow texture;

[0030] The infrared cloud image data with shadow texture is processed by mean filtering to obtain the target infrared cloud image data.

[0031] In a second aspect, an embodiment of the present invention further provides an infrared cloud image texture enhancement display device, comprising:

[0032] A brightness temperature determination module is used to obtain infrared cloud image data corresponding to the target area and determine brightness temperature data corresponding to the target area, where the brightness temperature data includes cloud top brightness temperature data and surface brightness temperature data;

[0033] A normal vector determination module is used to determine the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located by using the brightness temperature data and the surface elevation data corresponding to the target area;

[0034] A light reflection intensity determination module is used to determine the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source;

[0035] The texture enhancement module is used to generate a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity, and use the shadow texture to perform texture enhancement display on the infrared cloud image data to obtain the target infrared cloud image data.

[0036] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0037] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0038] The infrared cloud image texture enhancement display method, device and equipment provided by the embodiment of the present invention first obtains the infrared cloud image data corresponding to the target area, and determines the brightness temperature data corresponding to the target area, the brightness temperature data including the cloud top brightness temperature and the surface brightness temperature; then uses the brightness temperature data and the surface elevation data corresponding to the target area to determine the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located; then according to the first normal vector of the surface where each pixel vertex is located and the position information of the artificial light source, determines the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data; finally, based on the first normal vector and the light reflection intensity, generates the shadow texture corresponding to the target area, and uses the shadow texture to perform texture enhancement display on the infrared cloud image data to obtain the target infrared cloud image data. The above method simulates the shadow texture similar to the visible light image by combining the cloud top brightness temperature, and then uses the shadow texture to perform texture enhancement display on the infrared cloud image data by enhanced rendering, so that the texture of the infrared cloud image data can be significantly enhanced, so that the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoon is more explainable, and provides a more effective basis for its identification and early warning.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic diagram of a flow chart of an infrared cloud image texture enhancement display method provided by an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of the overall process of an infrared cloud image texture enhancement display method provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the structure of an infrared cloud image texture enhancement display device provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] At present, infrared cloud images lack intuitive information for monitoring, identifying and analyzing weather phenomena such as rapidly developing convection and typhoons. Based on this, the present invention provides an infrared cloud image texture enhancement display method, device and equipment, which can significantly enhance the texture of infrared cloud image data, thereby making the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoons more explainable, and providing a more effective basis for their identification and early warning.

[0048] To facilitate understanding of this embodiment, firstly, a method for enhancing the display of infrared cloud image texture disclosed in an embodiment of the present invention is described in detail. Figure 1 The flowchart of a method for enhancing the display of infrared cloud image texture is shown in FIG. 1 , and the method mainly includes the following steps S102 to S108:

[0049] Step S102, obtaining infrared cloud image data corresponding to the target area, and determining brightness temperature data corresponding to the target area.

[0050] Among them, infrared cloud images are infrared radiation emitted by the ground and cloud surface measured by satellites at 10.5 to 12.5 microns, and temperature distribution maps of this radiation are expressed in the form of images; brightness temperature data include cloud top brightness temperature and surface brightness temperature. In one example, the satellite obtains CIA infrared cloud image data for the target area, and uses the radiation represented by the infrared cloud image data to determine the cloud top brightness temperature and surface brightness temperature.

[0051] Step S104, using the brightness temperature data and the surface elevation data corresponding to the target area, determine the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located.

[0052] In one example, the cloud top brightness temperature and the ground surface brightness temperature can be used to identify the pixel vertices belonging to the cloud body and the pixel vertices belonging to the clear sky, sea and land surface in the infrared cloud image data, and then the temperature gradients are calculated separately, and the first normal vector of the surface where each pixel vertex is located is determined in combination with the ground elevation data.

[0053] Step S106, determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source.

[0054] In one example, an artificial light source can be set according to the observation characteristics of the satellite and the key focus areas, and the light direction vector of the artificial light source relative to each pixel vertex can be determined according to the position information of the artificial light source. The inner product of the first normal vector and the light direction vector corresponding to each pixel vertex is calculated to obtain the second normal vector, which can characterize the light reflection intensity of the artificial light source relative to the pixel vertex.

[0055] Step S108, generating a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity, and using the shadow texture to perform texture enhancement display on the infrared cloud image data to obtain the target infrared cloud image data.

[0056] In one example, a shadow texture can be generated based on the dot product result of the first normal vector and the second normal vector corresponding to each pixel vertex; then a reasonable enhanced rendering strategy is selected to perform color conversion on the infrared cloud image data; finally, the shadow texture is superimposed on the color-converted infrared cloud image data to obtain the target infrared cloud image data.

[0057] The infrared cloud image texture enhancement display method provided by the embodiment of the present invention simulates the shadow texture similar to the visible light image by combining the cloud top brightness temperature, and then uses the shadow texture to perform texture enhancement display on the infrared cloud image data through enhanced rendering. Therefore, the texture of the infrared cloud image data can be significantly enhanced, thereby making the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoons more explainable, and providing a more effective basis for their identification and early warning.

[0058] For ease of understanding, the embodiments of the present invention provide Figure 2 The schematic diagram of the overall process of an infrared cloud image texture enhancement display method shown includes data input, pixel normal vector calculation, light source position setting, light source normal vector calculation, texture calculation, color mapping, texture overlay, product output and other processes.

[0059] Based on this, the embodiment of the present invention provides a specific implementation of an infrared cloud image texture enhancement display method:

[0060] Step 1, calculating the cloud top height based on the infrared cloud image data T, includes the following steps 1.1 to 1.2:

[0061] Step 1.1, the radiation received by the satellite needs to be calibrated to ensure that the measured radiation intensity is proportional to the actual radiation intensity emitted by objects on the ground or in the atmosphere. Generally, a known radiation source (such as a blackbody radiator) is used to calibrate the satellite sensor. The surface brightness temperature and cloud top brightness temperature are calculated using the data after radiation calibration. Brightness temperature refers to the brightness temperature radiated by an object in the infrared band, usually expressed in temperature units (such as degrees Celsius or Kelvin). During the calibration and processing process, quality control and verification are required to ensure the accuracy and reliability of the data. This may involve comparison with ground observation station data, processing the same data using different algorithms, and statistical analysis.

[0062] Step 1.2, determine the cloud top height: The cloud top height is usually related to the temperature of the cloud top. Clouds will show a lower brightness temperature than the surrounding air in the infrared band, because the radiation reflected and emitted by the clouds makes its temperature lower than the surrounding environment. By comparing the brightness temperature of the clouds with the brightness temperature of the surrounding environment, the cloud top height can be inferred. Usually, there is a certain correspondence between the cloud top height and the cloud top temperature, and the temperature profile data of the forecast model can be combined to assist in calculating the cloud top height. However, the embodiment of the present invention only needs to obtain the cloud top texture, and the temperature difference between adjacent pixel vertices is small, and does not require a particularly accurate cloud top height. Therefore, the existence of atmospheric stability can be ignored, that is, it is assumed that the cloud top height is inversely proportional to the cloud top brightness temperature, that is:

[0063] CTH = 50-T / 6;

[0064] Among them, CTH is the cloud top height and T is the cloud top brightness temperature.

[0065] Step 2, calculate the normal vector of the surface of each pixel based on the infrared cloud image data T and the surface elevation, and calculate the first normal vector A of the pixel vertex based on the normal vector of the surface. It includes the following steps 2.1 to 2.4:

[0066] Step 2.1, based on the cloud top height calculated in step 1, determine the pixel vertices belonging to the clear sky, sea and land surface and the pixel vertices belonging to the cloud body in the infrared cloud image data.

[0067] In one example, since the infrared cloud image data T may contain pixel vertices of clear sky, land and sea surface and pixel vertices of cloud body, in general, the brightness temperature of cloud top is lower than the brightness temperature of ground surface. Using the inverse of brightness temperature as texture can distinguish cloud ground surface and cloud top, but since the altitude of land and sea surface is generally higher than sea surface, but the temperature is higher than sea surface, the texture near the coastline will cause texture illusion if the brightness temperature is used alone. Therefore, it is more reasonable to perform cloud monitoring before calculating texture, and to use surface elevation appropriately for texture calculation of clear sky, land and sea surface. The cloud monitoring algorithm is generally complex and may involve other auxiliary data sources. Here, to ensure the robustness of the algorithm, a simple threshold segmentation algorithm is used to separate the pixel vertices belonging to clear sky, land and sea surface and the pixel vertices belonging to cloud body in the infrared cloud image data based on cloud top elevation and ground elevation. Among them, the ground elevation of each pixel vertex is obtained from data sources such as topographic map or digital elevation model (DEM).

[0068] Step 2.2, respectively determine the temperature gradients corresponding to the pixel vertices belonging to the clear sky, sea and land surface; and the temperature gradients corresponding to the pixel vertices belonging to the cloud body.

[0069] In one example, for pixel vertices belonging to a cloud body, the temperature gradient can be calculated using infrared brightness temperature data. Specifically, the direction and magnitude of the temperature change can be estimated by comparing the temperature values ​​of adjacent pixels.

[0070] In one example, for pixel vertices belonging to the clear sky sea and land surface, the temperature gradient can be calculated in combination with the surface elevation data.

[0071] Step 2.3, according to the temperature gradient and surface elevation data corresponding to each pixel vertex, determine the initial normal vector of the surface where each pixel vertex is located.

[0072] Step 2.4, for each pixel vertex, determine multiple adjacent pixel vertices corresponding to the pixel vertex from the infrared cloud image data, and use the average of the initial normal vectors corresponding to the adjacent pixel vertices as the first normal vector corresponding to the pixel vertex.

[0073] In one example, for each pixel vertex, the first normal vector A of the pixel vertex can be estimated based on the initial normal vectors of the surrounding pixel vertices. For example, in the embodiment of the present invention, the first normal vector A of the pixel vertex is estimated using the average of the initial normal vectors of the neighboring pixel vertices of the pixel vertex.

[0074] Step 3, to simulate the lighting conditions during satellite observation, in order to better understand and analyze satellite images, the position of the artificial light source can be set according to the satellite's observation characteristics and key areas of interest. It includes the following steps 3.1 to 3.4:

[0075] Step 3.1, understand the characteristics of satellite observation: First, you need to understand the satellite observation parameters, including the satellite subsatellite point, observation resolution, etc. These parameters will affect the location of the light source.

[0076] Step 3.2, determine the area of ​​focus: Determine the area or target that needs to be focused on, which helps determine the location of the light source. For example, if the focus is on urban areas, the light source may be set directly above the satellite observation or slightly to the west to simulate midday lighting conditions; if the focus is on mountainous areas or canyons, the light source may be set slightly to the east to simulate morning or evening lighting conditions.

[0077] Step 3.3, simulate lighting conditions: Based on the above considerations, set the position of the light source to simulate the lighting conditions during satellite observation. You can use virtual light sources or ambient light to simulate the direction and intensity of sunlight.

[0078] Step 3.4, adjust parameters and observe results: After setting the light source position, observe the simulated satellite image, adjust parameters and experiment until satisfactory results are obtained. You can consider parameters such as the color, intensity, and direction of the light source to further optimize the simulation effect.

[0079] Step 4, calculating the second normal vector B of the artificial light source relative to each pixel vertex in the infrared cloud image data, includes the following steps 4.1 to 4.2:

[0080] Step 4.1, for each pixel vertex, based on the position information of the artificial light source and the position information of the pixel vertex in the infrared cloud image data, determine the light direction vector of the artificial light source relative to the pixel vertex. Specifically:

[0081] 1) Obtain infrared cloud image data and the first normal vector A of the pixel vertex: First, obtain the infrared cloud image data, including the position and attribute information of each pixel vertex, and the first normal vector A of the pixel vertex.

[0082] 2) Determine the position of the light source: Determine the position of the artificial light source in the cloud map coordinate system, which can usually be expressed using coordinates in three-dimensional space.

[0083] 3) Calculate the light direction vector: For each pixel vertex, connect the light source position with the pixel vertex position to obtain a light ray. Calculate the direction vector of this light ray, that is, the vector pointing from the light source to the pixel vertex.

[0084] Step 4.2, determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex.

[0085] In one example, for each pixel vertex, an inner product operation can be performed on the first normal vector of the surface where the pixel vertex is located and the light direction vector of the artificial light source relative to the pixel vertex to obtain a second normal vector, which is used to characterize the light reflection intensity of the artificial light source relative to the pixel vertex.

[0086] Specifically, for each pixel vertex, its first normal vector A is known, and the second normal vector B can be obtained by performing an inner product operation on the light direction vector and the first normal vector A. The result of the inner product operation is a scalar, and the second normal vector B can be obtained by multiplying the first normal vector A.

[0087] Repeat steps 4.1 to 4.2 for each pixel vertex in the infrared cloud image data to calculate the corresponding second normal vector B. The second normal vector B can be used for subsequent analysis such as lighting simulation and shadow calculation.

[0088] Step 5, by calculating the dot product of the first normal vector A and the second normal vector B, the shadow texture C of the infrared cloud image data is obtained. The result of the dot product represents the angle between the light and the surface normal vector, which can be used to simulate the effect of light on the surface. It includes the following steps 5.1 to 5.3:

[0089] Step 5.1: For each pixel vertex, perform dot multiplication on the first normal vector and the second normal vector corresponding to the pixel vertex to obtain the dot multiplication result corresponding to the pixel vertex.

[0090] In one example, for each pixel vertex, the first normal vector A and the second normal vector B are multiplied. The dot product operation can multiply the components of the vector and then sum them to get a scalar value. The result of the dot product represents the angle between the light and the surface normal vector, and its value range is usually between [-1,1].

[0091] Step 5.2, normalize the dot product result corresponding to each pixel vertex.

[0092] In one example, the dot product result is normalized and mapped to [0,1] to be used as the color value of the shadow texture.

[0093] Step 5.3, based on the normalized point multiplication result, generate the shadow texture corresponding to the target area.

[0094] In one example, the normalized dot product result is used as the color value of the shadow texture to generate the shadow texture C of the infrared cloud image data. Specifically, the dot product result can be used as a grayscale value, or color mapping can be performed according to specific needs to present different visual effects.

[0095] Step 6, select a reasonable enhanced rendering strategy, perform color conversion on the infrared cloud image data, enhance the display of the infrared cloud image data through color, and also try to retain the brightness and temperature information of the infrared cloud image data through color after texture superposition, in preparation for subsequent texture superposition. It includes the following steps 6.1 to 6.3:

[0096] Step 6.1, enhancement scheme selection: The embodiment of the present invention adopts the color texture enhancement scheme often used by the typhoon warning center. This scheme has richer colors and is equipped with color jumps in the qualitative change stage of convection, which can retain the brightness temperature information of the cloud map to the maximum extent after the subsequent texture superposition. Of course, the rendering scheme can also be changed arbitrarily according to different weather phenomena.

[0097] Step 6.2, color scale setting: The color scale setting should be determined according to the distribution of infrared brightness temperature data and observation requirements. Lower brightness temperatures can be mapped to warm colors and higher brightness temperatures can be mapped to cool colors to highlight the temperature difference. Adjusting the color scale range can control the color saturation and contrast, thus affecting the display effect.

[0098] Step 6.3, Color Mapping: Select an appropriate color mapping scheme to represent data of different brightness temperature ranges. For example, higher brightness temperatures can be mapped to blue or purple, and lower brightness temperatures can be mapped to yellow or orange. The choice of color mapping should take into account visual legibility and the overall effect after texture overlay.

[0099] Step 7, using the shadow texture to perform texture enhancement display on the infrared cloud image data after color conversion, to obtain the target infrared cloud image data. Specifically, based on the infrared cloud image data T and the shadow texture C, combined with the smoothing factor, the infrared cloud image data with shadow texture Tc can be obtained. It includes the following steps 7.1 to 7.3:

[0100] Step 7.1, define smoothing factor: define a smoothing factor to indicate the influence of shadow texture C on infrared cloud image data T. The smoothing factor is usually a value between 0 and 1, indicating the importance of texture to infrared brightness temperature, 0 means no consideration of texture, 1 means full consideration of texture.

[0101] Step 7.2, based on a predetermined smoothing factor, the infrared cloud image data after shadow texture and color conversion is smoothed to obtain infrared cloud image data with shadow texture.

[0102] In one example, for each pixel vertex, the textured infrared cloud image data Tc is calculated using the following formula:

[0103] Tc=(1-smoothing factor)×T+smoothing factor×C

[0104] Among them, T is the infrared cloud image data, C is the shadow texture, and Tc is the infrared cloud image data with texture.

[0105] Step 7.3, perform mean filtering on the infrared cloud image data with shadow texture to obtain the target infrared cloud image data.

[0106] In one example, the obtained textured infrared cloud image data Tc is smoothed using a mean filter to eliminate noise and mutations.

[0107] In summary, the embodiment of the present invention proposes a new method for calculating the texture of an infrared cloud image by a given light source. By combining the cloud top brightness temperature to simulate a texture similar to a visible light image, and then processing it through enhanced rendering technology, not only can the infrared brightness temperature information be retained to the maximum extent, but also a texture similar to a visible light cloud image can be provided for the infrared cloud image data. This method can provide a more intuitive geometric display, making the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoons more interpretable, and providing a more effective basis for their identification and early warning.

[0108] Based on the above embodiments, the present invention provides an infrared cloud image texture enhancement display device, see Figure 3 The structure diagram of an infrared cloud image texture enhancement display device shown in FIG. 1 mainly includes the following parts:

[0109] The brightness temperature determination module 302 is used to obtain infrared cloud image data corresponding to the target area and determine brightness temperature data corresponding to the target area, where the brightness temperature data includes cloud top brightness temperature data and surface brightness temperature data;

[0110] The normal vector determination module 304 is used to determine the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located by using the brightness temperature data and the surface elevation data corresponding to the target area;

[0111] The light reflection intensity determination module 306 is used to determine the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source;

[0112] The texture enhancement module 308 is used to generate a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity, and use the shadow texture to perform texture enhancement display on the infrared cloud image data to obtain the target infrared cloud image data.

[0113] The infrared cloud image texture enhancement display device provided by the embodiment of the present invention simulates the shadow texture similar to the visible light image by combining the cloud top brightness temperature, and then uses the shadow texture to perform texture enhancement display on the infrared cloud image data through enhanced rendering. Therefore, the texture of the infrared cloud image data can be significantly enhanced, thereby making the observation of strong weather phenomena such as rapidly developing convection, mesoscale convection and typhoons more explainable, and providing a more effective basis for their identification and early warning.

[0114] In one implementation, the normal vector determination module 304 is specifically configured to:

[0115] Determine the cloud top height based on brightness temperature data to determine the pixel vertices belonging to the clear sky, sea and land surface and the pixel vertices belonging to the cloud body in the infrared cloud image data;

[0116] Determine the temperature gradient corresponding to the pixel vertices belonging to the clear sky, sea and land surface; and the temperature gradient corresponding to the pixel vertices belonging to the cloud body;

[0117] According to the temperature gradient and surface elevation data corresponding to each pixel vertex, the initial normal vector of the surface where each pixel vertex is located is determined;

[0118] For each pixel vertex, multiple adjacent pixel vertices corresponding to the pixel vertex are determined from the infrared cloud image data, and the average of the initial normal vectors corresponding to the adjacent pixel vertices is used as the first normal vector corresponding to the pixel vertex.

[0119] In one implementation, the light reflection intensity determination module 306 is specifically configured to:

[0120] For each pixel vertex, based on the position information of the artificial light source and the position information of the pixel vertex in the infrared cloud image data, determine the light direction vector of the artificial light source relative to the pixel vertex;

[0121] The light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data is determined according to the first normal vector of the curved surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex.

[0122] In one implementation, the light reflection intensity determination module 306 is specifically configured to:

[0123] For each pixel vertex, an inner product operation is performed between the first normal vector of the surface where the pixel vertex is located and the light direction vector of the artificial light source relative to the pixel vertex to obtain a second normal vector;

[0124] The second normal vector is used to represent the light reflection intensity of the artificial light source relative to the pixel vertex.

[0125] In one implementation, the texture enhancement module 308 is specifically configured to:

[0126] For each pixel vertex, perform dot multiplication of the first normal vector and the second normal vector corresponding to the pixel vertex to obtain a dot multiplication result corresponding to the pixel vertex;

[0127] Normalize the dot product results corresponding to each pixel vertex;

[0128] Based on the normalized point multiplication result, the shadow texture corresponding to the target area is generated.

[0129] In one implementation, the texture enhancement module 308 is specifically configured to:

[0130] Perform color conversion on infrared cloud image data through the specified enhanced rendering strategy;

[0131] The shadow texture is used to perform texture enhancement display on the infrared cloud image data after color conversion to obtain the target infrared cloud image data.

[0132] In one implementation, the texture enhancement module 308 is specifically configured to:

[0133] Based on a predetermined smoothing factor, the infrared cloud image data after shadow texture and color conversion is smoothed to obtain infrared cloud image data with shadow texture;

[0134] The infrared cloud image data with shadow texture is processed by mean filtering to obtain the target infrared cloud image data.

[0135] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0136] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0137] Figure 4 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0138] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0139] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0140] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0141] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0142] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0143] If the functions are implemented in the form of software functional units and sold or used as independent products, they 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 the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several 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 methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0144] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for enhancing the display of infrared cloud image texture, characterized in that: include: Acquire infrared cloud image data corresponding to the target area, and determine brightness temperature data corresponding to the target area, wherein the brightness temperature data includes cloud top brightness temperature and surface brightness temperature; Determine a first normal vector of a curved surface at which each pixel vertex in the infrared cloud image data is located by using the brightness temperature data and the surface elevation data corresponding to the target area; Determine the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source; A shadow texture corresponding to the target area is generated based on the first normal vector and the light reflection intensity, and the infrared cloud image data is texture enhanced and displayed using the shadow texture to obtain target infrared cloud image data.

2. The infrared cloud image texture enhancement display method according to claim 1, characterized in that: Determining the first normal vector of the surface where each pixel vertex in the infrared cloud image data is located by using the brightness temperature data and the surface elevation data corresponding to the target area, including: Determine the cloud top height based on the brightness temperature data to determine the pixel vertices belonging to the clear sky, sea and land surface and the pixel vertices belonging to the cloud body in the infrared cloud image data; Respectively determine the temperature gradient corresponding to the pixel vertices belonging to the clear sky, sea and land surface; and the temperature gradient corresponding to the pixel vertices belonging to the cloud body; Determine an initial normal vector of the surface where each pixel vertex is located according to the temperature gradient and the surface elevation data corresponding to each pixel vertex; For each pixel vertex, a plurality of adjacent pixel vertices corresponding to the pixel vertex are determined from the infrared cloud image data, and an average of the initial normal vectors corresponding to the adjacent pixel vertices is used as a first normal vector corresponding to the pixel vertex.

3. The infrared cloud image texture enhancement display method according to claim 1, characterized in that: Determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the surface where each pixel vertex is located and the position information of the artificial light source, comprising: For each of the pixel vertices, based on the position information of the artificial light source and the position information of the pixel vertex in the infrared cloud image data, determining the light direction vector of the artificial light source relative to the pixel vertex; The light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data is determined according to the first normal vector of the surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex.

4. The infrared cloud image texture enhancement display method according to claim 3 is characterized in that: Determining the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the light direction vector of the artificial light source relative to each pixel vertex, comprising: For each pixel vertex, performing an inner product operation on the first normal vector of the curved surface where the pixel vertex is located and the light direction vector of the artificial light source relative to the pixel vertex to obtain a second normal vector; The second normal vector is used to represent the light reflection intensity of the artificial light source relative to the pixel vertex.

5. The infrared cloud image texture enhancement display method according to claim 4, characterized in that: Generating a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity includes: For each pixel vertex, performing a dot multiplication process on the first normal vector and the second normal vector corresponding to the pixel vertex to obtain a dot multiplication result corresponding to the pixel vertex; Normalizing the dot product result corresponding to each pixel vertex; Based on the normalized dot product result, a shadow texture corresponding to the target area is generated.

6. The infrared cloud image texture enhancement display method according to claim 1, characterized in that: The infrared cloud image data is texture enhanced and displayed using the shadow texture to obtain target infrared cloud image data, including: Performing color conversion on the infrared cloud image data by using a specified enhanced rendering strategy; The infrared cloud image data after color conversion is texture enhanced and displayed using the shadow texture to obtain target infrared cloud image data.

7. The infrared cloud image texture enhancement display method according to claim 6, characterized in that: The infrared cloud image data after color conversion is texture enhanced and displayed using the shadow texture to obtain target infrared cloud image data, including: Based on a predetermined smoothing factor, the infrared cloud image data after the shadow texture and color conversion is smoothed to obtain the infrared cloud image data with the shadow texture; The infrared cloud image data with the shadow texture is processed by mean filtering to obtain target infrared cloud image data.

8. An infrared cloud image texture enhancement display device, characterized in that: include: A brightness temperature determination module is used to obtain infrared cloud image data corresponding to a target area, and determine brightness temperature data corresponding to the target area, wherein the brightness temperature data includes cloud top brightness temperature data and surface brightness temperature data; A normal vector determination module, used to determine a first normal vector of the surface where each pixel vertex in the infrared cloud image data is located by using the brightness temperature data and the surface elevation data corresponding to the target area; a light reflection intensity determination module, used to determine the light reflection intensity of the artificial light source relative to each pixel vertex in the infrared cloud image data according to the first normal vector of the curved surface where each pixel vertex is located and the position information of the artificial light source; The texture enhancement module is used to generate a shadow texture corresponding to the target area based on the first normal vector and the light reflection intensity, and use the shadow texture to perform texture enhancement display on the infrared cloud image data to obtain target infrared cloud image data.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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