CN112818851A - Method for detecting icebound lake based on FY-3MWRI data - Google Patents

Method for detecting icebound lake based on FY-3MWRI data Download PDF

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CN112818851A
CN112818851A CN202110136227.9A CN202110136227A CN112818851A CN 112818851 A CN112818851 A CN 112818851A CN 202110136227 A CN202110136227 A CN 202110136227A CN 112818851 A CN112818851 A CN 112818851A
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王星东
杨淑绘
黄肖杰
王玉华
张浩伟
刘硕
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Abstract

The invention provides an intercropping lake detection method based on FY-3MWRI data, and relates to the technical field of satellite remote sensing. The method comprises the steps of firstly carrying out preprocessing such as image splicing, cutting, masking, radiometric calibration and the like on FY-3MWRI89GHz vertical polarization data, processing to obtain the polarization difference P of each point of an image based on processed horizontal polarization brightness temperature data and vertical polarization brightness temperature data of a sea area to be researched, obtaining the optimal segmentation threshold T for researching sea area ice lake detection through a maximum entropy algorithm, and further obtaining a south pole ice lake detection identification map. The method for detecting the iced lake based on the FY-3MWRI polarization difference data combined with the maximum entropy algorithm effectively improves the precision of the detection result of the iced lake.

Description

Method for detecting icebound lake based on FY-3MWRI data
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to an intercropping lake detection method based on FY-3MWRI data.
Background
For the global climate warming and the regulation of the atmospheric environment, the two polar regions have been the main cold source and fresh water storage regions of the global atmosphere, and have been important research objects in recent years for controlling the global atmospheric balance and the rising of seawater level. Although the geographical position and the climate characteristics of the south pole relative to the north pole are less understood, the south pole has a greater iceland reserve than the north pole, so that the south pole has an extremely important position and significance in the process of researching global climate change.
Climate change in the south Pole region has an inseparable relationship with a mesoscale phenomenon in the local region, namely the glacier lake. By intercrystalline lake is meant an open water area of ice that remains free of ice or covered only by thin ice for long periods of time under weather conditions up to freezing temperatures. According to the difference between the formation reason and the maintenance mechanism, the traditional classification method of the intercrystalline lake divides the intercrystalline lake into two types: latent heat intercropping lakes and sensible heat intercropping lakes. The power driving is the main cause of latent heat iced lake, the water in iced lake radiates to the air through sea-air interface, and the water temperature is close to the freezing point, so that a large amount of sea ice is generated, and the new ice is radiated under the power driving of wind or ocean current, thereby forming the iced open water area. The thermal driving is the main cause of the thermal ice lake, the temperature difference enables the seawater to generate convection, the deep ocean warm current can be caused to rise, the surface sea ice is melted by heat, and new ice is prevented from being generated, so that the ice lake surrounded by the sea ice is formed.
The water-ice-air heat flux exchange in the intercrystalline lake region is much larger than that in the thick ice covered region, so that the intercrystalline lake becomes the main ice producing land of the polar region. The obvious ocean-atmosphere heat exchange in the icebound lake causes the air temperature in the upper air and the nearby areas to be rapidly increased, greatly influences the atmospheric circulation in local areas, and plays an important role in polar climate change.
For extraction and identification of the glacier lake, a threshold method based on sea ice density is mostly adopted at present. The sea ice density result obtained by the sea ice density inversion algorithm is not high in precision (in a low ice concentration area, the uncertainty of the sea ice density result is up to 25%, and in an edge ice area, the comparison result of the sea ice density result with the SAR and the optical image shows that the root mean square error is up to 26.2%), and the sea ice density result is used as a data base for identifying the ice lake, so that the identification result of the ice lake is larger in error. Therefore, a method for detecting the iced lake with high identification precision and small identification result error needs to be found
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides the method for detecting the iced lake based on FY-3MWRI polarization difference data combined with the maximum entropy algorithm, so that the precision of the detection result of the iced lake is effectively improved.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
the method comprises the steps of firstly carrying out preprocessing such as image splicing, cutting, masking, radiometric calibration and the like on FY-3MWRI89GHz vertical polarization data, processing to obtain the polarization difference P of each point of an image based on processed horizontal polarization brightness temperature data and vertical polarization brightness temperature data of a sea area to be researched, obtaining the optimal segmentation threshold T for researching sea area ice lake detection through a maximum entropy algorithm, and further obtaining a south pole ice lake detection identification map.
The invention provides a method for detecting an icebound lake based on FY-3MWRI data, which comprises the following steps:
step 1), acquiring FY-3MWRI primary data;
step 2), preprocessing the acquired FY-3MWRI primary data, wherein the preprocessing comprises image splicing and cutting, mask operation and radiometric calibration;
image splicing and clipping: inlaying and cutting a target image based on the geographical position of the south pole by using 89GHz data of a microwave imager (MWRI) of an FY-3 satellite as original data, and then performing 2% linear stretching;
mask operation: extracting mask data of the Antarctic continent, namely assigning a land area to be 0 and assigning a sea area to be 1, carrying out binarization, carrying out mask operation on the fused image by using the mask binarization data, assigning the land area to be 0 by using a formula (2), and keeping the value of the sea area unchanged to obtain data after the Antarctic continent mask;
radiation calibration: performing radiation correction on the masked data by using the formula (1);
radiometric calibration (-0.01) × b1 (1)
Step 3), utilizing the formula (2), and according to the 89GHz vertical polarization brightness temperature data (T) of FY-3MWRIbv) And horizontally polarized light temperature data (T)bh) Obtaining the polarization difference P of each pixel point;
P＝Tbv－Tbh (2)
and 4) segmenting the polarization difference P obtained in the step 3) through a maximum entropy algorithm to obtain an optimal segmentation threshold T for the identification of the intercrystalline lake, and further obtain a detection identification map of the Antarctic intercrystalline lake.
Further, as the optimization of the detection method of the iced lake based on the FY-3MWRI data, the calculation method of the optimal segmentation threshold T in step 4) is as follows:
calculating the information entropy H of the image data according to the formula (3), wherein p (x) represents the frequency of the bright temperature difference x;
Figure BDA0002926766660000031
selecting a threshold value T, and carrying out image segmentation, wherein all pixel points can be divided into two types, pixels of T lower than the threshold value are the background and are marked as B, and pixels of T higher than the threshold value are the object and are marked as O;
calculating the probability of each bright temperature difference in B or O class, and calculating according to a formula (4) based on the background and a formula (5) based on the object, wherein the two exist in a relation formula (6);
Figure BDA0002926766660000032
Figure BDA0002926766660000033
Figure BDA0002926766660000034
respectively calculating the information entropies of the background and the object, and then obtaining an information entropy formula (7) of B and an information entropy formula (8) of O;
Figure BDA0002926766660000035
Figure BDA0002926766660000036
and obtaining the optimal segmentation threshold T for identifying the icebound lake based on the formula (9).
T＝ar gmax(HB+HO) (9)
Further, as optimization of the detection method of the intercity lake based on the FY-3MWRI data, the Antarctic intercity lake detection identification map in the step 4) is obtained by processing 89GHz polarization difference P by a maximum entropy method to obtain a classification threshold T of the intercity lake and the sea ice, and then classifying images to obtain distribution of the intercity lake, wherein when a P value is larger than a T value, the pixel point is the intercity lake, and when the P value is smaller than the T value, the pixel point is the sea ice.
(III) advantageous effects
In the related research of the ice lake detection, data and related work used all the time are satellite data in foreign countries, such as AMSR-E data, the invention mainly utilizes data generated by domestic FY-3 satellites to carry out the Antarctic ice lake detection research, and makes a certain contribution to the development of domestic satellite data and research fields thereof. The invention provides an intercrystalline lake detection method based on FY-3MWRI data, which comprises the steps of firstly carrying out preprocessing such as image splicing and cutting, masking, radiometric calibration and the like on FY-3MWRI89GHz vertical polarization data, processing to obtain polarization difference P of each point of an image based on processed horizontal polarization brightness temperature data and vertical polarization brightness temperature data of a sea area to be researched, obtaining an optimal segmentation threshold T for researching sea area intercrystalline lake identification through a maximum entropy algorithm, and further obtaining a south pole intercrystalline lake detection identification image. The invention adopts polarization difference and maximum entropy algorithm to obtain the segmentation threshold value for identifying the intercrystalline lake, and aims at the problems that the actual sea ice distribution information is required to be known when the traditional sea ice density threshold value method is used for detecting the intercrystalline lake, the characteristics of a poor selection sample are innovated and supplemented, and the comparison with the result of the single sea ice density algorithm with fixed threshold value proves the feasibility of the method in the detection of the Antarctic intercrystalline lake, and compared with the latter, the processed product data has higher precision.
Drawings
FIG. 1 is a HakangQishi map of a research area;
FIG. 2 is a graph of the relationship between the sea water and sea ice brightness temperature values and the frequency;
FIG. 3 is a flow chart of the flow of the identification of the icebound lake with 89GHz data;
FIG. 4 is an image of the microwave imager (MWRI) of the FY-3 satellite at 89GHz, which is preprocessed with data of 2017, 10 months and 15 days as raw data;
FIG. 5 is an image of the acquired FY-3MWRI primary data after being preprocessed;
FIG. 6 is a diagram of the detection and identification of the iced lake by using polarization difference in combination with the maximum entropy method;
FIG. 7 is an image of an advanced microwave imager AMSR-E89 GHz brightness temperature data of AQUA satellite acquired in 2017, 10 months and 15 days after preprocessing;
fig. 8 is an inter-ice lake detection identification diagram obtained by using a sea ice density threshold method.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
The intercropping lake studied in this example was located in the sea of Hakangqi, which was named for the first king of Hakangqi, disassembled from Sweden, memorial to Norway. This sea area is located between the wedgelet sea and the latagrev sea, and the central area extends from the norway corner along the martian princess coast to the february ice shelf, close to this elementary meridian. The approximate coordinates of the sea area are 20W-45E, about 6270 miles in total, covered with ice for most of the year. As shown in fig. 1.
Objects, which are the basis for remote sensing inversion, may have completely different electromagnetic wave reflection or emission radiation characteristics due to differences in species, characteristics and environmental conditions, and such characteristics may be reflected using spectral characteristics. The spectral characteristics for sea water and sea ice are shown in fig. 2. When the physical temperature of the object is fixed, the magnitude of the bright temperature is only related to the radiance of the object under the same frequency. As can be seen from the definition of the polarization difference, when the horizontally polarized electromagnetic wave and the vertically polarized electromagnetic wave are emitted simultaneously from the surface of the same object, the physical temperature of the same object should be the same, and therefore the polarization difference is only related to the emissivity. As can be seen from the relationship result in fig. 2, in the several frequency bands of FY-3MWRI, the difference of polarization difference of 89GHz seawater is the largest compared with that of sea ice, i.e. the polarization difference P ═ T of 89GHz can be usedbv－TbhAnd identifying the seawater and the sea ice to further obtain the distribution of the ice lake. That is, if P ≧ T, this pixel is the iced lake, otherwise it is the sea ice.
Firstly, preprocessing FY-3MWRI89GHz vertical and horizontal polarization data such as image splicing cutting, masking, radiometric calibration and the like, processing to obtain polarization difference P of each point of an image based on the processed horizontal polarization brightness temperature data and vertical polarization brightness temperature data of a sea area to be researched, obtaining an optimal segmentation threshold T for researching sea area ice lake detection through a maximum entropy algorithm, and further obtaining a south pole ice lake detection identification diagram, wherein the flowchart is shown in fig. 3.
Preprocessing the data by using AMSR-E89 GHz brightness temperature data of an AQUA satellite advanced microwave imager, performing inversion by using an ASI algorithm to obtain sea ice density, combining a traditional fixed threshold value method to obtain a south pole icebound lake detection identification map based on the sea ice density, and finally comparing and verifying the sea ice density and the south pole icebound lake detection identification map to show whether the improved algorithm is suitable for south pole icebound lake detection research. The specific procedure is as follows.
The invention provides a method for detecting an icebound lake based on FY-3MWRI data, which comprises the following steps:
(1) acquiring FY-3MWRI primary data
The research on the detection of the Antarctic glacier lake is carried out by using 89GHz horizontal vertical polarization data of FY-3 satellite (MWRI) of 15 days in 10 months in 2017, and the main system parameters of the FY-3MWRI microwave imager are shown in Table 1.
TABLE 1 FY-3MWRI microwave imager main system parameters
Figure BDA0002926766660000051
Figure BDA0002926766660000061
(2) And preprocessing the acquired FY-3MWRI primary data, including image splicing and cutting, mask operation and radiometric calibration.
Image stitching and cropping: inlaying and cutting a target image based on the geographical position of the south pole by using data of 2017, 10 months and 15 days of 89GHz of microwave imager (MWRI) of FY-3 satellite as original data, and then performing 2% linear stretching, as shown in FIG. 4;
mask operation: extracting mask data of the Antarctic continent, namely assigning a land area to be 0 and assigning a sea area to be 1, carrying out binarization, carrying out mask operation on the fused image by using the mask binarization data, assigning the land area to be 0 and keeping the value of the sea area unchanged to obtain the data after the Antarctic continent mask;
radiation calibration: the masked data was subjected to radiation correction using equation (1), as shown in fig. 5. (ii) a
Radiometric calibration (-0.01) × b1 (1)
(3) Difference in polarization
Vertical polarization luminance temperature data (T) of 89GHz according to FY-3MWRI using equation (2)bv) And horizontally polarized light temperature data (T)bh) And solving the polarization difference P of each pixel point.
P＝Tbv－Tbh (2)
(4) Image segmentation combined with maximum entropy algorithm
And (3) calculating the polarization difference data obtained in the step (3) by using a maximum entropy algorithm, and integrating the P value of each point to finally obtain an optimal segmentation threshold T of the data as 0.36, so as to obtain a south pole icerya lake detection identification diagram, as shown in fig. 6.
The calculation method of the optimal segmentation threshold T comprises the following steps:
obtaining the information entropy H of the polarization difference P data according to the formula (3), wherein P (x) represents the frequency of the occurrence of the bright temperature difference;
Figure BDA0002926766660000062
selecting a threshold value T, and carrying out image segmentation, wherein all pixel points can be divided into two types, pixels of T lower than the threshold value are the background and are marked as B, and pixels of T higher than the threshold value are the object and are marked as O;
calculating the probability of each bright temperature difference in B or O class, and calculating according to a formula (4) based on the background and a formula (5) based on the object, wherein the two exist in a relation formula (6);
Figure BDA0002926766660000063
Figure BDA0002926766660000071
Figure BDA0002926766660000072
respectively calculating the information entropies of the background and the object, and then obtaining an information entropy formula (7) of B and an information entropy formula (8) of O;
Figure BDA0002926766660000073
Figure BDA0002926766660000074
and obtaining the optimal segmentation threshold T for identifying the icebound lake based on the formula (9).
T＝argmax(HB+HO) (9)
(5) Comparison and verification
Acquiring AMSR-E89 GHz brightness temperature data of the AQUA satellite advanced microwave imager in 2017, 10 months and 15 days, wherein the main system parameters of the AMSR-E89 GHz microwave imager are shown in table 2.
TABLE 2 AMSR-E89 GHz microwave imager main system parameters
Figure BDA0002926766660000075
The projection of AMSR-E data is different from that of the FY-3 satellite, the projection correction needs to be carried out by using the formula (10), and the rest of the required preprocessing is the same as that of the FY-3 satellite data. The preprocessed AMSR-E data are finally shown in fig. 7.
Projection correction ═ (b 1-1) × (-1) (10)
According to the AMSR-E89 GHz brightness temperature data of the AQUA satellite advanced microwave imager in 2017, 10, 15 and combined with an ASI algorithm, a sea ice intensity image is obtained, and a detection and identification map for the icebound lake is obtained by combining a traditional threshold segmentation algorithm (the threshold T is 0.75), as shown in FIG. 8.
Based on the 89GHz horizontally polarized bright temperature data and the vertically polarized bright temperature data of the Antarctic FY-3MWRI in 2017, 10, 15 and the combination of polarization difference and the maximum entropy method, the detection and identification result of the glacier lake is shown in fig. 6, and the detection and identification result of the glacier lake based on the traditional threshold method of sea ice concentration is shown in fig. 8.
The total area of the icehouse lake in FIG. 6 is about 2.3700 km2In FIG. 8, the total area of the icehouse lake is about 2.8230 km2. As can be seen from FIGS. 6 and 8, the difference between the area size and distribution of the iced lake and the distribution of the iced lake is not great, but the distribution of partial marginal areas is different. The distribution of the iced lake in the upper right circle of FIG. 6 does not have the phenomenon that a plurality of points are scattered and distributed without concentration; the distribution of the icebound lake in the upper right circle of fig. 8 shows the phenomenon that a plurality of single freezing points are scattered. FIG. 6 shows the distribution of the intercity lake in the right middle circle of the figure showing a state of natural disorder, tortuosity and changeability; the distribution of the intercity lake in the right middle circle of fig. 8 shows a state of geometric order.
From the above comparison, it can be seen that: the method for identifying the intercrystalline lake by combining the polarization difference with the maximum entropy method is effective and feasible, and according to the distribution information of the intercrystalline lake, the method for detecting the intercrystalline lake based on the FY-3MWRI data has higher precision.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (3)

1. A method for detecting an iced lake based on FY-3MWRI data is characterized by comprising the following steps:
step 1), acquiring FY-3MWRI primary data;
step 2), preprocessing the acquired FY-3MWRI primary data, wherein the preprocessing comprises image splicing and cutting, mask operation and radiometric calibration;
image splicing and clipping: inlaying and cutting a target image based on the geographical position of the south pole by using 89GHz data of a microwave imager (MWRI) of an FY-3 satellite as original data, and then performing 2% linear stretching;
masking: extracting mask data of the Antarctic continent, namely assigning a land area to be 0 and assigning a sea area to be 1, carrying out binarization, carrying out mask operation on the fused image by using the mask binarization data, assigning the land area to be 0 and keeping the value of the sea area unchanged to obtain the data after the Antarctic continent mask;
radiation calibration: performing radiation correction on the masked data by using the formula (1);
radiometric calibration (-0.01) × b1 (1)
Step 3), utilizing the formula (2) to carry out vertical polarization brightness temperature data (T) of 89GHz on the FY-3MWRIbv) And horizontally polarized light temperature data (T)bh) Solving the polarization difference P of each pixel point;
P＝Tbv－Tbh (2)
and 4) segmenting the polarization difference P obtained in the step 3) through a maximum entropy algorithm to obtain an optimal segmentation threshold T for the ice-lake identification, and further obtaining a south pole ice-lake detection identification map.
2. The method for detecting the iced lake based on the FY-3MWRI data according to claim 1, wherein the method for calculating the optimal segmentation threshold T in the step 4) comprises the following steps:
calculating the information entropy H of the polarization difference P data according to the formula (3), wherein P (x) represents the frequency of the bright temperature difference x;
Figure FDA0002926766650000011
selecting a threshold value T, and carrying out image segmentation, wherein all pixel points can be divided into two types, pixels of T lower than the threshold value are the background and are marked as B, and pixels of T higher than the threshold value are the object and are marked as O;
calculating the probability of each bright temperature difference in B or O class, and calculating according to a formula (4) based on the background and a formula (5) based on the object, wherein the two exist in a relation formula (6);
Figure FDA0002926766650000012
Figure FDA0002926766650000021
Figure FDA0002926766650000022
respectively calculating the information entropies of the background and the object, and then obtaining an information entropy formula (7) of B and an information entropy formula (8) of O;
Figure FDA0002926766650000023
Figure FDA0002926766650000024
and obtaining the optimal segmentation threshold T for identifying the icebound lake based on the formula (9).
T＝argmax(HB+HO) (9)
3. The method according to claim 1, wherein the Antarctic icebound lake detection identification chart in step 4) is obtained by processing 89GHz polarization difference P by a maximum entropy method to obtain a classification threshold T of the icebound lake and the sea ice, and further classifying the image to obtain distribution of the icebound lake, wherein when the P value is greater than the T value, the pixel point is the icebound lake, and when the P value is less than the T value, the pixel point is the sea ice.
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Application publication date: 20210518