CN113537083A - Fog identification method and device - Google Patents
Fog identification method and device Download PDFInfo
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Abstract
The invention provides a fog identification method, which comprises the following steps: calculating a solar zenith angle, dividing the whole day into three intervals of day, night and morning and evening according to a threshold value, and adaptively matching different fog identification algorithms to initial data to obtain a night pixel, a day pixel and a morning and evening pixel; carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result; according to the fog identification result, calculating the pixel identified as the fog according to the difference value of the long-wave infrared ray and the medium-wave infrared ray to obtain a water vapor content index; removing noise, processing misjudgment points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing; and combining the finally obtained fog identification result with ground observation and geographic information data to obtain a fog identification visual analysis result. The invention also provides a fog recognition device.
Description
Technical Field
The invention relates to the technical field of atmospheric environment monitoring, in particular to a fog identification method and device.
Background
Fog refers to a hydrometeor consisting of small water droplets or ice crystals suspended in the atmosphere near the earth's surface, a common natural phenomenon. The mist of small water droplets and ice crystals is formed by condensation of water in saturated or supersaturated air, resembling a cloud. Fog can affect visibility, and has great influence on transportation, production and life, aviation and navigation, and the like. In conventional meteorological observation, horizontal visibility is generally used as a definition standard of fog.
An advanced satellite remote sensing platform is an important means for monitoring fog, and the ground observation is limited by space-time conditions, so that the fog monitoring with full coverage, rapidness and the same standard cannot be formed. Currently, the satellite platforms with multispectral band data acquisition capability mainly include stationary satellites and polar orbit satellites 2 series, including: china Fengyun No. four (FY-4) series, China Fengyun No. three (FY-3) series, Japanese Himapari-8/9, American GOES-16/17, American NOAA series, American EOS/MODIS series, European MSG-3/4, European METOP series, etc. The satellite platforms provide abundant data resources for monitoring the fog, mostly have spectral bands suitable for monitoring the fog, and can realize accurate identification and quantitative calculation of the fog.
At present, a fog identification method based on satellite remote sensing comprises the following steps: the visible light and infrared threshold methods are the thought of fog identification given earlier, the overall misjudgment rate is high, and the method cannot be applied to various satellite data; the difference value between the short-wave infrared band of 3.7 mu m and the long-wave infrared band is utilized, so that the good effect on identifying the fog at night is achieved; the threshold control identification method based on the brightness temperature of the clear sky background is suitable for identifying the sea fog on the simple underlying surface and has poor applicability to the complex land surface. The method can partially solve the problem of fog identification, but has obvious limitations, can not realize all-weather and all-region fog monitoring, lacks real quantitative identification capability, and can not well adapt to the requirements of all-around fog monitoring.
Disclosure of Invention
In order to solve the problems in the prior art, embodiments of the present invention provide a method and an apparatus for identifying fog, so as to solve the problems in the prior art. The technical scheme is as follows:
in one aspect, the invention provides a fog identification method, which comprises the following steps: calculating a solar zenith angle, dividing the whole day into three intervals of day, night and morning and evening according to a threshold value, and adaptively matching different fog identification algorithms to initial data to obtain a night pixel, a day pixel and a morning and evening pixel; carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result; according to the fog identification result, calculating the pixel identified as the fog according to the difference value of the long-wave infrared ray and the medium-wave infrared ray to obtain a water vapor content index; removing noise, processing misjudgment points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing; and combining the finally obtained fog identification result with ground observation and geographic information data to obtain a fog identification visual analysis result.
Further, the method also comprises the following steps: and reading satellite remote sensing data, and analyzing different data formats to obtain a uniform memory object entity.
Further, the method also comprises the following steps: and performing space projection conversion on the analyzed data, and performing space interpolation processing by adopting an optimal matching interpolation algorithm to obtain initial data for fog identification.
In another aspect, the present invention provides a fog recognition apparatus, including: the self-adaptive matching unit is used for calculating a solar zenith angle, dividing the whole day into three intervals of day, night and morning and evening according to a threshold value, and performing self-adaptive matching on initial data by different fog identification algorithms to obtain a night pixel, a day pixel and a morning and evening pixel; the fog identification unit is used for carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result; the water vapor content index calculation unit is used for calculating the pixel identified as the fog according to the fog identification result and obtaining the water vapor content index by using the difference value of long-wave infrared and medium-wave infrared; the result correction unit is used for removing noise, processing misjudged points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing; and the fog analysis unit is used for combining the finally obtained fog identification result with ground observation and geographic information data to obtain a fog identification visual analysis result.
Furthermore, the device also comprises a data analysis unit for reading the satellite remote sensing data and analyzing different data formats to obtain a uniform memory object entity.
Further, the device further comprises a data preprocessing unit, wherein the data preprocessing unit is used for performing space projection conversion on the analyzed data and performing space interpolation processing by adopting an optimal matching interpolation algorithm to obtain initial data for identifying the fog.
The fog identification method and the fog identification device can be a comprehensive fog remote sensing identification method facing global coverage, all-weather, multi-scale and multi-source data, are suitable for monitoring consistency in the global range, have good adaptability to complex land surfaces, mountainous areas, sea surfaces and the like, and can be applied to real-time monitoring. Meanwhile, according to the fog identification result, a calculation method of the fog water vapor content index is provided, so that quantitative remote sensing monitoring of the fog becomes possible. According to the fog identification and quantitative calculation method, the invention also provides a fog monitoring device based on satellite remote sensing, which can be applied to real-time on-site monitoring and analysis of fog.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. In the drawings:
FIG. 1 is a general flow diagram of a fog identification method according to an embodiment of the invention;
FIG. 2 is a detailed flowchart of a fog identification method according to an embodiment of the present invention;
FIG. 3 is a block diagram showing a fog recognition device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a fog identification device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a general flowchart of a fog identification method according to an embodiment of the present invention.
Preprocessing satellite remote sensing data to obtain initial data with consistent space-time and standard physical quantity;
carrying out fog identification processing on the initial data by using a fog remote sensing identification method to obtain a fog removing part identification result;
carrying out accurate fog recognition at night, day and morning based on the change characteristics of the zenith angle of the sun;
obtaining a quantitative index of the water vapor content of the fog by an index calculation method for a fog identification result;
and finally, correcting the fog identification result by adopting a small component removal method and structural element matrix convolution.
According to a further aspect of the method for identifying fog based on satellite remote sensing, the data preprocessing comprises the following steps: calculating physical quantity of each wave band based on the satellite calibration coefficient; adopting an optimal matching interpolation algorithm to complete data conversion from a geographic coordinate system to a projection coordinate system; matching and fusing data of the multi-space-time overlapping region by using a minimum satellite zenith angle priority algorithm; and for visible light and near infrared bands, data correction is carried out by using a solar altitude angle cosine formula.
Fig. 2 is a specific flowchart of a fog identification method according to an embodiment of the present invention.
In this step, the remote sensing data is mainly analyzed by converting the remote sensing data into physical quantities to obtain the memory object entity.
And 102, performing data projection transformation on the analyzed remote sensing data.
In the step, an optimal matching interpolation algorithm is adopted to perform spatial interpolation processing, and projection conversion is performed on data of each wave band.
And 103, calculating the solar zenith angle of each pixel.
104, automatically matching each pixel to the time periods of day, night and morning and evening according to each solar zenith angle; then, the pixel reading is started, and the following processing is executed on different pixels:
if the image element at night is read, executing steps 105, 109 and 112; if the daytime pixel is read, executing steps 106, 110, 113, 115 and 116; if it is a morning and evening pel, steps 107, 108, 111, 114 are performed.
And steps 105, 106 and 107 are all to use the infrared band brightness temperature to judge a basic threshold, the brightness temperature threshold of 3 time periods is 270K, if the brightness temperature of the pixel is less than 270K, the possibility of fog is eliminated, and the next pixel processing is carried out. Specifically, since the physical characteristics of the fog are mainly represented by small water droplets surrounding condensation nuclei, the temperature of the water droplets cannot be too low, the basic threshold value identification of the temperature is performed by using long-wave infrared, and the main aim is to determine the temperature condition suitable for the occurrence of the preliminary fog, reduce the fog identification range and improve the identification precision. The threshold value is determined by using satellite 10.5 mu m long-wave infrared band data, considering the physical characteristics of the fog, and the infrared brightness temperature of the fog is higher than 270K, so as to be used as the first step of judgment.
The short wave infrared mainly refers to a wave band located in a range of 3.5-3.9 microns, the radiation transmission characteristic of the infrared is very suitable for monitoring the fog, the radiation of the underlying surface obtained by the short wave infrared and the long wave infrared at night is different, the short wave infrared is cooler, and the characteristic plays an important role in identifying the fog at night. In daytime, the short wave infrared can obtain the reflection and radiation of the underlying surface at the same time, so that the short wave infrared is warmer and has more reflection characteristics than the long wave infrared. For the identification of the fog, a correlation analysis is carried out by introducing a difference value between short wave infrared and long wave infrared, and the difference value is used as a criterion for the identification of the fog, and the calculation formula is as follows:
TBDsir＝Tir-Tsir
wherein T isirIs a long-wave infrared bright temperature, TsirFor short wave infrared bright temperature, TBD is respectively established for night and daysirThe threshold value space of (2) is used as a basis for identifying the fog.
For the pixels in the night time, calculating the difference value between the long wave infrared and the short wave infrared, judging whether the difference value is in a threshold interval [2.0,8.0],
The medium wave infrared mainly refers to a wave band positioned in an interval of 8.2-8.7 mu m and belongs to a window area channel. The water vapor absorption of the medium wave infrared is more obvious, and compared with the long wave infrared, the water vapor of the water drops has larger influence on the brightness temperature obtained by the medium wave infrared. Because the fog is mainly formed by small water drops with high water vapor content, the observation difference between medium wave infrared and long wave infrared can be used as the basis for identifying the fog. Therefore, a water vapor content index calculation model is established based on the difference value of the medium wave infrared and the long wave infrared and is used as a main basis for identifying the fog, and the calculation formula is as follows:
WVCI＝Tir-Tmir
wherein T isirIs a long-wave infrared bright temperature, TmirThe temperature is medium wave infrared brightness temperature, and WVCI is water vapor content index which is used as the basis for identifying the fog.
wherein R isnirIs the near infrared band albedo, R1.6Is the albedo, NI, of 1.6 mu m wave bandnirTo normalize the index, NInirTake on a value of [0,0.1]The interval is used as the basis for identifying the fog.
In step 108, for the pixels in the morning and evening time period, linear mapping functions of the short-wave infrared, medium-wave infrared and long-wave infrared brightness temperatures and the solar zenith angle are established so as to adapt to the change of the solar zenith angle and adjust the physical quantity.
And step 116, calculating the mean square error of the visible light albedo of the daytime pixel and the surrounding pixels thereof, and determining the uniformity of the pixel to be used as the final screening of the daytime pixel.
And step 117, combining the fog identification results in 3 time periods of day, night and morning and evening to generate a final fog identification result.
And step 118, calculating the water vapor content index of each pixel according to the final fog identification result to obtain a quantitative fog water vapor content index data result.
119, 120, based on the continuous distribution characteristic of the fog, a small hole area appears in the connected component of the identification result, and in the embodiment, based on the algorithm model of image morphology, the reasonable structural element matrix and the identification result image are used for performing morphological operation to remove the small hole area, so that the continuous distribution characteristic of the dust is ensured.
Fig. 3 is a structural diagram of a fog recognition device according to an embodiment of the present invention.
And the data analysis unit 201 is used for reading the satellite remote sensing data and analyzing different data formats to obtain a uniform memory object entity.
And the data preprocessing unit 202 is configured to perform spatial projection conversion on the analyzed data, and perform spatial interpolation processing by using an optimal matching interpolation algorithm to obtain initial data for fog identification.
And the self-adaptive matching unit 203 is used for calculating the solar zenith angle, dividing the whole day into 3 intervals of day, night and morning and evening according to a threshold value, and performing self-adaptive matching on initial data by different fog identification algorithms to obtain night pixels, day pixels and morning and evening pixels.
And the fog identification unit 204 is used for carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result.
And the water vapor content index calculation unit 205 is used for calculating the pixel identified as the fog according to the fog identification result and obtaining the water vapor content index by using the long-wave infrared and medium-wave infrared difference value.
And the result correcting unit 206 is used for removing noise, processing misjudged points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing.
And the fog analysis unit 207 is used for obtaining a fog identification visual analysis result by combining the finally obtained fog identification result with ground observation and geographic information data.
Fig. 4 is a schematic structural diagram of a fog identification device provided in an embodiment of the present invention. The fog recognition device 1100 may vary significantly due to configuration or performance, and may include one or more central processors 1122 (e.g., one or more processors) and memory 1132, one or more storage media 1130 (e.g., one or more mass storage devices) storing application programs 1142 or data 1144. Memory 1132 and storage media 1130 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 1130 may include one or more modules (not shown), each of which may include a sequence of instructions operating on the fog recognition apparatus 1100. Further, the central processor 1122 may be configured to communicate with the storage medium 1130 to execute a series of instruction operations in the storage medium 1130 on the fog recognition apparatus 1100.
The fog identification device 1100 may also include one or more power supplies 1129, one or more wired or wireless network interfaces 1150, one or more input-output interfaces 1158, one or more keyboards 1156, and/or one or more operating systems 1141, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.
The fog identification apparatus 1100 may include a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured for execution by the one or more processors to include instructions for performing the fog identification feature described above.
The decision device provided in the embodiment of the present invention can achieve the same technical effects as the fog recognition methods shown in fig. 1 and fig. 2, and details are not repeated herein.
The fog identification method and the fog identification device can be a comprehensive fog remote sensing identification method facing global coverage, all-weather, multi-scale and multi-source data, are suitable for monitoring consistency in the global range, have good adaptability to complex land surfaces, mountainous areas, sea surfaces and the like, and can be applied to real-time monitoring. Meanwhile, according to the fog identification result, a calculation method of the fog water vapor content index is provided, so that quantitative remote sensing monitoring of the fog becomes possible. According to the fog identification and quantitative calculation method, the invention also provides a fog monitoring device based on satellite remote sensing, which can be applied to real-time on-site monitoring and analysis of fog.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The above description is only exemplary of the present invention and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A fog identification method is characterized by comprising the following steps:
calculating the solar zenith angle, dividing the whole day into 3 intervals of day, night and morning and evening according to a threshold value, and adaptively matching different fog identification algorithms to initial data to obtain night pixels, day pixels and morning and evening pixels;
carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result;
according to the fog identification result, calculating the pixel identified as the fog according to the difference value of the long-wave infrared ray and the medium-wave infrared ray to obtain a water vapor content index;
removing noise, processing misjudgment points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing;
and combining the finally obtained fog identification result with ground observation and geographic information data to obtain a fog identification visual analysis result.
2. The method of claim 1, further comprising the steps of:
and reading satellite remote sensing data, and analyzing different data formats to obtain a uniform memory object entity.
3. The method of claim 1, further comprising the steps of:
and performing space projection conversion on the analyzed data, and performing space interpolation processing by adopting an optimal matching interpolation algorithm to obtain initial data for fog identification.
4. A fog recognition device, comprising:
the self-adaptive matching unit is used for calculating the solar zenith angle, dividing the whole day into 3 intervals of day, night and morning and evening according to a threshold value, and performing self-adaptive matching on initial data by different fog identification algorithms to obtain night pixels, day pixels and morning and evening pixels;
the fog identification unit is used for carrying out fog identification processing on the night pixels, the day pixels and the morning and evening pixels by using a fog identification method to obtain a fog identification result;
the water vapor content index calculation unit is used for calculating the pixel identified as the fog according to the fog identification result and obtaining the water vapor content index by using the difference value of long-wave infrared and medium-wave infrared;
the result correction unit is used for removing noise, processing misjudged points or areas, and ensuring the connectivity of the fog identification result by using structural element matrix convolution processing;
and the fog analysis unit is used for combining the finally obtained fog identification result with ground observation and geographic information data to obtain a fog identification visual analysis result.
5. The apparatus of claim 4, further comprising:
and the data analysis unit is used for reading the satellite remote sensing data and analyzing different data formats to obtain a uniform memory object entity.
6. The apparatus of claim 4, further comprising:
and the data preprocessing unit is used for performing space projection conversion on the analyzed data and performing space interpolation processing by adopting an optimal matching interpolation algorithm to obtain initial data for fog identification.
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