Method and apparatus for generating cloud images using multi-spectral long-wave infrared remote sensing data

Abstract

The disclosed embodiments disclose a method, device, and electronic device for generating cloud maps using multi-spectral long-wave infrared remote sensing data. A specific implementation of the method includes: acquiring long-wave infrared remote sensing data from multiple spectral bands, determining the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band, and then determining the pseudo-emissivity corresponding to the long-wave infrared remote sensing data of each spectral band based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; and normalizing the pseudo-emissivity to obtain the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band; thereby generating a color cloud map for indicating meteorological information. In this way, it is helpful to use the generated color cloud map to effectively distinguish between areas such as the surface, high clouds, and low clouds. This can provide high-frequency effective information, which is beneficial for weather forecasters to understand the actual weather conditions.

Description

Technical Field

[0001] The present disclosure relates to the technical field of remote sensing image generation, and in particular to a method and device for generating cloud images using multi-spectral long-wave infrared remote sensing data. Background Art

[0002] Meteorological satellite remote sensing data is a crucial data source for numerical weather forecasting in the meteorological field and a crucial reference for forecasters in obtaining real-time weather conditions. Geosynchronous (or geostationary) satellites provide 24-hour Earth observation, acquiring data in the solar reflection and thermal infrared bands.

[0003] Therefore, after obtaining ground observation data, corresponding weather cloud maps can be generated based on the observation data, and weather forecasters can make weather forecasts based on the generated weather cloud maps. Therefore, the accuracy of weather cloud map generation is related to the accuracy of weather forecasts.

[0004] However, the weather cloud map generation solutions currently available on the market all have certain limitations, which means that the generated weather cloud maps may not allow weather forecasters to accurately identify various weather conditions, thus affecting the forecast accuracy. Summary of the Invention

[0005] This disclosure section is provided to briefly introduce concepts that will be described in detail in the detailed description section below. This disclosure section is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0006] In a first aspect, embodiments of the present disclosure provide a method and apparatus for generating a cloud map using multi-spectral long-wave infrared remote sensing data, the method comprising:

[0007] Acquire long-wave infrared remote sensing data in multiple spectral bands;

[0008] Determine the brightness temperature information corresponding to the long-wave infrared remote sensing data in each spectral band;

[0009] Based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band, the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band is determined;

[0010] Each pseudo-emissivity information is normalized respectively to obtain the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0011] A color cloud map indicating meteorological information is generated based on the normalized pseudo-emissivity information corresponding to each segment of long-wave infrared remote sensing data.

[0012] In a second aspect, an embodiment of the present disclosure provides a device for generating a cloud map using multi-spectral long-wave infrared remote sensing data, comprising:

[0013] An acquisition unit, used for acquiring long-wave infrared remote sensing data of multiple spectral bands;

[0014] The first determining unit is used to determine the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0015] The second determining unit is configured to determine the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0016] The calculation unit is used to perform normalization processing on each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0017] The generating unit is used to generate a color cloud map for indicating meteorological information according to the normalized pseudo emissivity information corresponding to each segment of long-wave infrared remote sensing data.

[0018] In a third aspect, an embodiment of the present disclosure provides an electronic device comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating cloud maps using multi-spectral long-wave infrared remote sensing data as described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for generating a cloud map using multi-spectral long-wave infrared remote sensing data as described in the first aspect.

[0020] The present disclosure discloses a method and apparatus for generating cloud maps using multi-band long-wave infrared remote sensing data. This method obtains long-wave infrared remote sensing data from multiple spectral bands and determines the brightness temperature information corresponding to each spectral band. Based on the brightness temperature information corresponding to each spectral band, the pseudo-emissivity corresponding to each spectral band is determined. The pseudo-emissivity is then normalized to obtain the normalized pseudo-emissivity information corresponding to each spectral band of long-wave infrared remote sensing data. This method generates a color cloud map for indicating meteorological information. This method facilitates the use of the generated color cloud map to effectively distinguish between areas such as the surface, high clouds, and low clouds. This helps weather forecasters understand the actual weather conditions and provides high-frequency, effective information. Furthermore, compared to traditional single-band brightness temperature grayscale maps, the color cloud map of this solution more prominently displays image details, making it more convenient for weather forecasters to make weather forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0022] Figure 1 is a flow chart of an embodiment of a method for generating a cloud map using multi-spectral long-wave infrared remote sensing data according to the present disclosure;

[0023] Figures 2A-2D It is a schematic diagram of the radiance temperature of a cloud map generated by using multi-spectral long-wave infrared remote sensing data disclosed in the present invention;

[0024] Figures 3A-3C It is a schematic diagram of a pseudo-emissivity corresponding map of a cloud map generated using multi-spectral long-wave infrared remote sensing data according to the present disclosure;

[0025] Figures 4A-4D It is a schematic diagram of color conversion for generating cloud images using multi-spectral long-wave infrared remote sensing data according to the present disclosure;

[0026] Figure 5 This is a schematic structural diagram of an embodiment of a device for generating cloud images using multi-spectral long-wave infrared remote sensing data according to the present disclosure;

[0027] Figure 6 This is an exemplary system architecture in which the method for generating cloud images using multi-spectral long-wave infrared remote sensing data according to an embodiment of the present disclosure can be applied;

[0028] Figure 7 It is a schematic diagram of the basic structure of an electronic device provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0030] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0031] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0032] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0033] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0034] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0035] One or more embodiments of the present application generate a color cloud map for indicating meteorological information by acquiring long-wave infrared remote sensing data from multiple spectral bands. This method facilitates the use of the generated color cloud map to effectively distinguish between surface, high cloud, low cloud, and other meteorological conditions. This helps weather forecasters understand the actual weather conditions and can provide high-frequency, effective information. Furthermore, compared to traditional single-band brightness temperature grayscale maps, the color cloud map of this solution more prominently displays image details and is more helpful for weather forecasters in making weather forecasts.

[0036] Please refer to Figure 1 , which shows the process of an embodiment of the method for generating cloud images using multi-spectral long-wave infrared remote sensing data according to the present disclosure. Figure 1 The method for generating a cloud map using multi-spectral long-wave infrared remote sensing data includes the following steps:

[0037] Step 101, acquiring long-wave infrared remote sensing data of multiple spectral bands;

[0038] Step 102, determining brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0039] Step 103, based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band, determining the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0040] Step 104, normalizing each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0041] Step 105 : generating a color cloud map for indicating meteorological information based on the normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band.

[0042] In step 101, remote sensing data detected by geostationary meteorological satellites can be used as a basis to obtain long-wave infrared remote sensing data in multiple spectral bands. Of course, the specific spectral bands of remote sensing data to be used can be reasonably set according to actual conditions. At the same time, the specific remote sensing data detected by geostationary meteorological satellites to be used can also be limited according to actual conditions.

[0043] For example, infrared spectrum data is not limited by solar radiation, so the weather cloud images generated in this way can achieve continuous ground monitoring throughout the day.

[0044] In step 102, the long-wave infrared remote sensing data of each spectral band may be converted into corresponding radiance temperature. That is, the brightness temperature information may indicate the radiance temperature.

[0045] Brightness temperature (BT) refers to the radiance of an object at a specific temperature, as an ideal blackbody radiator. This concept is particularly important in remote sensing, as remote sensing sensors receive radiant energy from the Earth's surface or other celestial bodies. This radiant energy is converted into electrical signals that can be used to calculate and analyze the temperature and other physical properties of the surface or atmosphere. It should be understood that radiance temperature can be calculated using Planck's law. It should be understood that radiance temperature is expressed in Kelvin (K).

[0046] The pseudo-emissivity in step 103 refers to the ratio of an actual object's radiative capacity to that of a blackbody at the same temperature in radiative heat transfer. Calculating pseudo-emissivity using radiance temperature is an inversion process. Determining the pseudo-emissivity corresponding to long-wave infrared remote sensing data across each spectral band helps enhance detail in the resulting image.

[0047] In step 104, each pseudo-emissivity is normalized to eliminate the effects of external factors on the image's radiometric characteristics, such as sensor state variations, solar radiation, atmospheric changes, and seasonal variations in ground objects. This helps to enhance the weather displayed in the resulting meteorological cloud map. It should be understood that the normalized pseudo-emissivity information can better indicate brightness temperature differences across different regions.

[0048] In step 105, RGB color map processing can be performed based on the normalized pseudo-emissivity information corresponding to each segment of the long-wave infrared remote sensing data to obtain a color cloud map. Since the processing from steps 101 to 104 obtains normalized emissivity values corresponding to the long-wave infrared remote sensing data of multiple spectral segments, this can also be understood as obtaining normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of multiple spectral segments. This helps make the resulting color cloud map more distinct for different weather conditions, thereby helping weather forecasters use the generated color cloud map to make more accurate forecasts.

[0049] For example, in some spectral bands, the emissivity of small particle targets around 10 μm, such as fog droplets and dust, is low, while the emissivity of cloud droplets around 100 μm is high. Therefore, it is possible to effectively distinguish between target areas such as low clouds, heavy fog, dust, and deserts, as well as high clouds. In some spectral bands, the emissivity of water surfaces and dense clouds is almost identical in these two spectral bands, allowing the distinction between sea (cloud) and land targets. Furthermore, it is sensitive to the water vapor content along the light transmission path, allowing information on atmospheric water vapor to be obtained. In some spectral bands, it is sensitive to the length of the light path due to the absorption of water vapor and carbon dioxide. The differences in the incidence of land and sea also allow for effective distinction between land and sea targets. Therefore, generating meteorological cloud maps using normalized pseudo-emissivity information corresponding to long-wave infrared remote sensing data in multiple spectral bands helps to make the final meteorological cloud map more clearly distinguish between different weather conditions.

[0050] As can be seen, in the present disclosure, by acquiring long-wave infrared remote sensing data from multiple spectral bands and determining the brightness temperature information corresponding to each spectral band of long-wave infrared remote sensing data, the pseudo-emissivity corresponding to each spectral band of long-wave infrared remote sensing data can be determined based on the brightness temperature information corresponding to each spectral band of long-wave infrared remote sensing data. After normalizing the pseudo-emissivity, the normalized pseudo-emissivity information corresponding to each spectral band of long-wave infrared remote sensing data can be obtained, thereby generating a color cloud map for indicating meteorological information. This method facilitates the use of the generated color cloud map to effectively distinguish between meteorological conditions such as the surface, high clouds, and low clouds. This helps weather forecasters understand the actual weather conditions and provides high-frequency and effective information. Moreover, compared to traditional single-spectral band brightness temperature grayscale maps, the color cloud map of this solution more prominently displays image details, making it more convenient for weather forecasters to make weather forecasts.

[0051] In some embodiments, the wavelengths included in the multiple spectral bands of long-wave infrared remote sensing data include: 8.7um, 10.8um, 12.0um and 13.4um.

[0052] For example, the wavelengths selected for long-wave infrared remote sensing data in multiple spectral bands can fall within the atmospheric window. This means that the atmosphere absorbs and scatters radiation at these wavelengths less effectively. Therefore, these wavelengths can penetrate the atmosphere better, making the radiation signals received from satellite sensors more representative of the true surface conditions. The wavelengths selected in this disclosure meet these conditions, thus facilitating the acquisition of better color images.

[0053] At the same time, in the process of obtaining the pseudo-emissivity using the inversion algorithm, since multiple bands are set in the present disclosure, the differences between different bands can be used to improve the accuracy of surface parameter inversion, thereby helping to more accurately obtain the pseudo-emissivity corresponding to the long-wave infrared remote sensing data of each spectral band.

[0054] It should be understood that in the 8.7um spectral band, the emissivity of small particle targets around 10um, such as fog droplets and dust, is low, while the emissivity of cloud droplets around 100um is high. Therefore, it is possible to effectively distinguish target areas such as low clouds, heavy fog, dust, deserts, and high clouds; the wavelengths of the 10.8um and 12.0um reference spectral bands are close, and in the spectral range of 10.8um to 12.0um, the emissivity of water surfaces and dense clouds is almost the same in these two spectral bands, so sea (cloud) and land targets can be distinguished; at the same time, it is more sensitive to the water vapor content in the light transmission path, and can obtain information on atmospheric water vapor; in the 13.4um spectral band, it is affected by the absorption of water vapor and carbon dioxide, and is sensitive to the length of the path traveled by light; there are differences in the occurrence rates of sea and land, so sea and land targets can be effectively distinguished.

[0055] In some embodiments, the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of any spectral band may be determined by:

[0056] Determining first numerical information based on a blackbody thermal radiation formula, brightness temperature information corresponding to the long-wave infrared remote sensing data of the spectral band, and wavelength information corresponding to the long-wave infrared remote sensing data of the spectral band;

[0057] Determining second numerical information based on a blackbody thermal radiation formula, wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and brightness temperature information corresponding to the long-wave infrared remote sensing data of the target spectrum segment;

[0058] Based on the first numerical information and the second numerical information, pseudo emissivity information corresponding to the long-wave infrared remote sensing data of the spectral band is determined.

[0059] Here, the target spectrum band long-wave infrared remote sensing data is data from multiple spectrum bands long-wave infrared remote sensing data;

[0060] The blackbody thermal radiation formula can include:

[0061] Among them, c can be used to indicate the speed of light in vacuum, k can be used to indicate the Boltzmann constant, h can be used to indicate the Planck constant, λ can be used to indicate the wavelength, and BT can be used to indicate the brightness temperature.

[0062] As an example, when determining the pseudo-emissivity corresponding to the long-wave infrared remote sensing data of a certain spectral band, it is necessary to determine the brightness temperature information, wavelength information, and wavelength information of the long-wave infrared remote sensing data of this spectral band, and then the blackbody radiation formula can be used to determine the pseudo-emissivity corresponding to this segment of long-wave infrared remote sensing data.

[0063] As an example, the blackbody radiation formula can be understood as:

[0064] Here, PE can be understood as pseudo emissivity (PE), the numerator is the single-channel thermal radiation energy observed by the satellite, and the denominator is the standard blackbody thermal radiation energy calculated based on the brightness temperature of the reference channel. B can be understood as the blackbody radiation formula, with the input variables being wavelength λ and brightness temperature BT. λ is the central wavelength of the observation channel, and λ0 is the central wavelength of the reference channel.

[0065] In some embodiments, the wavelength of the target spectrum long-wave infrared remote sensing data may be 12.0 um.

[0066] For example, in the 10.8um-12.0um reference spectrum, the emissivity of water surfaces and dense clouds is nearly identical, making it possible to distinguish between sea (cloud) and land targets. Furthermore, the sensor is sensitive to the water vapor content along the light's propagation path, enabling information on atmospheric water vapor to be obtained. When selecting 25, the reference spectrum can be set to 12.0um.

[0067] In some embodiments, step 104 (normalizing each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to each spectral band of long-wave infrared remote sensing data) may specifically include:

[0068] For the first pseudo emissivity information, determining a maximum pseudo emissivity value and a minimum pseudo emissivity value in the first pseudo emissivity information; and determining that a normalized value corresponding to the maximum pseudo emissivity value is 1, and a normalized value corresponding to the minimum pseudo emissivity value is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in the pseudo emissivity information;

[0069] For any pseudo emissivity value in the first pseudo emissivity information except the maximum pseudo emissivity value and the minimum pseudo emissivity value, a normalized value corresponding to the any pseudo emissivity value is determined based on a proportional relationship with the maximum pseudo emissivity value and the minimum pseudo emissivity value.

[0070] As an example, the pseudo emissivity can be normalized so that its value is distributed between 0 and 1. In the normalization process, there are many specific methods to choose from. This solution adopts a 2% enhancement solution, which can be normalized more efficiently. For example, for each pseudo emissivity information, the pseudo emissivity values corresponding to the 1% and 99% statistical quantiles can be statistically calculated and recorded as PE respectively. λ1 and PE λ99 , which is used as the normalized minimum value of 0 and maximum value of 1, and other values are converted to between 0 and 1 according to the proportion. The normalized pseudo emissivity (NPE) is calculated as follows:

[0071]

[0072] Here, PE λ It can represent any pseudo-emissivity in the pseudo-emissivity information corresponding to the long-wave infrared remote sensing data. It should be understood that a single long-wave infrared remote sensing data segment can correspond to an image, or to a region (meteorological observation area). Pseudo-emissivity information can also correspond to a region. Using generated images to distinguish different regions, it can be understood that different regions in the image correspond to different locations, and thus, different pseudo-emissivity values correspond to different regions of the image. This is due to the different radiant brightness temperatures in different locations.

[0073] After normalization, the normalized pseudo-emissivity of each meteorological observation area can be better distinguished, thereby helping to generate meteorological cloud maps more accurately.

[0074] In some embodiments, the plurality of spectral long-wave infrared remote sensing data includes: first long-wave infrared remote sensing data, second long-wave remote sensing data, and third long-wave remote sensing data; and generating a color cloud map for indicating meteorological information based on normalized pseudo-emissivity information corresponding to each segment of the long-wave infrared remote sensing data includes:

[0075] Generate a red image based on normalized pseudo emissivity information corresponding to the first long-wave infrared remote sensing data;

[0076] Generate a green image based on the normalized pseudo emissivity information corresponding to the second long-wave infrared remote sensing data;

[0077] Generate a blue image based on the normalized pseudo-emissivity information corresponding to the third long-wave infrared remote sensing data;

[0078] Generate a color cloud image based on the red image, green image, and blue image.

[0079] As an example, the meteorological characteristics indicated by infrared remote sensing data in each band are different. Therefore, infrared remote sensing data in different bands can be used to generate corresponding images with different colors, and finally synthesized, so that the meteorological characteristics represented by the final color cloud map can be more obvious, which is beneficial for meteorological forecasters to observe and determine the meteorological conditions.

[0080] In some implementations, the wavelength of the first long-wave infrared remote sensing data is 13.4 μm;

[0081] The wavelength of the second long-wave infrared remote sensing data is 8.7um;

[0082] The wavelength of the third long-wave infrared remote sensing data is 10.8um.

[0083] For example, the first long-wave infrared remote sensing data, with a wavelength of 13.4 μm, can effectively distinguish between land and sea. The long-wave infrared remote sensing data, with a wavelength of 8.7 μm, can effectively distinguish between low clouds, fog, dust, and deserts. And the long-wave infrared remote sensing data, with a wavelength of 10.8 μm, can effectively distinguish between sea (clouds) and land targets. These three different wavelengths of long-wave infrared remote sensing data have their own distinguishing characteristics, making it easier for the final color cloud map to distinguish various meteorological features.

[0084] As an example, in the process of generating a color cloud, the data type can be converted from floating point to unsigned integer data, that is, an unsigned 8-bit integer from 0 to 255, with red (R) as NPE. 13.5 , green (G) is NPE 8.7 , blue (B) is NPE 10.8 The sequence is synthesized into an RGB image.

[0085] Do the following:

[0086] R = uint8(NPE 13.4 *255)

[0087] G = uint8(NPE 8.7 *255)

[0088] B = uint8 (NPE 10.8 *255)

[0089] Where uint8 represents an unsigned 8-bit integer. By synthesizing the image according to the above color setting scheme, a color cloud map can be obtained.

[0090] In order to better understand the concept of the present disclosure, the following describes the differences between the present disclosure and the related art. Specifically:

[0091] Currently, infrared brightness temperature (temperature) data at a single wavelength is typically converted into a grayscale (0-255) output image. Because the human eye has limited ability to distinguish grayscale, typically only able to discern a dozen or so grayscale levels, brightness temperature grayscale images are not very effective for distinguishing between cloud areas and the ground surface. Furthermore, Earth experiences seasonal changes, with temperature distributions varying by tens of degrees between winter and summer. Consequently, the brightness temperature to grayscale conversion scheme exhibits winter-summer differences. The same grayscale often appears different in winter and summer, leading to misidentification of cloud and clear sky areas.

[0092] One improvement to the related technology is to use color mapping to convert the low-temperature range into color, thereby effectively distinguishing the low-temperature target area. Another solution is to map the entire temperature range into color, such as rainbow colors, to distinguish the spatial distribution of temperature from a multi-color perspective. However, the same color represents different temperatures in winter and summer, and thus cannot effectively distinguish between cloud areas and surface information. For example, in foggy and low-cloud areas, due to the low cloud layer, its surface temperature is often similar to the temperature of the surrounding clear sky surface and has the same settings. In this case, the brightness temperature image of a single spectral band cannot distinguish between low clouds and heavy fog.

[0093] That is, the related art cannot effectively distinguish between various types of weather, while the method disclosed in the present invention, by utilizing a combination of multi-spectral infrared data, can effectively distinguish between high clouds, low clouds, clear land, and clear ocean.

[0094] Furthermore, in order to better understand the concept of the present disclosure, illustrations may be used for description.

[0095] First, if Figures 2A to 2D As shown in the present disclosure, satellite infrared spectrum remote sensing data can be collected and sorted, including 8.7um, 10.8um, 12.0um and 13.4um spectrum remote sensing data. The raw data is converted into radiance temperature (Brightness Temperature, BT), unit Kelvin (K). These spectrum BT can be represented by 8.7 , BT 10.8 , BT 12.0 and BT 13.4 The actual observation value of the observation data, and BT can be generated separately 8.7 , BT 10.8 , BT 12.0 and BT 13.4 The brightness temperature difference diagram of the observation data can be seen as follows Figures 2A to 2D As shown, Figure 2A Can be used to indicate BT 8.7 The corresponding brightness temperature distribution map, Figure 2B Can be used to indicate BT10.8 The corresponding brightness temperature distribution map, Figure 2C Can be used to indicate BT 12.0 The corresponding brightness temperature distribution map, Figure 2D Can be used to indicate BT 13.4 The corresponding brightness temperature distribution map.

[0096] Continue to combine Figures 3A-3C As shown in the figure, since the emissivity is generally the ratio of the single-channel thermal radiation energy observed by the satellite to the thermal radiation energy of the standard black body, the pseudo-emissivity adopts the same definition, but the standard black body thermal radiation temperature uses the reference channel observation brightness temperature. Specifically, the pseudo-emissivity calculation formula is:

[0097]

[0098] Where PE is the pseudo emissivity (PE), the numerator is the single-channel thermal radiation energy observed by the satellite, and the denominator is the standard blackbody thermal radiation energy calculated based on the brightness temperature of the reference channel. B is the blackbody radiation formula, and the input variables are wavelength λ and brightness temperature BT; λ is the central wavelength of the observation channel, and λ0 is the central wavelength of the reference channel. Experiments have shown that using the 12.0 μm channel as the reference channel works best, so λ0 = 12 μm can be used in this solution. BT λ is the λ channel brightness temperature observed by satellite, is the brightness temperature of the λ0 channel observed by the satellite.

[0099] The formula for blackbody radiation is:

[0100]

[0101] Where c is the speed of light in vacuum, k is the Boltzmann constant, h is the Planck constant, λ is the wavelength, and BT is the temperature.

[0102] According to the above formula, the pseudo emissivity of 8.5um, 10.8um and 13.4um can be calculated respectively, which is recorded as PE 8.5 PE 10.8 and PE 13.4 .

[0103] The principle can be understood as follows: the pseudo-emissivity calculation assumes that the emissivity of the reference channel is 1. At the same time, it assumes that the influence of atmospheric radiation on the light propagation path is small and does not affect the analysis results. In the 8.7um spectral band, the emissivity of small particle targets around 10um, such as fog droplets and dust, is low, while the emissivity of cloud droplets around 100um is high. Therefore, it can effectively distinguish target areas such as low clouds, heavy fog, dust, deserts, and high clouds; in the 10.8um spectral band, due to the wavelength close to the 12.0um reference spectral band, the emissivity of the water surface and dense clouds is almost the same in these two spectral bands, so it can distinguish between sea (cloud) and land targets; at the same time, it is more sensitive to the water vapor content in the light transmission path, and can obtain information on atmospheric water vapor; in the 13.4um spectral band, it is affected by the absorption of water vapor and carbon dioxide, and is sensitive to the length of the path traveled by light; there are differences in the occurrence rates of sea and land, and it can also effectively distinguish between sea and land targets.

[0104] The pseudo-emission rate distribution of the three spectral bands is as follows Figures 3A-3C As shown, Figure 3A It can be understood as PE 8.5 The corresponding image, Figure 3B It can be understood as PE 10.8 The corresponding image, Figure 3C It can be understood as PE 13.4 The corresponding image.

[0105] It can be seen that the details of the pseudo emissivity image are effectively enhanced compared to the original brightness temperature distribution image.

[0106] Next, pseudo emission rate normalization can be performed to normalize the pseudo emission rate.

[0107] Specifically:

[0108] The pseudo-emission rate is normalized so that its value is distributed between 0 and 1. There are many specific methods to choose from. This scheme adopts a 2% enhancement scheme. The pseudo-emission rate values corresponding to the 1% and 99% statistical quantiles are statistically calculated and recorded as PE λ1 and PE λ99 , which is used as the normalized minimum value of 0 and maximum value of 1, and other values are converted to between 0 and 1 according to the proportion. The normalized pseudo emissivity (NPE) is calculated as follows:

[0109]

[0110] The normalized pseudo emissivity of 8.7um, 10.8um and 13.4um can be processed separately to obtain the normalized pseudo emissivity, which are recorded as NPE respectively. 8.7 、NPE 10.8 , and NPE 13.4 .

[0111] Furthermore, RGB color composite image output can be performed, specifically:

[0112] Convert the data type from floating point to unsigned integer data, that is, an unsigned 8-bit integer between 0 and 255; red (R) indicates NPE 13.5 , green (G) is NPE 8.7 , blue (B) is NPE 10.8 The sequence is synthesized into an RGB image.

[0113] Do the following:

[0114] R = uint8(NPE 13.4 *255)

[0115] G = uint8(NPE 8.7 *255)

[0116] B = uint8 (NPE 10.8 *255)

[0117] Where uint8 represents an unsigned 8-bit integer, and the image is synthesized according to the above color setting scheme.

[0118] Combine Figures 4A-4D visible, Figure 4A It can be understood as a schematic diagram of the green component. Figure 4B It can be understood as a schematic diagram of the blue component. Figure 4C It can be understood as a red classification diagram, and Figure 4D Then we can understand the synthesized color cloud map. It can be seen that the color cloud map can effectively distinguish the surface, high clouds, low clouds and other meteorological phenomena.

[0119] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for generating cloud images using multi-spectral long-wave infrared remote sensing data. Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0120] like Figure 5 As shown, the apparatus for generating a cloud map using multi-spectral long-wave infrared remote sensing data of this embodiment includes: an acquisition unit 501, configured to acquire a plurality of spectral long-wave infrared remote sensing data;

[0121] The first determining unit 502 is configured to determine brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0122] The second determining unit 503 is configured to determine the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0123] The calculation unit 504 is used to perform normalization processing on each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band;

[0124] The generating unit 505 is configured to generate a color cloud map for indicating meteorological information according to the normalized pseudo emissivity information corresponding to each segment of the long-wave infrared remote sensing data.

[0125] In this embodiment, the specific processing of the acquisition unit 501, the first determination unit 502, the second determination unit 503, the calculation unit 504 and the generation unit 505 of the device for generating a cloud map using multi-spectral long-wave infrared remote sensing data and the technical effects thereof can be referred to in the respective Figure 1 The relevant descriptions of step 101, step 102, step 103, step 104 and step 105 in the corresponding embodiment are not repeated here.

[0126] In some embodiments, the wavelengths included in the multiple spectral bands of long-wave infrared remote sensing data include: 8.7um, 10.8um, 12.0um and 13.4um.

[0127] In some embodiments, the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of any spectral band is determined by:

[0128] Determining first numerical information based on a blackbody thermal radiation formula, brightness temperature information corresponding to the long-wave infrared remote sensing data of the spectral band, and wavelength information corresponding to the long-wave infrared remote sensing data of the spectral band;

[0129] Determining second numerical information based on a blackbody thermal radiation formula, wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and brightness temperature information corresponding to the long-wave infrared remote sensing data of the target spectrum segment;

[0130] Determining pseudo emissivity information corresponding to the long-wave infrared remote sensing data of the spectral band based on the first numerical information and the second numerical information;

[0131] The target spectrum long-wave infrared remote sensing data is data from the plurality of spectrum long-wave infrared remote sensing data;

[0132] The blackbody thermal radiation formula includes:

[0133] Among them, c is used to indicate the speed of light in vacuum, k is used to indicate the Boltzmann constant, h is used to indicate the Planck constant, λ is used to indicate the wavelength, and BT is used to indicate the brightness temperature.

[0134] In some embodiments, the wavelength of the target spectrum long-wave infrared remote sensing data is 12.0 um.

[0135] In some embodiments, normalizing each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to each spectral band of long-wave infrared remote sensing data includes:

[0136] For the first pseudo emissivity information, determining a maximum pseudo emissivity value and a minimum pseudo emissivity value in the first pseudo emissivity information; and determining that a normalized value corresponding to the maximum pseudo emissivity value is 1, and a normalized value corresponding to the minimum pseudo emissivity value is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in the pseudo emissivity information;

[0137] For any pseudo emissivity value in the first pseudo emissivity information except the maximum pseudo emissivity value and the minimum pseudo emissivity value, a normalized value corresponding to the any pseudo emissivity value is determined based on a proportional relationship with the maximum pseudo emissivity value and the minimum pseudo emissivity value.

[0138] In some embodiments, the plurality of spectral long-wave infrared remote sensing data include: first long-wave infrared remote sensing data, second long-wave remote sensing data, and third long-wave remote sensing data; and generating a color cloud map for indicating meteorological information based on normalized pseudo-emissivity information corresponding to each segment of the long-wave infrared remote sensing data includes:

[0139] generating a red image based on normalized pseudo emissivity information corresponding to the first long-wave infrared remote sensing data;

[0140] generating a green image based on normalized pseudo emissivity information corresponding to the second long-wave infrared remote sensing data;

[0141] generating a blue image based on normalized pseudo emissivity information corresponding to the third long-wave infrared remote sensing data;

[0142] The color cloud map is generated based on the red image, the green image, and the blue image.

[0143] In some embodiments, the wavelength of the first long-wave infrared remote sensing data is 13.4 μm;

[0144] The wavelength of the second long-wave infrared remote sensing data is 8.7 μm;

[0145] The wavelength of the third long-wave infrared remote sensing data is 10.8 um.

[0146] Please refer to Figure 6 , Figure 6 The present invention illustrates an exemplary system architecture in which the method for generating cloud images using multi-spectral long-wave infrared remote sensing data according to an embodiment of the present disclosure can be applied.

[0147] like Figure 6 As shown, the system architecture may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 is used to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. The network 604 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0148] Terminal devices 601, 602, and 603 can interact with server 605 via network 604 to receive or send messages, etc. Various client applications can be installed on terminal devices 601, 602, and 603, such as web browser applications, search applications, and news and information applications. The client applications in terminal devices 601, 602, and 603 can receive user instructions and perform corresponding functions based on the user instructions, such as adding corresponding information to the message based on the user's instructions.

[0149] Terminal devices 601, 602, and 603 can be hardware or software. When terminal devices 601, 602, and 603 are hardware, they can be various electronic devices with display screens and support web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers, and desktop computers, etc. When terminal devices 601, 602, and 603 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, software or software modules used to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0150] The server 605 may be a server that provides various services, such as receiving information acquisition requests sent by the terminal devices 601, 602, and 603, acquiring display information corresponding to the information acquisition requests through various means according to the information acquisition requests, and sending relevant data of the display information to the terminal devices 601, 602, and 603.

[0151] It should be noted that the method for generating a cloud map using multi-spectral long-wave infrared remote sensing data provided in the embodiment of the present disclosure can be executed by a terminal device, and accordingly, the apparatus for generating a cloud map using multi-spectral long-wave infrared remote sensing data can be provided in the terminal devices 601, 602, and 603. In addition, the method for generating a cloud map using multi-spectral long-wave infrared remote sensing data provided in the embodiment of the present disclosure can also be executed by a server 605, and accordingly, the apparatus for generating a cloud map using multi-spectral long-wave infrared remote sensing data can be provided in the server 605.

[0152] It should be understood that Figure 6 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0153] Reference below Figure 7 , which shows an electronic device (eg Figure 6 The terminal device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0154] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. Various programs and data required for the operation of the electronic device 700 are also stored in the RAM 703. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0155] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0156] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0157] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0158] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0159] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0160] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device is enabled to: obtain long-wave infrared remote sensing data of multiple spectral bands; determine brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; determine pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; normalize each pseudo emissivity information respectively to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band; and generate a color cloud map for indicating meteorological information based on the normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band.

[0161] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0163] The units described in the embodiments of the present disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the acquisition unit may also be described as a "unit for acquiring long-wave infrared remote sensing data in multiple spectral bands."

[0164] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0165] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0166] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0167] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0168] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for generating cloud images using multi-spectral long-wave infrared remote sensing data, characterized in that: The method comprises: Acquire long-wave infrared remote sensing data in multiple spectral bands; Determine the brightness temperature information corresponding to the long-wave infrared remote sensing data in each spectral band; Based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band, the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band is determined; Each pseudo-emissivity information is normalized respectively to obtain the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band; Generate a color cloud map for indicating meteorological information based on the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band; Among them, the wavelengths of the long-wave infrared remote sensing data in multiple spectral bands include: 8.7um, 10.8um, 12.0um and 13.4um; The pseudo emissivity information corresponding to the long-wave infrared remote sensing data of any spectral band is determined by the following method: Determining first numerical information based on a blackbody thermal radiation formula, brightness temperature information corresponding to the long-wave infrared remote sensing data of the spectral band, and wavelength information corresponding to the long-wave infrared remote sensing data of the spectral band; Determining second numerical information based on a blackbody thermal radiation formula, wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and brightness temperature information corresponding to the long-wave infrared remote sensing data of the target spectrum segment; Determining pseudo emissivity information corresponding to the long-wave infrared remote sensing data of the spectral band based on the first numerical information and the second numerical information; The target spectrum long-wave infrared remote sensing data is data from the plurality of spectrum long-wave infrared remote sensing data; The blackbody thermal radiation formula includes: , Among them, c is used to indicate the speed of light in vacuum, k is used to indicate the Boltzmann constant, and h is used to indicate the Planck constant. Used to indicate wavelength, Used to indicate brightness temperature.

2. The method according to claim 1, characterized in that The wavelength of the target spectrum long-wave infrared remote sensing data is 12.0 um.

3. The method according to claim 1, characterized in that The normalization processing is performed on each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band, including: For the first pseudo emissivity information, determining a maximum pseudo emissivity value and a minimum pseudo emissivity value in the first pseudo emissivity information; and determining that a normalized value corresponding to the maximum pseudo emissivity value is 1, and a normalized value corresponding to the minimum pseudo emissivity value is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in the pseudo emissivity information; For any pseudo emissivity value in the first pseudo emissivity information except the maximum pseudo emissivity value and the minimum pseudo emissivity value, a normalized value corresponding to the any pseudo emissivity value is determined based on a proportional relationship with the maximum pseudo emissivity value and the minimum pseudo emissivity value.

4. The method according to claim 1, wherein The plurality of spectral long-wave infrared remote sensing data include: first long-wave infrared remote sensing data, second long-wave remote sensing data, and third long-wave remote sensing data; and generating a color cloud map for indicating meteorological information based on normalized pseudo-emissivity information corresponding to each segment of the long-wave infrared remote sensing data includes: generating a red image based on normalized pseudo emissivity information corresponding to the first long-wave infrared remote sensing data; generating a green image based on normalized pseudo emissivity information corresponding to the second long-wave infrared remote sensing data; generating a blue image based on normalized pseudo emissivity information corresponding to the third long-wave infrared remote sensing data; The color cloud map is generated based on the red image, the green image, and the blue image.

5. The method according to claim 4, characterized in that The wavelength of the first long-wave infrared remote sensing data is 13.4 μm; The wavelength of the second long-wave infrared remote sensing data is 8.7 μm; The wavelength of the third long-wave infrared remote sensing data is 10.8 um.

6. A device for generating cloud images using multi-spectral long-wave infrared remote sensing data according to any one of claims 1 to 5, characterized in that: The device comprises: An acquisition unit, used for acquiring long-wave infrared remote sensing data of multiple spectral bands; The first determining unit is used to determine the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; The second determining unit is configured to determine the pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; The calculation unit is used to perform normalization processing on each pseudo emissivity information to obtain normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band; A generating unit, configured to generate a color cloud map for indicating meteorological information based on normalized pseudo emissivity information corresponding to each segment of long-wave infrared remote sensing data; Among them, the wavelengths included in the long-wave infrared remote sensing data of multiple spectral bands include: 8.7um, 10.8um, 12.0um and 13.4um.

7. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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