A method and apparatus for daytime sea fog identification

Abstract

The application discloses a daytime sea fog identification method and device, solves the problem that the prior art cannot accurately judge sea fog and is prone to misjudgment of stratus and thin cloud. The method comprises the following steps: extracting a sea fog identification threshold; grouping the threshold; extracting daytime pixels; distinguishing the daytime pixels into suspected non-cloud fog pixels and cloud fog pixels through a first group of thresholds; finding sea fog pixels in the suspected non-cloud fog pixels through a second group of thresholds; distinguishing sea fog pixels in the cloud fog pixels through a third group of thresholds; and further screening the results of the first two groups of thresholds through a fourth group of thresholds to obtain final sea fog pixels. The application also proposes a daytime sea fog identification device, which comprises a pixel acquisition module, a data transmission module, a threshold data storage module and a sea fog identification module. The application realizes accurate sea fog identification and effectively distinguishes sea fog and stratus. Compared with a sea surface observation station, satellite data is easy to obtain, and the application plays a good reference role in safety of large-scale sea shipping and sea water circulation.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing data, and in particular to a method and apparatus for identifying daytime sea fog. Background Technology

[0002] Sea fog is a significant factor affecting maritime transportation and operations, and accurate identification and forecasting of sea fog play a crucial role in the effective operation of these sectors. Currently, the main methods for identifying sea fog include: buoy or ship detection, and the spectral thresholding method. Buoy and ship detection methods are limited by their spatial and temporal scope; the spectral thresholding method relies on the unique spectral characteristics of clouds and fog, utilizing the fact that the reflectivity of clouds and fog in the visible light band is higher than that of the sea surface. However, it is prone to misidentifying stratus clouds and thin clouds as sea fog, resulting in relatively low accuracy in sea fog identification. Summary of the Invention

[0003] This application provides a method and apparatus for identifying daytime sea fog, which solves the problem that the prior art cannot accurately identify sea fog and is prone to misidentifying stratus clouds and thin clouds.

[0004] This application provides a method for identifying daytime sea fog, including the following steps:

[0005] Data on albedo / brightness temperature, solar zenith angle, and cloud parameters from the Himawari-8 satellite were extracted, and a threshold method was used to obtain the sea fog identification threshold.

[0006] The sea fog identification threshold data were extracted and used to form a first threshold set, a second threshold set, a third threshold set, and a fourth threshold set.

[0007] Pixels with a solar zenith angle of less than 80° are retained as daytime pixels;

[0008] The daytime pixels are distinguished into suspected non-cloudy / foggy pixels and cloudy / foggy pixels using a first threshold set;

[0009] The second threshold set is used to perform threshold judgment on suspected non-cloudy / foggy pixels to obtain the first part of primary sea fog pixels;

[0010] The third threshold set is used to perform threshold judgment on cloud and fog pixels to obtain the second part of primary sea fog pixels;

[0011] The primary sea fog pixels are thresholded using the fourth threshold set to obtain the final sea fog pixels.

[0012] Preferably, the method further includes the steps of: extracting sea fog information from CALIPSO and comparing and verifying the final sea fog pixels.

[0013] Preferably, albedo at wavelengths of 0.51 μm, 0.64 μm, 0.86 μm, 1.6 μm, and 2.3 μm is extracted from the Himawari-8 satellite. Brightness temperature at wavelengths of 3.9 μm, 7.3 μm, 11.2 μm, and 12.4 μm is extracted.

[0014] More preferably, the cloud parameters extracted from the Himawari-8 satellite include cloud top height (CLTH), cloud optical thickness (CLOT), and cloud particle effective radius (CLER).

[0015] More preferably, the first threshold set includes albedo R0.64, R0.51, R1.6, brightness temperature difference BTD11.2-3.9, BTD11.2-12.4, albedo ratio R0.86 / R0.64, and brightness temperature BT7.3.

[0016] More preferably, the second threshold set includes R0.51, BT3.9, and CLER. The third threshold set includes R0.64, 0.64μm texture information, R2.3, CLOT, and CLER.

[0017] More preferably, the fourth threshold set includes CLTH, R0.64, CLER, and CLOT.

[0018] This application also provides a daytime sea fog identification device, employing the aforementioned daytime sea fog identification method, comprising a pixel acquisition module, a data transmission module, a threshold data storage module, and a sea fog identification module. The pixel acquisition module is used to acquire parameters of pixels in the area to be measured. The data transmission module is used to transmit the acquired pixel parameters of the area to be measured to the sea fog identification module and transmit the data output by the sea fog identification module. The threshold data storage module is used to store identification threshold data constructed from Himawari-8 satellite data and transmit the data to the sea fog identification module. The sea fog identification module is used to receive the data transmitted by the data transmission module and the threshold data storage module, and by combining and comparing them, identify whether the target area is sea fog.

[0019] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described daytime sea fog identification method.

[0020] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable by the processor. When the processor executes the computer program, it implements the above-described daytime sea fog identification method.

[0021] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0022] This invention achieves accurate sea fog identification and effectively distinguishes between sea fog and stratus clouds. Compared with sea surface observation stations, satellite data is easier to obtain and provides a better reference for the safety of large-scale maritime shipping and the marine water cycle. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 This is a flowchart of the daytime sea fog identification method of this application;

[0025] Figure 2 This is a flowchart of another embodiment of the daytime sea fog identification method of this application;

[0026] Figure 3 This is a schematic diagram of a sorting device according to this application;

[0027] Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0030] Example 1

[0031] Figure 1 This is a flowchart of the daytime sea fog identification method of this application.

[0032] A method for identifying sea fog during the day includes the following steps:

[0033] Step 101: Extract albedo / brightness temperature, solar zenith angle data and cloud parameter data from the Himawari-8 satellite, and use the thresholding method to obtain the sea fog identification threshold;

[0034] Albedo at wavelengths of 0.51 μm, 0.64 μm, 0.86 μm, 1.6 μm, and 2.3 μm was extracted from the Himawari-8 satellite. Brightness temperature at wavelengths of 3.9 μm, 7.3 μm, 11.2 μm, and 12.4 μm was also extracted.

[0035] The extracted cloud parameters include cloud top height (CLTH), cloud optical thickness (CLOT), and cloud particle effective radius (CLER).

[0036] Step 102: Extract the sea fog identification threshold data to form the first threshold set, the second threshold set, the third threshold set, and the fourth threshold set.

[0037] The first threshold set is used to make an initial judgment on the data, distinguishing between suspected non-cloudy pixels and cloudy pixels. The second and third threshold sets are used to make a second judgment on the two results of the judgment made by the first threshold set, and the fourth threshold set is used to make a third judgment on the results of the second judgment. Therefore, different threshold combinations should be used for two adjacent judgments.

[0038] For example, the first threshold set differs from the second or third threshold set in at least one parameter. The parameters of the second and third threshold sets may be the same or different; no further limitation is made here. The second or third threshold set differs from the fourth threshold set in at least one parameter.

[0039] For example, the sea fog recognition threshold can be divided into four completely different parts, which are respectively placed into a first threshold set, a second threshold set, a third threshold set, and a fourth threshold set.

[0040] Step 103: Retain pixels with a solar zenith angle < 80° as daytime pixels, and discard other pixels as nighttime pixels.

[0041] The method described in this application is used for daytime sea fog identification, but it cannot identify sea fog at night. Therefore, it is necessary to first remove nighttime pixels and use the solar zenith angle (SOZ) < 80° as a daytime constraint to remove invalid nighttime values.

[0042] Step 104: The daytime pixels are distinguished into suspected non-cloudy pixels and cloudy pixels using a first threshold set.

[0043] The first threshold set is composed of at least one of the following factors or combinations: bands where the albedo of clouds and fog is higher than that of other backgrounds, bands where the albedo of water bodies is lower than that of clouds and fog, bands where the brightness-temperature difference of clouds and fog is lower than that of other backgrounds, bands where the brightness-temperature difference of clouds and fog is lower than that of other backgrounds, and bands where the ratio of thick cloud albedo to that of other backgrounds is higher than that of other backgrounds.

[0044] For example, the first set of thresholds includes parameters or combinations of parameters among albedo R0.64, R0.51, R1.6, brightness temperature difference BTD11.2-3.9, BTD11.2-12.4, albedo ratio R0.86 / R0.64, and brightness temperature BT7.3.

[0045] Using albedo R 0.64 R 0.51 R 1.6 Brightness temperature difference BTD 11.2-3.9 BTD 11.2-12.4 Albedo R 0.86 / R 0.64 and brightness temperature BT 7.3 These bands or combinations of bands serve as the basis for identifying sea fog, thereby distinguishing between suspected non-cloudy / fog pixels and cloudy / fog pixels.

[0046] According to Mie scattering theory, the scattering effect of clouds and fog is quite significant, especially in the visible light band R. 0.64 The albedo of clouds and fog is significantly higher than that of the underlying surface such as vegetation and water bodies. Furthermore, due to the temporal variation of the solar zenith angle, the albedo of the visible light band changes with different times and seasons. Therefore, by analyzing the functional relationship between cloud and fog reflectance in the visible light band and the solar zenith angle, a cloud and fog reflectance function with the solar zenith angle as the independent variable is constructed, setting R... 0.64 The dynamic threshold improves the accuracy of cloud and fog detection when the solar zenith angle is large.

[0047] Compared to clouds and fog, water has a lower albedo, therefore, through R... 0.51 Separate the water from the clouds.

[0048] The mid-infrared band at 3.9 μm lies at the overlap of the solar spectrum and the Earth-atmosphere radiation spectrum. Therefore, the radiation obtained in this channel during the day includes both reflected solar radiation and infrared radiation emitted by the object itself, neither of which can be ignored. The intensity of reflected radiation in this channel depends on the effective radius of the particles; the smaller the effective radius of the particles, the greater the reflectivity of the channel. Therefore, the reflectivity region of this channel roughly corresponds to the sea fog-covered area.

[0049] Converting the radiation in the 3.9 μm band into brightness temperature, the brightness temperature difference (BTD) between 11.2 μm and 3.9 μm for sea fog is... 11.2-3.9 A negative value indicates a low level of water cloud, which is more effective in detecting the presence of low-level water clouds and is smaller than that of other surfaces. The brightness temperature difference in fog areas is relatively concentrated, ranging from -35°C to -20°C, with a peak around -35°C. The brightness temperature difference spectrum in mid-to-high cloud areas is wider, ranging from -45°C to -20°C, with a peak around -30°C. Although the peak distributions of fog and cloud areas show some differences, they also overlap significantly, exhibiting a degree of cohesion, especially in the brightness temperature difference distribution. Therefore, in addition to these two indicators for identifying fog areas, other criteria need to be incorporated.

[0050] Long-wave infrared band BT 11.2 BT 12.4Solar radiation has very little energy; the primary source of radiation is the radiation emitted by the underlying surface and the clouds themselves. Therefore, the higher the temperature of an object, the greater its emissivity. Mid-to-high clouds, compared to sea fog and the underlying surface, have a higher altitude, thus their cloud top temperatures are lower than fog tops and the underlying surface, resulting in lower brightness temperatures. This can be utilized in BTD (Brightness Temperature Detection). 11.2-12.4 The small differences in brightness temperature of clouds and fog, compared to the large variations in the brightness temperature of water bodies, distinguish them from water bodies.

[0051] The albedo ratio R between the 0.86μm band and the 0.64μm band 0.86 / R0.64 It is effective for detecting thick clouds on the ocean surface and can effectively locate areas of water.

[0052] In shortwave infrared R 1.6 Snow and ice crystals have very low reflectivity, therefore ice clouds also have very low reflectivity, but low clouds still have high reflectivity in this band. BT 7.3 It is more sensitive to high clouds; the higher the cloud top, the lower the temperature, thus enabling the detection of high clouds.

[0053] Step 105: Use the second threshold set to perform threshold judgment on the suspected non-cloudy / foggy pixels to obtain the first part of primary sea fog pixels.

[0054] The second threshold set is composed of at least one of the following factors or combinations: bands with reflectivity greater than other backgrounds in the sea fog channel, bands with reflectivity lower than other backgrounds in the water body, and the effective radius range of cloud particles in the sea fog;

[0055] For example, the second threshold set includes R0.51, BT3.9, and CLER. Using R... 0.51 BT 3.9 CLER performs threshold judgment on suspected non-cloudy pixels.

[0056] Non-sea fog detection data selection R 0.51 BT 3.9 The threshold setting of the effective radius (CLER) of cloud particles is used to determine the sea fog identification results in areas suspected to be non-cloudy or foggy.

[0057] Because the brightness temperature difference method has limitations in detecting shallow fog, some fog may be identified as non-sea fog pixels. Furthermore, although R... 0.86 / R0.64 It works well for detecting thick clouds over the ocean surface, but errors can easily occur at the cloud edges, thus some areas covered by haze are also identified as non-cloudy areas. Therefore, R is selected in this step. 0.51 BT 3.9 Further analysis of the effective radius of cloud particles led to a preliminary identification result that incorrectly identified the area as non-sea fog as sea fog.

[0058] R 0.51 The water body has a low albedo, therefore, through R0.51 Separate the sea fog that was mistakenly identified as not being a sea fog area.

[0059] The reflected radiation intensity at 3.9 μm depends on the effective radius of the particle; the smaller the effective radius, the greater the reflectivity of the channel. Therefore, there is a good correspondence between the region of high reflectivity in this channel and the sea fog-covered area. Therefore, BT... 3.9 The effective radius of cloud particles was used as a criterion for further sea fog identification.

[0060] Step 106: Use the third threshold set to perform threshold judgment on the cloud and fog pixels to obtain the second part of primary sea fog pixels.

[0061] The third threshold set is composed of at least one of the following factors or combinations: physical features related to cloud identification (including cloud optical thickness and effective radius of cloud particles), bands that distinguish the albedo of clouds from fog, and bands that distinguish the texture of clouds from fog.

[0062] For example, the third threshold set includes R0.64, 0.64μm texture information, R2.3, CLOT, and CLER. Using R... 0.64 0.64μm texture information, R 2.3 CLOT and CLER are used to determine the threshold of cloud and fog pixels.

[0063] The pixel was judged to be a cloud or fog pixel using R 0.64 0.64μm texture information, R 2.3 By excluding cloud pixels based on cloud optical thickness and effective radius of cloud particles, the result may be sea fog.

[0064] Fog in R 0.64 The reflectivity of the cloud is significantly greater than that of the underlying surface, but less than that of mid-to-high clouds. Although the ranges of cloud albedo and sea fog albedo overlap to some extent, some cloud pixels can be excluded from the cloud and fog pixels.

[0065] Since most fog forms under stable weather conditions (temperature inversion) when warm, moist air moves onto a cold surface, its top height is uniform, resulting in minimal variation in brightness and a smooth, uniform texture. In contrast, mid-to-high clouds, especially cumulus, exhibit significant variations in top height, leading to marked changes in brightness and texture. Therefore, R... 0.64 The texture information of each pixel is calculated using formula (1), and cumulus clouds with large texture changes are excluded by setting a texture threshold.

[0066] Texture = (R i-1,j-1 +R i-1,j +R i-1,j+1 +R i,j-1 +R i,j+1 +R i+1,j-1 +R i+1,j +R i+1,j+1)-8×R i,j (1)

[0067] Where i and j are pixel indices, and R is the albedo.

[0068] In the near-infrared (NIR) band R 2.3 The reflectivity is highly sensitive to changes in the effective particle radius of clouds and fog; the larger the effective particle radius, the lower the reflectivity. When the optical thickness is large, the increase in reflectivity with increasing optical thickness decreases. For clouds and fog with a certain optical thickness, the reflectivity at 2.3 μm gradually becomes less than the reflectivity at channel 0.64 as the effective particle radius increases. Therefore, R is set... 2.3 Thresholds for cloud optical thickness and cloud particle effective radius are used to identify fog pixels in cloud pixels.

[0069] Step 107: Use the fourth threshold set to perform threshold judgment on the primary sea fog pixels to obtain the final sea fog pixels.

[0070] The fourth threshold set comprises at least one of the following factors or combinations: the range of cloud top heights that distinguish sea fog from clouds, cloud optical thickness, effective particle radius range, and band reflectivity values ​​that vary linearly with optical thickness.

[0071] For example, the fourth threshold set includes CLTH, R0.64, CLER, and CLOT. Using CLTH, R... 0.64 CLER and CLOT are used to threshold the pixels identified as sea fog.

[0072] The sea fog identification results for suspected non-cloudy pixels and the sea fog identification results for cloudy pixels were obtained by using cloud top height and R, respectively. 0.64 The effective radius of cloud particles and the optical thickness of clouds are used as further criteria to determine sea fog, thus obtaining the final sea fog identification result. Due to the unique physical and meteorological conditions that form sea fog, its thickness is generally only tens to hundreds of meters. Therefore, cloud top height can be used as a threshold to distinguish some sea fog from stratus clouds.

[0073] The peak diameter of sea fog droplets is mostly between 3 and 7 μm, and the effective particle radius is smaller than that of clouds as generally considered. Regardless of the effective particle radius, the reflectivity at 0.64 μm varies very little, but changes significantly with optical thickness. The greater the optical thickness of the cloud / fog, the greater the reflectivity at 0.64 μm, with a generally linear increasing relationship between the two. Therefore, by setting R... 0.64 The thresholds for the effective radius of cloud particles and the optical thickness of clouds are used to obtain the final sea fog detection results, and to effectively distinguish between sea fog and stratus clouds, thereby improving the accuracy of sea fog identification.

[0074] Example 2

[0075] Figure 2 This is a flowchart of another embodiment of the daytime sea fog identification method of this application.

[0076] A method for identifying sea fog during the day includes the following steps:

[0077] Step 101: Extract albedo / brightness temperature, solar zenith angle data and cloud parameter data from the Himawari-8 satellite, and use the thresholding method to obtain the sea fog identification threshold;

[0078] Step 102: Extract the sea fog identification threshold data to form a first threshold set, a second threshold set, a third threshold set, and a fourth threshold set;

[0079] Step 103: Retain pixels with a solar zenith angle < 80° as daytime pixels;

[0080] Step 104: The daytime pixels are distinguished into suspected non-cloudy / foggy pixels and cloudy / foggy pixels using a first threshold set;

[0081] Step 105: Use the second threshold set to perform threshold judgment on suspected non-cloudy / foggy pixels to obtain the first part of primary sea fog pixels;

[0082] Step 106: Use the third threshold set to perform threshold judgment on the cloud and fog pixels to obtain the second part of primary sea fog pixels;

[0083] Step 107: Use the fourth threshold set to perform threshold judgment on the primary sea fog pixels to obtain the final sea fog pixels.

[0084] Step 108: Extract sea fog information from CALIPSO and compare and verify the sea fog identification results.

[0085] The sea fog identification results are verified. In this embodiment, data from the CLIPSO lidar satellite is used as the ground truth for verifying sea fog identification.

[0086] This application also proposes a daytime sea fog recognition device, which adopts the above-mentioned daytime sea fog recognition method and includes a pixel acquisition module 201, a data transmission module 202, a threshold data storage module 203, and a sea fog recognition module 204.

[0087] The pixel acquisition module is used to acquire parameters of the pixels in the area to be tested.

[0088] The data transmission module is used to transmit the acquired pixel parameters of the area to be tested to the sea fog recognition module, and transmit the data output by the sea fog recognition module.

[0089] The threshold data storage module is used to store the identification threshold data constructed from Himawari-8 satellite data and transmit the data to the sea fog identification module.

[0090] The sea fog identification module is used to receive data from the data transmission module and the threshold data storage module, and by combining and comparing the data, identify whether the target area is sea fog.

[0091] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] It should be noted that the execution subject of each step in the method provided in Embodiment 1 or 2 can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 101, 102 and 103 can be device 203, and the execution subject of step 104 can be device 204; or, for example, the execution subject of steps 101 and 102 can be device 203, and the execution subject of steps 103 and 104 can be device 204; and so on.

[0093] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] Therefore, this application also proposes a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the methods described in any embodiment of this application.

[0095] Furthermore, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any embodiment of this application.

[0096] For example, in Embodiment 3, this application embodiment provides an electronic device. Figure 4This is a schematic diagram of the structure of an electronic device 300 provided in Embodiment 3 of this application, which includes: one or more processors 301; and a storage device 302 for storing one or more programs. When the one or more programs are run by the one or more processors 301, the one or more processors 301 implement the cloud base height determination method provided in this embodiment of the application. The method includes:

[0097] Obtain image metadata for the target region;

[0098] Compare the target area image metadata with the sea fog recognition threshold to obtain suspected non-cloudy / fog pixels and cloudy / fog pixels;

[0099] The sea fog recognition threshold is used to determine the threshold of suspected non-cloudy / foggy pixels, and the sea fog recognition results of non-sea fog pixels are obtained.

[0100] The cloud and fog pixels are thresholded using the sea fog recognition threshold to obtain the sea fog recognition results for the cloud and fog pixels.

[0101] Threshold judgments are performed on the sea fog recognition results of non-sea fog pixels and the sea fog recognition results of cloud fog pixels respectively to remove pixels that are misclassified as sea fog, and the final sea fog recognition result is obtained.

[0102] Figure 4 The electronic device 300 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0103] like Figure 4 As shown, the electronic device 300 includes a processor 301, a storage device 302, an input device 303, and an output device 304; the electronic device may have one or more processors. Figure 4 Taking a processor as an example; in electronic devices, the processor, storage device, input device, and output device can be connected via a bus or other means. Figure 4 Taking the connection between China and Israel via bus 305 as an example.

[0104] Storage device 302, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and module units, such as the program instructions corresponding to the cloud bottom height determination method in the embodiments of this application.

[0105] Storage device 302 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, storage device 302 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, storage device 302 may further include memory remotely located relative to processor 302, and these remote memories can be connected via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0106] Input device 303 can be used to receive input digital, character, or voice information, and to generate key signal inputs related to user settings and function control of electronic devices. Output device 304 may include electronic devices such as displays and speakers.

[0107] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0108] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for identifying sea fog during the day, characterized in that, Includes the following steps: Albedo / brightness temperature, solar zenith angle data, and cloud parameter data were extracted from the Himawari-8 satellite, and a threshold method was used to obtain the sea fog identification threshold. Among them, the cloud parameters include cloud top height CLTH, cloud optical thickness CLOT, and cloud particle effective radius CLER. Sea fog recognition threshold data were extracted and used to form a first threshold set, a second threshold set, a third threshold set, and a fourth threshold set. The first threshold set includes albedo R0.64, R0.51, R1.6, brightness temperature difference BTD11.2-3.9, BTD11.2-12.4, albedo ratio R0.86 / R0.64, and brightness temperature BT7.

3. The second threshold set includes R0.51, BT3.9, and CLER. The third threshold set includes R0.64, 0.64μm texture information, R2.3, CLOT, and CLER. The fourth threshold set includes CLTH, R0.64, CLER, and CLOT. Pixels with a solar zenith angle of less than 80° are retained as daytime pixels; The daytime pixels are distinguished into suspected non-cloudy / foggy pixels and cloudy / foggy pixels using a first threshold set; The second threshold set is used to perform threshold judgment on suspected non-cloudy / foggy pixels to obtain the first part of primary sea fog pixels; The third threshold set is used to perform threshold judgment on cloud and fog pixels to obtain the second part of primary sea fog pixels; The primary sea fog pixels are thresholded using the fourth threshold set to obtain the final sea fog pixels.

2. The daytime sea fog identification method according to claim 1, characterized in that, It also includes the following steps: Sea fog information was extracted from CALIPSO, and the final sea fog pixels were compared and verified.

3. The daytime sea fog identification method according to claim 1, characterized in that, The method for calculating the 0.64μm texture information is as follows: Where i and j are pixel indices, and R is the albedo.

4. A daytime sea fog identification device, used to implement the method described in any one of claims 1-3, characterized in that, It includes a pixel acquisition module, a data transmission module, a threshold data storage module, and a sea fog recognition module; The pixel acquisition module is used to acquire parameters of pixels in the area to be measured; The data transmission module is used to transmit the acquired pixel parameters of the area to be tested to the sea fog recognition module, and transmit the data output by the sea fog recognition module. The threshold data storage module is used to store the identification threshold data constructed from Himawari-8 satellite data and transmit the data to the sea fog identification module; The sea fog identification module is used to receive data from the data transmission module and the threshold data storage module, and by combining and comparing the data, identify whether the target area is sea fog.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

6. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1-3.

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