Method and device for generating cloud picture by using multispectral long-wave infrared remote sensing data

Abstract

The embodiment of the invention discloses a method and device for generating a cloud picture by using multi-spectral long-wave infrared remote sensing data and electronic equipment. A specific embodiment of the method comprises the following steps: acquiring long-wave infrared remote sensing data of a plurality of spectral segments, determining brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral segment, and determining pseudo-emissivity corresponding to the long-wave infrared remote sensing data of each spectral segment based on the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral segment; performing normalization processing on the pseudo-emissivity to obtain normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectrum segment; and thus, a color cloud picture for indicating weather information is generated. In this way, the generated color cloud picture can be utilized to effectively distinguish the ground surface, the high cloud, the low cloud and other areas. Therefore, high-frequency effective information can be provided, and weather forecast personnel can master the actual weather condition conveniently.

Description

Technical Field

[0001] The present invention 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 an important data source for numerical weather forecasting in the current meteorological field, and is also an important reference for forecasters to obtain weather conditions. Geosynchronous orbit satellites (geostationary orbit satellites) can achieve 24-hour uninterrupted earth observation and obtain earth observation data in the solar reflection band and thermal infrared band.

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

[0004] However, the weather cloud map generation solutions currently 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 introduce concepts in a brief form, which 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, the embodiments of the present disclosure provide a method and device 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] According to the normalized pseudo-emissivity information corresponding to each segment of long-wave infrared remote sensing data, a color cloud map for indicating meteorological information is generated.

[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 determination 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 determination unit is used 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] A calculation unit is used to 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;

[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 section 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 a cloud map 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 disclosed embodiment discloses a method and device for generating cloud maps using multi-spectral long-wave infrared remote sensing data. The method can obtain multiple spectral long-wave infrared remote sensing data and determine the brightness temperature information corresponding to each spectral long-wave infrared remote sensing data. Based on the brightness temperature information corresponding to each spectral long-wave infrared remote sensing data, the pseudo-emissivity corresponding to each spectral long-wave infrared remote sensing data is determined; and after normalizing the pseudo-emissivity, the normalized pseudo-emissivity information corresponding to each spectral long-wave infrared remote sensing data is obtained; 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 the surface, high clouds, low clouds and other areas. This is conducive to weather forecasters to grasp the actual weather conditions and can provide high-frequency effective information. Moreover, compared with the traditional single-spectral brightness temperature grayscale map, the color cloud map of this scheme has more prominent display of the detailed information of the image; it is also more conducive to meteorological forecasters to make weather forecasts. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0022] Figure 1 It is a flowchart 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 by 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 It is a structural schematic 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 It 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] Embodiments of the present disclosure will be described in more detail below 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 being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes 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] The term "including" and its variations used herein are open inclusions, 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"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0032] It should be noted that the concepts such as "first" and "second" mentioned in the present 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 "plurality" 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, it 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, by acquiring long-wave infrared remote sensing data of multiple spectral bands, generate 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 surface, high cloud, low cloud and other meteorological conditions. This helps weather forecasters to understand the actual weather conditions and can provide high-frequency effective information. Moreover, compared with the traditional single-spectral brightness temperature grayscale map, the color cloud map of this solution has a more prominent display of the detailed information of the image; it is also more conducive to meteorological forecasters to make 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 comprises the following steps:

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

[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 according to the normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band.

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

[0043] As an 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 the 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 as an ideal black body radiation source at a specific temperature. In the field of remote sensing, this concept is particularly important because remote sensing sensors receive radiation energy from the earth's surface or other celestial bodies. After these radiation energies are converted into electrical signals, they can be used to calculate and analyze the temperature and other physical properties of the surface or atmosphere. It should be understood that the radiance temperature can be calculated using Planck's law. It should be understood that the unit of radiance temperature can be Kelvin (K).

[0046] The pseudo-emissivity in step 103 may refer to the ratio of the radiation capacity of an actual object to the radiation capacity of a black body at the same temperature in radiation heat transfer. The calculation of pseudo-emissivity using radiant brightness temperature is an inversion process. By calculating and determining the pseudo-emissivity corresponding to the long-wave infrared remote sensing data of each spectral band, the details of the final generated image can be more prominent.

[0047] In step 104, each pseudo-emissivity is normalized, which helps to eliminate the influence of external factors such as sensor state difference, solar radiation, atmospheric environment change, and seasonal change of ground objects on the image radiation characteristics. This helps to make the weather indicated by the finally generated weather cloud map more prominent. It should be understood that the normalized pseudo-emissivity information can better indicate the brightness temperature difference of different regions.

[0048] In step 105, RGB color map processing can be performed according to the normalized pseudo emissivity information corresponding to each segment of long-wave infrared remote sensing data, thereby obtaining a color cloud map. Since the normalized values ​​of emissivity corresponding to the long-wave infrared remote sensing data of multiple spectral segments can be obtained through the processing process of steps 101 to 104, it can also be understood as obtaining the normalized pseudo emissivity information corresponding to the long-wave infrared remote sensing data of multiple spectral segments, which helps to make the final generated color cloud map more distinguishable for different meteorological conditions, thereby helping meteorological forecasters to 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 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; in some spectral bands, the emissivity of water surface 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 of the light transmission path, and the information of atmospheric water vapor can be obtained; in some spectral bands, it is affected by the absorption of water vapor and carbon dioxide, and is sensitive to the length of the path that the light passes; there are differences in the occurrence rate of sea and land, and sea and land targets can also be effectively distinguished. Therefore, generating a meteorological cloud map through the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of multiple spectral bands will help make the final meteorological cloud map more obvious in distinguishing different meteorological conditions.

[0050] It can be seen that in the present disclosure, by acquiring multiple spectral long-wave infrared remote sensing data and determining the brightness temperature information corresponding to each spectral long-wave infrared remote sensing data, the pseudo-emissivity corresponding to each spectral long-wave infrared remote sensing data can be determined based on the brightness temperature information corresponding to each spectral long-wave infrared remote sensing data; and after normalizing the pseudo-emissivity, the normalized pseudo-emissivity information corresponding to each spectral long-wave infrared remote sensing data can be obtained; 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 meteorological conditions such as the surface, high clouds, and low clouds. This is helpful for weather forecasters to grasp the actual weather conditions and can provide high-frequency effective information. Moreover, compared with the traditional single-spectral brightness temperature grayscale map, the color cloud map of this scheme has a more prominent display of the detailed information of the image; it is also more conducive to meteorological forecasters to make meteorological 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] As an example, the wavelengths selected for the long-wave infrared remote sensing data of multiple spectral bands may be located within the atmospheric window, that is, the atmosphere has less radiation absorption and scattering of these wavelengths, so these wavelengths can penetrate the atmosphere better, so that the radiation signals received from the satellite sensor can better represent the real situation of the surface. The wavelengths selected in the present disclosure meet the above conditions, thereby helping to better obtain 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, which helps 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 relatively low, while the emissivity of cloud droplets around 100um is relatively 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 within the spectral range of 10.8um to 12.0um, the emissivity of the water surface 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 the 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 any spectral band of long-wave infrared remote sensing data may be determined in the following manner:

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

[0057] Determine the second numerical information based on the blackbody thermal radiation formula, the wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and the 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 spectral band long-wave infrared remote sensing data is data from multiple spectral 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 of the long-wave infrared remote sensing data of the spectral band, and the wavelength information of the reference band. Then, the blackbody radiation formula can be used to determine the pseudo-emissivity corresponding to the long-wave infrared remote sensing data of this spectral band.

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

[0064] Among them, 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, and the input variables can be wavelength λ and brightness temperature BT respectively; λ can be the central wavelength of the observation channel, and λ0 can be the central wavelength of the reference channel.

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

[0066] As an example, in the 10.8um-12.0um reference spectrum, the emissivity of the water surface and dense clouds is almost the same in these two spectrums, 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 the information of atmospheric water vapor can be obtained. When 25 is selected, the reference spectrum can be determined as 12.Oum.

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

[0068] For the first pseudo emissivity information, determine the maximum pseudo emissivity value and the minimum pseudo emissivity value in the first pseudo emissivity information; and determine that the normalized value corresponding to the maximum pseudo emissivity value is 1, and the normalized value corresponding to the minimum pseudo emissivity is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in each 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 according to 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 scheme adopts a 2% enhancement scheme, which can be more efficiently normalized. 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 calculation formula of normalized pseudo emissivity (NPE) is as follows:

[0071]

[0072] Here, PE λ It can represent any pseudo emissivity in the pseudo emissivity information corresponding to the spectral long-wave infrared remote sensing data. It should be understood that a spectral long-wave infrared remote sensing data can correspond to an image, or be understood as corresponding to a region (meteorological observation area). The pseudo emissivity information can also correspond to a region. To distinguish by generating an image, it can be understood that different regions in the image correspond to different places, and different regions of the image correspond to different pseudo emissivity values, the reason for which is that the radiant brightness temperature of different places is different.

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

[0074] 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, based on normalized pseudo-emissivity information corresponding to each segment of the long-wave infrared remote sensing data, generating a color cloud map for indicating meteorological information, including:

[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 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, the green image, and the 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 then synthesized. This can make the meteorological characteristics represented by the final color cloud map more obvious, which is beneficial for meteorological forecasters to observe and determine the meteorological situation.

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

[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] As an example, the wavelength of the first long-wave infrared remote sensing data is 13.4um, which can effectively distinguish between land and sea, while the wavelength of the long-wave infrared remote sensing data is 8.7um, which can effectively distinguish between low clouds, fog, dust, desert, etc.; and the wavelength of the long-wave infrared remote sensing data is 10.8um, which can effectively distinguish between sea (clouds) and land targets. At this time, the three different wavelengths of long-wave infrared remote sensing data have their own distinguishing characteristics, which will help the final color cloud map to more easily distinguish various meteorological features.

[0084] As an example, in the process of forming a color cloud map, the data type can be converted from floating point data to unsigned integer data, that is, an unsigned 8-bit integer of 0-255, and red (R) is 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 as follows:

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

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

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

[0089] In the formula, 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 differences between the present disclosure and the related art are described below, specifically:

[0091] At present, infrared brightness temperature (temperature) data on a single wavelength is usually converted into grayscale (0-255) output images. Since the human eye has limited ability to distinguish grayscale, it can only distinguish a dozen grayscale levels. Therefore, the grayscale image of brightness temperature is not very effective in distinguishing cloud areas, ground surfaces, etc. In addition, the earth has four seasons, and the temperature distribution range in winter and summer differs by dozens of degrees. Therefore, the conversion scheme of brightness temperature to grayscale has differences in winter and summer. The same grayscale is often different in winter and summer, resulting in misjudgment of cloud areas and clear sky areas.

[0092] An improvement scheme of the related technology is: for the low temperature range, use color mapping to convert the low temperature range into color, so as to achieve effective distinction of the low temperature target area. Another scheme is to map the entire temperature range into color, such as rainbow color, 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 setting. 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 weather conditions, while the method disclosed herein, using a combination of multi-spectral infrared data, can effectively distinguish between high clouds, low clouds, clear sky over land, and clear sky over ocean.

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

[0095] First, if FIG. 2A to FIG. 2D As shown, in the present disclosure, satellite infrared spectrum remote sensing data, including 8.7um, 10.8um, 12.0um and 13.4um spectrum remote sensing data, can be collected and sorted. The raw data is converted into radiant brightness temperature (brightness temperature, BT), in Kelvin (K). These spectrum BTs can be represented by 8.7 , BT 10.8 , BT 12.0 and BT 13.4 The actual observed values ​​of the observed data can generate BT respectively 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 FIG. 2A to FIG. 2D As shown, Figure 2A Can be used to indicate BT 8.7 The corresponding brightness temperature distribution diagram, Figure 2B Can be used to indicate BT10.8 The corresponding brightness temperature distribution diagram, Figure 2C Can be used to indicate BT 12.0 The corresponding brightness temperature distribution diagram, Figure 2D Can be used to indicate BT 13.4 The corresponding brightness temperature distribution diagram.

[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 standard blackbody thermal radiation energy, the pseudo-emissivity uses the same definition, but the standard blackbody thermal radiation temperature uses the reference channel observed brightness temperature. Specifically, the pseudo-emissivity calculation formula is:

[0097]

[0098] In the formula, 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. After testing, it is found that the 12.0um channel is used as the reference channel for better results, that is, λ0=12um 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 is assumed that the influence of the 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 relatively low, while the emissivity of cloud droplets around 100um is relatively high. Therefore, it can effectively distinguish between target areas such as low clouds, heavy fog, dust, deserts, and high clouds; in the 10.8um spectral band, due to the close wavelength to the 12.0um reference spectral band, the emissivity of the water surface and dense clouds in these two spectral bands is almost the same, 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 that the light has traveled; there are differences in the occurrence rates of sea and land, and sea and land targets can also be effectively distinguished.

[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 0 and maximum value 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 as follows:

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

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

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

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

[0118] Combination 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 weather conditions.

[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 device for generating a cloud map using multi-spectral long-wave infrared remote sensing data of this embodiment includes: an acquisition unit 501, which is used to acquire a plurality of spectral long-wave infrared remote sensing data;

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

[0122] The second determining unit 503 is used 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 used to generate a color cloud map for indicating meteorological information according to the normalized pseudo emissivity information corresponding to each section of 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 Figure 1 The relevant descriptions of step 101, step 102, step 103, step 104 and step 105 in the corresponding embodiments 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 any spectral band of long-wave infrared remote sensing data is determined by:

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

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

[0130] Based on the first numerical information and the second numerical information, determining pseudo emissivity information corresponding to the long-wave infrared remote sensing data of the spectrum band;

[0131] Wherein, the target spectral band long-wave infrared remote sensing data is data among the multiple spectral band 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 spectral band 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, determine the maximum pseudo emissivity value and the minimum pseudo emissivity value in the first pseudo emissivity information; and determine that the normalized value corresponding to the maximum pseudo emissivity value is 1, and the normalized value corresponding to the minimum pseudo emissivity is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in each 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 according to 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 the step of generating a color cloud map for indicating meteorological information according to 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 the normalized pseudo emissivity information corresponding to the second long-wave infrared remote sensing data;

[0141] Generate a blue image based on the 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 um;

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

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

[0146] Please refer to Figure 6 , Figure 6 An exemplary system architecture is shown 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, wireless communication links or optical fiber cables, etc.

[0148] The terminal devices 601, 602, and 603 can interact with the server 605 through the network 604 to receive or send messages, etc. Various client applications, such as web browser applications, search applications, and news information applications, can be installed on the terminal devices 601, 602, and 603. The client applications in the terminal devices 601, 602, and 603 can receive user instructions and perform corresponding functions according to the user instructions, such as adding corresponding information to the information according to the user 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 supporting 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 in various ways according to the information acquisition requests, and sending the 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 device for generating a cloud map using multi-spectral long-wave infrared remote sensing data can be set 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 device for generating a cloud map using multi-spectral long-wave infrared remote sensing data can be set in the server 605.

[0152] It should be understood that Figure 6 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0153] Reference below Figure 7 , which shows an electronic device (eg, Figure 6 The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is only an example and should not bring any limitation to 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 processor, 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. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. 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: input devices 706 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 708 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 709. The communication devices 709 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all 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 contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 709, or installed from a storage device 708, or installed from a 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 executed.

[0157] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, 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 containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit 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 of the above.

[0158] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may 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 the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; 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; normalize each pseudo-emissivity information respectively to obtain the 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 separate software package, partially on the user's computer and partially on a remote computer, or entirely on a 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 flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0163] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, an acquisition unit may also be described as a "unit for acquiring long-wave infrared remote sensing data of 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, 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 chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0165] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may 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 may 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 only a preferred embodiment of the present disclosure and an explanation of the technical principles used. 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 a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

[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 are 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 actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions 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; According to the normalized pseudo-emissivity information corresponding to the long-wave infrared remote sensing data of each spectral band, a color cloud map for indicating meteorological information is generated.

2. The method according to claim 1, characterized in that: The wavelengths of the multi-spectral long-wave infrared remote sensing data include: 8.7um, 10.8um, 12.0um and 13.4um.

3. The method according to claim 1, characterized in that The pseudo emissivity information corresponding to any spectral band of long-wave infrared remote sensing data is determined by the following method: Determine the first numerical information based on the blackbody thermal radiation formula, the brightness temperature information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and the wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment; Determine the second numerical information based on the blackbody thermal radiation formula, the wavelength information corresponding to the long-wave infrared remote sensing data of the spectrum segment, and the brightness temperature information corresponding to the long-wave infrared remote sensing data of the target spectrum segment; Based on the first numerical information and the second numerical information, determining pseudo emissivity information corresponding to the long-wave infrared remote sensing data of the spectral band; Wherein, the target spectral band long-wave infrared remote sensing data is data among the multiple spectral band 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, h is used to indicate the Planck constant, λ is used to indicate the wavelength, and BT is used to indicate the brightness temperature.

4. The method according to claim 3, characterized in that The wavelength of the target spectrum long-wave infrared remote sensing data is 12.0 um.

5. The method according to claim 3, characterized in that: The normalization process 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, determine the maximum pseudo emissivity value and the minimum pseudo emissivity value in the first pseudo emissivity information; and determine that the normalized value corresponding to the maximum pseudo emissivity value is 1, and the normalized value corresponding to the minimum pseudo emissivity is 0; wherein the first pseudo emissivity information is any pseudo emissivity information in each 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 according to a proportional relationship with the maximum pseudo emissivity value and the minimum pseudo emissivity value.

6. The method according to claim 1, characterized in that 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 the step of generating a color cloud map for indicating meteorological information according to 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 the normalized pseudo emissivity information corresponding to the second long-wave infrared remote sensing data; Generate a blue image based on the 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.

7. The method according to claim 6, characterized in that The wavelength of the first long-wave infrared remote sensing data is 13.4um; The wavelength of the second long-wave infrared remote sensing data is 8.7um; The wavelength of the third long-wave infrared remote sensing data is 10.8 um.

8. A device for generating cloud images using multi-spectral long-wave infrared remote sensing data, characterized in that: The device comprises: An acquisition unit, used for acquiring long-wave infrared remote sensing data of multiple spectral bands; The first determination unit is used to determine the brightness temperature information corresponding to the long-wave infrared remote sensing data of each spectral band; The second determination unit is used 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; A calculation unit is used to 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; 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 section of long-wave infrared remote sensing data.

9. 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 7.

10. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method described in any one of claims 1 to 7 is implemented.

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