CN113936226A - Global glacier search identification method based on remote sensing cloud computing - Google Patents
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Abstract
The invention provides a global glacier searching and identifying method based on remote sensing cloud computing, which comprises the following steps: acquiring a ground surface reflectivity image of a summer optical satellite image through a cloud computing platform, and acquiring spectral information of glaciers and other ground objects by using spectrum acquisition software; analyzing the difference between the glacier spectrum and other ground object spectra, and determining the effective spectral band and the threshold value of the identified glacier; constructing a glacier index algorithm, and determining a threshold value of the glacier index to distinguish glaciers from non-glaciers; calculating a glacier index image of each satellite image in summer in the research area based on a glacier index algorithm, and then obtaining a median synthetic image of the glacier index image in summer by adopting a median synthetic algorithm; and constructing an automatic glacier identification model by using the effective spectral band and the threshold thereof of the glacier, and the glacier index median synthetic image and the threshold thereof, thereby realizing remote sensing automatic identification mapping of glacier distribution. The invention provides a glacier remote sensing identification model, which can realize the remote sensing accurate and automatic identification of global glacier distribution.
Description
Technical Field
The invention relates to the technical field of remote sensing target identification, in particular to a global glacier search identification method based on remote sensing cloud computing.
Background
Glacier distribution exhibits significant annual variation characteristics against a global climate change background. Glacier distribution data on the global scale are updated effectively in time, and the method has important scientific value for researching the interaction between climate change and ecological environment and the like. Glaciers and clouds are both white in the visible range and show similar visible spectral features on optical satellite images. Glaciers covered by cloud shadows also have significantly changed their spectral characteristics on optical satellite imagery. These factors increase the difficulty of accurate glaciers identification. In addition, the characteristics of wide glacier distribution and large annual change provide higher requirements for rapidly updating glacier distribution data on the global scale. Therefore, a rapid interpretation method for glaciers on a global scale is urgently needed in the field of glacier distribution mapping.
Disclosure of Invention
Aiming at the defects in the technical background, the invention provides a global glacier searching and identifying method based on remote sensing cloud computing, which solves the technical problem of low glacier identification accuracy and realizes the annual remote sensing interpretation and mapping of glaciers on a global scale.
The technical scheme of the invention is realized as follows:
a global glacier searching and identifying method based on remote sensing cloud computing comprises the following steps:
s1, collecting a plurality of optical satellite images within the summer time range of X years by using a remote sensing cloud platform, wherein the pixel rows and columns of the plurality of optical satellite images are the same, and preprocessing the optical satellite images to obtain a plurality of earth surface reflectivity images;
s2, acquiring geographical position information of the glacier and non-glacier ground objects by using a visual interpretation method, and acquiring pixel spectral samples of the glacier and non-glacier ground objects in the surface reflectivity image by using spectrum acquisition software;
s3, analyzing the difference between the pixel spectrum of the glacier and the pixel spectrum of the non-glacier ground object based on the pixel spectrum sample obtained in the step S2 to obtain an effective spectrum waveband for identifying the glacier, wherein the effective spectrum waveband comprises a blue waveband, a green waveband, a red waveband and a short-wave infrared waveband;
s4, performing median synthesis on the plurality of earth surface reflectivity images in the step S1 by adopting a median synthesis algorithm to respectively obtain a blue waveband median synthesis image and a green waveband median synthesis image;
s5, respectively counting the blue waveband median composite images obtained in the step S4 according to the geographic position information of the glaciers and non-glaciers obtained in the step S2 to obtain pixel value distribution of the glaciers and the non-glaciers in the blue waveband median composite images, and taking the minimum value in the pixel value distribution of the glaciers in the blue waveband median composite images as a threshold value alpha for distinguishing the glaciers and the non-glaciers;
s6, respectively counting the green waveband median composite images obtained in the step S4 according to the geographic position information of the glaciers and non-glaciers obtained in the step S2 to obtain pixel value distribution of the glaciers and the non-glaciers in the green waveband median composite images, and taking the minimum value in the pixel value distribution of the glaciers in the green waveband median composite images as a threshold value beta for distinguishing the glaciers and the non-glaciers;
s7, constructing a glacier remote sensing index, calculating a plurality of glacier index images by using the glacier remote sensing index and the plurality of ground surface reflectivity images in the step S1, and then performing median synthesis on the plurality of glacier index images by adopting a median synthesis method to obtain a glacier index median synthesis image;
s8, according to the geographical position information of the glacier and non-glacier ground objects obtained in the step S2, counting the glacier index median composite image obtained in the step S7 to obtain pixel value distribution of the glacier and non-glacier ground objects in the glacier index median composite image, and taking the average value of the minimum value of the pixel value distribution of the glacier and the maximum value of the non-glacier ground objects in the glacier index median composite image as a threshold value gamma for distinguishing the glacier and non-glacier ground objects;
s9, dividing the global earth surface into 72X 36 to-be-searched identification areas, wherein the geographic range of each area is 5X 5 longitude and latitude, acquiring optical satellite images to be identified in the summer time range of X year of the J-th to-be-searched identification area by using a remote sensing cloud platform, preprocessing the optical satellite images to be identified to obtain earth surface reflectivity images to be identified, performing median synthesis on the earth surface reflectivity images to be identified by adopting a median synthesis algorithm, and respectively synthesizing to obtain a blue wave band median synthetic image and a green wave band median synthetic image;
s10, calculating the glacial index of the land surface reflectivity image to be identified obtained in the step S9 by using the calculation method of the glacial remote sensing index in the step S7, and synthesizing by adopting a median synthesis algorithm to obtain a glacial index median synthesis image to be identified;
s11, judging whether the pixel value of the blue wave band of the pixel i in the blue wave band median synthetic image in the step S9 is larger than a threshold value alpha or not, and meanwhile, whether the pixel value of the green wave band of the pixel i in the green wave band median synthetic image is larger than a threshold value beta or not, if the two constraint conditions are met, executing a step S12, and if not, setting the position of the pixel where the pixel i is located as a non-glacier;
s12, judging whether the pixel value of the pixel i in the glacier index median synthetic image to be identified obtained in the step S10 is larger than a threshold value gamma, if so, determining that the position of the pixel i is glacier, otherwise, determining that the position of the pixel i is non-glacier;
s13, circularly executing the steps S11 to S12 until all pixel positions in the synthetic image to be identified are traversed, and completing glacier remote sensing identification of the J-th identification area to be searched;
and S14, circularly executing the steps S9 to S13 until all the areas to be searched and identified of the complete ball are traversed, and completing the search and identification of the global glacier.
Preferably, the method for obtaining the surface reflectivity image comprises the following steps: and multiplying all pixel values in the optical image by a correction coefficient 0.0000275, and subtracting 0.2 to obtain the earth surface reflectivity image of the optical satellite image.
Preferably, in step S4, the method for obtaining the blue-band median composite image includes: firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all blue wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; sequentially traversing all pixel positions to obtain a blue waveband median synthetic image;
the method for acquiring the green waveband median composite image comprises the following steps: firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all green wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain a green waveband median composite image.
Preferably, the glacier remote sensing index is:
GIi＝(ξSWIR,i-ξred,i)/(ξSWIR,i+ξred,i+0.1)；
wherein, GIiRepresenting glacier remote sensing index xi of pixel i in glacier remote sensing index imageSWIR,iSurface reflectivity xi of short wave infrared band of pixel i in surface reflectivity imagered,iAnd (3) representing the earth surface reflectivity of the red wave band of the image element i in the earth surface reflectivity image, wherein i is 1,2, …, n is the total number of the image elements in the earth surface reflectivity image.
Preferably, the glacier index median synthetic image is obtained by: firstly, creating a blank image with the same number of rows and columns of pixels as the glacier index image; then extracting all pixel values in the glacier index image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain the glacier index median synthetic image.
Preferably, in step S9, the method for dividing the global earth surface into 72 × 36 blocks of the identification area to be searched is as follows: firstly, dividing geographic longitude lines by taking a geographic initial meridian as a starting point and taking 5 geographic longitudes as intervals, and dividing the global earth surface into 72 strips parallel to the geographic longitude lines; then, taking the north pole as a starting point, dividing geographical latitude lines at intervals of 5 geographical latitudes, and dividing the globe into 36 strips parallel to the geographical latitude lines; the weft strips cross the warp strips, dividing the earth's surface into 72 x 36 areas.
Compared with the prior art, the invention has the following beneficial effects:
(1) the invention provides a glacier index based on an optical satellite image, namely a GI (Glacier index) index, which provides a theoretical basis for glacier remote sensing identification;
(2) the global glacier automatic identification algorithm established by the invention fully utilizes the spectral characteristics of the glacier short wave infrared and visible light wave bands, can be suitable for quickly identifying glaciers on the global scale, and provides accurate glacier information for related departments and industries.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
fig. 2 shows the glacier recognition result according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a global glacier search identification method based on remote sensing cloud computing, which includes the following specific steps:
s1, collecting 5 optical satellite images within the summer time range of 2021 year by using a remote sensing cloud platform (Google Earth Engine), wherein the 5 optical satellite images have the same pixel array number, and preprocessing the optical satellite images to obtain a plurality of Earth surface reflectivity images.
The method for obtaining the earth surface reflectivity image comprises the following steps: and multiplying all pixel values in the optical image by a correction coefficient 0.0000275, and subtracting 0.2 to obtain the earth surface reflectivity image of the optical satellite image.
S2, acquiring geographical position information of glacier and non-glacier ground objects by using a visual interpretation method, and acquiring pixel spectral samples of the glacier and non-glacier ground objects in a ground surface reflectivity image of 7, 10 and 2021 by using spectral acquisition software; the number of the sample pixels of the glacier is 1190, the number of the sample pixels of the non-glacier ground objects is 1134, and the types of the non-glacier ground objects comprise cloud layers, water bodies, bare land, vegetation, construction land and the like.
S3, analyzing the difference between the pixel spectrum of the glacier and the pixel spectrum of the non-glacier ground object in 2021, 7 months and 10 days based on the pixel spectrum sample obtained in the step S2, and finding through comparative analysis that the short wave infrared band is the optimal band for distinguishing the glacier from the cloud layer, and the blue band, the green band and the red band are effective bands for distinguishing the glacier from other land coverage types such as water, bare land, vegetation, construction land and the like, so that the effective spectrum band for identifying the glacier is determined, wherein the effective spectrum band comprises the blue band, the green band, the red band and the short wave infrared band;
and S4, performing median synthesis on the 5 earth surface reflectivity images in the step S1 by adopting a median synthesis algorithm to respectively obtain a blue waveband median synthesis image and a green waveband median synthesis image.
The method for acquiring the blue waveband median synthetic image comprises the following steps:
firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all blue wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain a blue waveband median synthetic image.
The method for acquiring the green waveband median composite image comprises the following steps:
firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all green wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain a green waveband median composite image.
S5, according to the geographic location information of the glacier and non-glacier ground objects obtained in step S2, respectively counting the blue waveband median composite images obtained in step S4 to obtain pixel value distributions of the glacier and non-glacier ground objects in the blue waveband median composite images, that is, the glacier belongs to [0.19,0.98], and taking the minimum value in the pixel value distributions of the glacier in the blue waveband median composite images as a threshold α for distinguishing the glacier and non-glacier ground objects, which is 0.19.
S6, according to the geographic location information of the glacier and non-glacier ground objects obtained in step S2, respectively counting the green band median composite images obtained in step S4 to obtain pixel value distributions of the glacier and non-glacier ground objects in the green band median composite images, that is, the glacier belongs to [0.21,0.98], and taking the minimum value in the pixel value distributions of the glacier in the green band median composite images as a threshold β for distinguishing the glacier and non-glacier ground objects, which is 0.21.
S7, finding that the reflectivity of the glacier on the short-wave infrared band is extremely low and is generally less than 0.1 according to the result obtained in the step S3, the reflectivity of the glacier on the red band is obviously higher than the reflectivity of the glacier on the short-wave infrared band, and non-glacier ground objects rarely have the characteristic, so that a glacier remote sensing index is constructed, a plurality of glacier index images are obtained by utilizing the glacier remote sensing index and the plurality of ground surface reflectivity images obtained in the step S1, and then a median synthesis method is adopted to perform median synthesis on the plurality of glacier index images to obtain a glacier index median synthesis image;
the glacier remote sensing index is as follows:
GIi＝(ξSWIR,i-ξred,i)/(ξSWIR,i+ξred,i+0.1)；
wherein, GIiRepresenting glacier remote sensing index xi of pixel i in glacier remote sensing index imageSWIR,iSurface reflectivity xi of short wave infrared band of pixel i in surface reflectivity imagered,iAnd (3) representing the earth surface reflectivity of the red wave band of the image element i in the earth surface reflectivity image, wherein i is 1,2, …, n is the total number of the image elements in the earth surface reflectivity image.
And S8, counting the glacier index median composite image obtained in the step S7 according to the geographic position information of the glacier and non-glacier ground objects obtained in the step S2 to obtain pixel value distribution of the glacier and non-glacier ground objects in the glacier index median composite image, namely the glacier belongs to [0.62,0.81], the non-glacier ground objects belong to [ -0.51,0.13], and taking the average value of the minimum value of the pixel value distribution of the glacier and the maximum value of the non-glacier ground objects in the glacier index median composite image as a threshold value gamma which is 0.47 for distinguishing the glacier and the non-glacier ground objects.
S9, dividing the global earth surface into 72 x 36 to-be-searched identification areas, wherein the geographic range of each area is 5 x 5 longitude and latitude, acquiring the optical satellite image to be identified in the summer time range of 2021 year of the J-th to-be-searched identification area by using a remote sensing cloud platform, preprocessing the optical satellite image to be identified to obtain an earth surface reflectivity image to be identified, performing median synthesis on the earth surface reflectivity image to be identified by adopting a median synthesis algorithm, and respectively synthesizing to obtain a blue wave band median synthetic image and a green wave band median synthetic image, namely the median synthetic image to be identified. The method for dividing the global earth surface into 72 x 36 blocks of identification areas to be searched comprises the following steps: firstly, dividing geographic longitude lines by taking a geographic initial meridian as a starting point and taking 5 geographic longitudes as intervals, and dividing the global earth surface into 72 strips parallel to the geographic longitude lines; then, taking the north pole as a starting point, dividing geographical latitude lines at intervals of 5 geographical latitudes, and dividing the globe into 36 strips parallel to the geographical latitude lines; the weft strips cross the warp strips, dividing the earth's surface into 72 x 36 areas.
S10, calculating the glacial index of the land surface reflectivity image to be identified obtained in the step S9 by using the calculation method of the glacial remote sensing index in the step S7, and synthesizing by adopting a median synthesis algorithm to obtain a glacial index median synthesis image to be identified; the method for obtaining the glacier index median synthetic image comprises the following steps: firstly, creating a blank image with the same number of rows and columns of pixels as the glacier index image; then extracting all pixel values in the glacier index image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain the glacier index median synthetic image.
S11, judging whether the pixel value of the blue wave band of the pixel i in the blue wave band median synthetic image in the step S9 is larger than a threshold value alpha or not, and meanwhile, whether the earth surface reflectivity of the green wave band of the pixel i in the green wave band median synthetic image is larger than a threshold value beta or not, if the two constraint conditions are met, executing the step S12, and if not, the position of the pixel where the pixel i is located is a non-glacier.
And S12, judging whether the pixel value of the pixel i in the glacier index median synthetic image to be identified obtained in the step S10 is larger than a threshold value gamma, if so, determining that the position of the pixel i is glacier, otherwise, determining that the position of the pixel i is non-glacier.
And S13, circularly executing the steps S11 to S12 until all pixel positions in the synthetic image to be identified are traversed, and completing the glacier remote sensing identification of the J-th identification area to be searched.
And S14, circularly executing the steps S9 to S13 until all the areas to be searched and identified of the complete ball are traversed, and completing the search and identification of the global glacier.
In order to verify the effect of the invention, the glacier remote sensing identification in the 2021 year Qinghai-Tibet plateau area is taken as an experimental object, the satellite image is a Landsat-8 optical satellite image, the cloud platform is a Google Earth Engine remote sensing cloud computing platform, and the identification result is shown in fig. 2. As can be seen from fig. 2, the boundary and other texture information of the glacier is complete, and other ground objects such as the cloud layer and the like can be effectively distinguished, which illustrates the reliability and accuracy of the invention for identifying the glacier.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A global glacier search identification method based on remote sensing cloud computing is characterized by comprising the following steps:
s1, collecting a plurality of optical satellite images within the summer time range of X years by using a remote sensing cloud platform, wherein the pixel rows and columns of the plurality of optical satellite images are the same, and preprocessing the optical satellite images to obtain a plurality of earth surface reflectivity images;
s2, acquiring geographical position information of the glacier and non-glacier ground objects by using a visual interpretation method, and acquiring pixel spectral samples of the glacier and non-glacier ground objects in the surface reflectivity image by using spectrum acquisition software;
s3, analyzing the difference between the pixel spectrum of the glacier and the pixel spectrum of the non-glacier ground object based on the pixel spectrum sample obtained in the step S2 to obtain an effective spectrum waveband for identifying the glacier, wherein the effective spectrum waveband comprises a blue waveband, a green waveband, a red waveband and a short-wave infrared waveband;
s4, performing median synthesis on the plurality of earth surface reflectivity images in the step S1 by adopting a median synthesis algorithm to respectively obtain a blue waveband median synthesis image and a green waveband median synthesis image;
s5, respectively counting the blue waveband median composite images obtained in the step S4 according to the geographic position information of the glaciers and non-glaciers obtained in the step S2 to obtain pixel value distribution of the glaciers and the non-glaciers in the blue waveband median composite images, and taking the minimum value in the pixel value distribution of the glaciers in the blue waveband median composite images as a threshold value alpha for distinguishing the glaciers and the non-glaciers;
s6, respectively counting the green waveband median composite images obtained in the step S4 according to the geographic position information of the glaciers and non-glaciers obtained in the step S2 to obtain pixel value distribution of the glaciers and the non-glaciers in the green waveband median composite images, and taking the minimum value in the pixel value distribution of the glaciers in the green waveband median composite images as a threshold value beta for distinguishing the glaciers and the non-glaciers;
s7, constructing a glacier remote sensing index, calculating a plurality of glacier index images by using the glacier remote sensing index and the plurality of ground surface reflectivity images in the step S1, and then performing median synthesis on the plurality of glacier index images by adopting a median synthesis method to obtain a glacier index median synthesis image;
s8, according to the geographical position information of the glacier and non-glacier ground objects obtained in the step S2, counting the glacier index median composite image obtained in the step S7 to obtain pixel value distribution of the glacier and non-glacier ground objects in the glacier index median composite image, and taking the average value of the minimum value of the pixel value distribution of the glacier and the maximum value of the non-glacier ground objects in the glacier index median composite image as a threshold value gamma for distinguishing the glacier and non-glacier ground objects;
s9, dividing the global earth surface into 72X 36 to-be-searched identification areas, wherein the geographic range of each area is 5X 5 longitude and latitude, acquiring optical satellite images to be identified in the summer time range of X year of the J-th to-be-searched identification area by using a remote sensing cloud platform, preprocessing the optical satellite images to be identified to obtain earth surface reflectivity images to be identified, performing median synthesis on the earth surface reflectivity images to be identified by adopting a median synthesis algorithm, and respectively synthesizing to obtain a blue wave band median synthetic image and a green wave band median synthetic image;
s10, calculating the glacial index of the land surface reflectivity image to be identified obtained in the step S9 by using the calculation method of the glacial remote sensing index in the step S7, and synthesizing by adopting a median synthesis algorithm to obtain a glacial index median synthesis image to be identified;
s11, judging whether the pixel value of the blue wave band of the pixel i in the blue wave band median synthetic image in the step S9 is larger than a threshold value alpha or not, and meanwhile, whether the pixel value of the green wave band of the pixel i in the green wave band median synthetic image is larger than a threshold value beta or not, if the two constraint conditions are met, executing a step S12, and if not, setting the position of the pixel where the pixel i is located as a non-glacier;
s12, judging whether the pixel value of the pixel i in the glacier index median synthetic image to be identified obtained in the step S10 is larger than a threshold value gamma, if so, determining that the position of the pixel i is glacier, otherwise, determining that the position of the pixel i is non-glacier;
s13, circularly executing the steps S11 to S12 until all pixel positions in the synthetic image to be identified are traversed, and completing glacier remote sensing identification of the J-th identification area to be searched;
and S14, circularly executing the steps S9 to S13 until all the areas to be searched and identified of the complete ball are traversed, and completing the search and identification of the global glacier.
2. The global glacier search and identification method based on remote sensing cloud computing of claim 1, wherein the earth surface reflectivity image is obtained by the following method: and multiplying all pixel values in the optical image by a correction coefficient 0.0000275, and subtracting 0.2 to obtain the earth surface reflectivity image of the optical satellite image.
3. The remote sensing cloud computing-based global glacier search and identification method according to claim 1 or 2, wherein in step S4, the method for obtaining the blue waveband median composite image is as follows: firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all blue wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; sequentially traversing all pixel positions to obtain a blue waveband median synthetic image;
the method for acquiring the green waveband median composite image comprises the following steps: firstly, creating a blank image with the same number of rows and columns of pixels as the earth surface reflectivity image; then extracting pixel values of all green wave bands in the surface reflectivity image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain a green waveband median composite image.
4. The remote sensing cloud computing-based global glacier search and identification method according to claim 1, wherein the glacier remote sensing index is:
GIi＝(ξSWIR,i-ξred,i)/(ξSWIR,i+ξred,i+0.1)；
wherein, GIiRepresenting glacier remote sensing index xi of pixel i in glacier remote sensing index imageSWIR,iSurface reflectivity xi of short wave infrared band of pixel i in surface reflectivity imagered,iAnd (3) representing the earth surface reflectivity of the red wave band of the image element i in the earth surface reflectivity image, wherein i is 1,2, …, n is the total number of the image elements in the earth surface reflectivity image.
5. The remote sensing cloud computing-based global glacier search and identification method according to claim 1 or 4, wherein the glacier index median synthetic image obtaining method comprises the following steps: firstly, creating a blank image with the same number of rows and columns of pixels as the glacier index image; then extracting all pixel values in the glacier index image at the pixel i position, calculating to obtain a median value of the pixel values, and writing the median value into a corresponding position in a blank image; and traversing all pixel positions in sequence to obtain the glacier index median synthetic image.
6. The remote sensing cloud computing-based global glacier search identification method according to claim 1, wherein in the step S9, the method for dividing the global earth surface into 72 x 36 to-be-searched identification areas comprises the following steps: firstly, dividing geographic longitude lines by taking a geographic initial meridian as a starting point and taking 5 geographic longitudes as intervals, and dividing the global earth surface into 72 strips parallel to the geographic longitude lines; then, taking the north pole as a starting point, dividing geographical latitude lines at intervals of 5 geographical latitudes, and dividing the globe into 36 strips parallel to the geographical latitude lines; the weft strips cross the warp strips, dividing the earth's surface into 72 x 36 areas.
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