CN113610189B - Remote sensing classification method and device for sea ice and sea water and electronic equipment - Google Patents
Remote sensing classification method and device for sea ice and sea water and electronic equipment Download PDFInfo
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Abstract
The invention provides a remote sensing classification method, a remote sensing classification device and electronic equipment for sea ice and seawater, and relates to the technical field of remote sensing application, wherein the remote sensing classification method comprises the steps of obtaining radar altimeter data, corrected radiometer data and sea ice intensity data in a specified time of a target sea area; determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data; carrying out graphical processing on the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located; and correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region. The embodiment improves the efficiency and the precision of sea ice and seawater classification by combining radar altimeter data and corrected radiometric data.
Description
Technical Field
The invention relates to the technical field of remote sensing application, in particular to a remote sensing classification method and device for sea ice and sea water and electronic equipment.
Background
Sea ice is an amplifier of global climate change, and the change of polar sea ice has important influence on global climate change, so that the accurate distinguishing of the regions of sea ice and seawater has important significance. Early sea ice and sea water type data can only be obtained through field investigation, and sea water and sea ice are classified later according to optical and SAR remote sensing images or information such as echo waveforms of a satellite radar altimeter. However, the existing HY-2 series satellite-based correction radiometer and radar altimeter combined use classification technology is single, and sea ice and sea water cannot be accurately and efficiently classified.
Disclosure of Invention
In view of the above problems in the prior art, an object of the present invention is to provide a method, an apparatus and an electronic device for remote sensing classification of sea ice and seawater, so as to improve the efficiency and accuracy of sea ice and seawater classification.
In a first aspect, an embodiment of the present invention provides a method for remote sensing classification of sea ice and seawater, where the method includes: acquiring radar altimeter data, corrected radiometer data and sea ice intensity data in a designated time of a target sea area; determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data; carrying out graphical processing on the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located; and correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region.
In an alternative embodiment, the radar altimeter data includes waveform data, first position data of the waveform data, and first time data of the waveform data; the corrected radiation count data comprises brightness temperature value data, second position data of the brightness temperature value data and second time data of the brightness temperature value data; determining an initial classification result of the sea ice and the sea water according to the radar altimeter data and the correction radiometer data, wherein the step of performing time and space matching on the waveform data and the brightness temperature value data according to the first position data, the second position data, the first time data and the second time data to obtain a clustering distribution range of the sea ice and the sea water in a target sea area; and dividing the cluster distribution range according to a preset rule to obtain an initial classification result.
In an optional embodiment, the step of performing time and space matching on the waveform data and the brightness temperature data according to the first position data and the second position data, and the first time data and the second time data to obtain the clustered distribution range of the sea ice and the sea water in the target ocean area includes: calculating according to the waveform data to obtain a backscattering coefficient; and matching the backscattering coefficient and the brightness temperature value data which are the same in position and time to obtain a clustering distribution range.
In an optional embodiment, the step of graphically processing the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located includes: carrying out data screening on the sea ice density data to obtain screened density data; carrying out binarization processing on the screened density data by utilizing a graphics processing technology to obtain a processing result; according to the processing result, the area of the edge position of the sea ice and the sea water is obtained.
In an optional embodiment, the step of performing binarization processing on the filtered intensity data by using a graphics processing technique to obtain a processing result includes: respectively carrying out corrosion calculation on the edge of the sea ice and the edge of the sea water in the screened density data according to a preset rule to obtain a first corrosion result and a second corrosion result; and obtaining a processing result according to the difference value of the first corrosion result and the second corrosion result.
In an alternative embodiment, the step of obtaining the area of the edge position of the sea ice and the sea water according to the processing result includes: and calculating to obtain a pulse peak value of the waveform characteristic parameters through waveform data, and constraining a processing result according to the pulse peak value to obtain the region of the edge position.
In an optional embodiment, the step of obtaining the pulse peak value of the waveform characteristic parameter by waveform data calculation includes:
wherein pp (pulse peak) represents a pulse peak value; pmaxMaximum echo energy values of 21 st to 108 th range gates representing waveform data;
represents the sum of all energy values between the 21 st to 108 th range gates in the waveform data; 88 represents the number of range gates.
In a second aspect, an embodiment of the present invention further provides a device for remote sensing classification of sea ice and sea water, where the device includes: the data acquisition module is used for acquiring radar altimeter data, corrected radiometer data and sea ice intensity data in the designated time of the target ocean area; the first classification module is used for determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data; the sea ice edge determining module is used for carrying out graphical processing on the sea ice density data to obtain the area of the edge positions of the sea ice and the sea water; and the correction module is used for correcting the initial classification result in the area where the sea ice sea water is located to obtain the final classification result of the sea ice sea water in the target sea area.
In an optional embodiment, the first classification module is further configured to: the radar altimeter data includes waveform data, first position data, and first time data; the corrected radiation count data includes brightness temperature value data, second position data, and second time data; according to the first position data, the second position data, the first time data and the second time data, time and space matching is conducted on the waveform data and the brightness temperature value data, and the clustering distribution range of the sea ice and the sea water in the target ocean area is obtained; and dividing the cluster distribution range according to a preset rule to obtain an initial classification result.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, where the memory stores machine executable instructions capable of being executed by the processor, and the processor executes the machine executable instructions to implement any one of the above methods for remote sensing classification of sea ice and sea water.
The embodiment of the invention has the following beneficial effects:
the invention provides a remote sensing classification method, a remote sensing classification device and electronic equipment for sea ice and seawater, and relates to the technical field of remote sensing application, wherein the remote sensing classification method comprises the steps of obtaining radar altimeter data, corrected radiometer data and sea ice intensity data in a specified time of a target sea area; determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data; carrying out graphical processing on the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located; and correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region. According to the method, the efficiency and the precision of sea ice and seawater classification are improved by combining radar altimeter data with radiometric data.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention as set forth above.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a remote sensing classification method for sea ice and seawater according to an embodiment of the present invention;
FIG. 2 is a flow chart of another remote sensing classification method for sea ice and seawater according to an embodiment of the present invention;
FIG. 3 is a scattering coefficient-luminance temperature value joint histogram according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a preliminary boundary line of sea ice according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a region where an edge of ice water is located according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a remote sensing classification device for sea ice and sea water according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an electronic device according to 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 clearer, 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 some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
Arctic sea ice is an amplifier of global climate change, and changes in arctic sea ice have a significant effect on global climate change. The radar altimeter has important significance for sea ice detection. Early sea ice and sea water type data can only be obtained through field investigation, and sea water and sea ice are classified later according to optical and SAR remote sensing images or information such as echo waveforms of a satellite radar altimeter. However, the current classification technology is single, and sea ice and sea water cannot be accurately and efficiently classified.
In the prior art, radiometers for atmosphere correction mainly provide co-range measurement atmosphere parameters for radar altimeters, and are still radiometer measuring instruments for earth observation, and are not used in the technical field of remote sensing. According to the method, the calibration radiometer is introduced to the field of remote sensing, the calibration radiometer is used for identifying the sea ice and the sea water, and the technical problem that the radiometer for calibration is difficult to accurately identify the discrete small floating ice in the edge area of the sea ice is solved through constraint processing.
The marine environment power satellite is provided with a radar altimeter and an atmosphere correction radiometer for on-range measurement. The HY-2B satellite is a polar orbit satellite with an inclination angle of 98 degrees, the orbit sub-satellite point can cover most of the area of the two polar regions, and a new data source can be provided for polar region research.
Based on the above problems, embodiments of the present invention provide a method, an apparatus, and an electronic device for remote sensing classification of sea ice and sea water. The sea ice and seawater are classified and extracted more accurately in a mode of combining HY-2B satellite height measurement data and corrected radiometric data. The technology can be applied to ocean remote sensing monitoring scenes, in particular to classification scenes of sea ice and sea water in monitoring.
Example one
In order to facilitate understanding of the embodiment of the present invention, a method for remotely classifying sea ice and seawater disclosed by the embodiment of the present invention is first described in detail, and as shown in fig. 1, the method includes the following steps:
step S102, radar altimeter data, corrected radiometer data and sea ice intensity data in the designated time of the target sea area are obtained.
In a specific implementation, the target sea area may be any sea area on the earth. The radar altimeter data may be radar altimeter L1B data for a period of HY-2B acquired from a satellite marine application center. The corrected radiometric data may be corrected radiometer L1B data acquired from a satellite marine application center. Sea Ice concentration Data is obtained from OSI-SAF (Ocean and Sea Ice-Satellite Application Facility) database, NSIDC (National Snow and Ice Data Center) database.
And step S104, determining an initial classification result of the sea ice and the sea water according to the radar altimeter data and the corrected radiometer data.
During specific implementation, calculating by using the waveform data of the echo in the radar altimeter data to obtain a backscattering coefficient; then extracting brightness temperature value data from the data of the correction radiometer; and finally, carrying out time and space matching on the backscattering coefficient and the brightness temperature value data to obtain an initial classification result, namely a coarse classification result.
And S106, carrying out graphical processing on the sea ice density data to obtain the area of the edge positions of the sea ice and the sea water.
In the concrete implementation, the ice edge line is corroded by a graphical processing method, and then the observation data near the sea ice edge is subjected to fine ice-water separation to obtain the area of the sea ice edge position.
And S108, correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region.
In the concrete implementation, the original rough classification result (obtained in step S104) is corrected by using the fine classification result (obtained in step S106) of the ice water edge, and a more accurate ice water extraction and classification result is obtained by combining the radar altimeter with the corrected radiometric data.
The embodiment of the invention provides a remote sensing classification method of sea ice and seawater, which comprises the steps of obtaining radar altimeter data, corrected radiometer data and sea ice intensity data in a designated time of a target sea area; determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data; carrying out graphical processing on the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located; and correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region. The extraction and conversion of the related data are realized through a python platform, so that the method has better platform portability and simple operation; by means of the combination of the radar altimeter and the corrected radiometric data, the efficiency and the precision of ice water classification are improved.
Example two
The embodiment of the invention also provides another remote sensing classification method for sea ice and seawater, which is realized on the basis of the method of the embodiment; as shown in fig. 2; the method comprises the following specific steps:
step S202, radar altimeter data and corrected radiometer data in the designated time of the target ocean area are obtained.
In a specific implementation, the specified time may be a pre-specified time period, such as a year, a month, etc. The radar altimeter data includes waveform data, first position data of the waveform data, and first time data of the waveform data; the corrected radiation count data includes bright temperature value data, second position data of the bright temperature value data, and second time data of the bright temperature value data.
Specifically, the method for extracting the data comprises the following steps: and matching time marks (first time data and second time data) recorded in the radar altimeter and the corrected radiometer data to obtain detection data and position information (first position data and second position data) of the radar altimeter and the corrected radiometer at the same time, and extracting waveform data of the radar altimeter corresponding to the position point.
And S204, performing time and space matching on the waveform data and the brightness temperature value data according to the first position data, the second position data, the first time data and the second time data to obtain the clustering distribution range of the sea ice and the sea water in the target ocean area.
During specific implementation, calculating to obtain a backscattering coefficient according to waveform data of an echo in the radar altimeter; and matching the backscattering coefficient and the brightness temperature value data which are the same in position and time to obtain a clustering distribution range.
Specifically, a scattering coefficient-brightness temperature value combined histogram is obtained by using the backscattering coefficient and the brightness temperature value, and as shown in fig. 3, a clustering distribution range naturally formed by sea ice and seawater at 18.7GHz in a specific year is obtained. Fig. 3 contains a plot of the cluster distribution for each month of 12 months of 2019, plotted separately for the top and bottom of each month, so that there are two panels for each month, for a total of 24 panels in fig. 3. The abscissa of each plot is the backscattering coefficient (Backscatter coefficient in Ku band) in dB; the ordinate is Brightness temperature value data (Brightness temperature) in units of K; the right color bar represents the corresponding brightness temperature data and the observed Number of backscattering coefficients (numbers of observations) at the same position and in the same time period, and the darker the color, the more the observed Number is represented, and the easier it is to distinguish whether the area is sea ice or sea water.
And step S206, dividing the cluster distribution range according to a preset rule to obtain an initial classification result.
During specific implementation, distinguishing boundary points are defined through the range, a dividing line is fitted, and sea ice and sea water are roughly classified to obtain an initial classification result.
Specifically, data that the main distribution range of sea ice in the 18.7GHz bright temperature channel in a specific year is 150K or less and the bright temperature value of sea ice is mainly 200K or more can be obtained. In terms of backscattering coefficient, the backscattering coefficient is mainly distributed between 10-55 dB. And (10dB, 175K) and (55dB, 125K) are used as boundary points (namely preset rules), a dividing line is fitted in the histogram, and the sea ice and the sea water are roughly classified to obtain an initial classification result.
And S208, acquiring radar altimeter data and sea ice density data in the designated time of the target ocean area.
And step S210, carrying out data screening on the sea ice density data to obtain screened density data.
In the concrete implementation, according to the sea ice density data of OSI-SAF, 30% of the sea ice density is taken as the edge of one side of the sea ice, 0% of the sea ice density is taken as the edge line of one side of the sea water, as shown in FIG. 4, the density data is primarily screened to obtain a primary edge line. The color bar of fig. 4 represents the sea ice concentration (SeaIce concentration), and the higher the concentration value is, the easier it is to distinguish whether the area is sea water or sea ice, and the marginal area of sea ice and sea water can be preliminarily observed from fig. 4.
And step S212, carrying out binarization processing on the screened density data by utilizing a graphic processing technology to obtain a processing result.
During specific implementation, the preliminary ice edge line is corroded by a graphical processing technology to obtain a sea ice edge area, so that data of the sea ice edge area are extracted, and a processing result is obtained. Performing corrosion calculation on the edge of the sea ice and the edge of the sea water in the screened density data to obtain a first corrosion result and a second corrosion result; and obtaining a processing result according to the difference value of the first corrosion result and the second corrosion result. Specifically, the 5 × 5 square operator of the python toolkit CV2 is used for corroding the edges of the sea ice and the seawater, and the corrosion is subtracted from the corrosion to obtain the edge transition zone of the sea ice and the seawater with moderate width, so that the processing result is obtained.
And step S214, calculating the pulse peak value of the waveform characteristic parameter through the waveform data.
During specific implementation, the pulse peak value of the waveform characteristic parameter is obtained through waveform data calculation in the radar altimeter, and the specific formula is as follows:
wherein PP represents the pulse peak; pmaxMaximum echo energy values of 21 st to 108 th range gates representing waveform data;
represents the sum of all energy values between the 21 st to 108 th range gates in the waveform data; 88 represents the number of range gates.
And S216, constraining the processing result according to the pulse peak value to obtain the area of the edge position.
In a specific implementation, a classification threshold is defined by a pulse peak, and a fine Ice-water separation constraint is performed on the observation data near the Sea Ice edge (i.e., the processing result obtained in step S212) to obtain a region where the Ice-water edge position is located, as shown in fig. 5, the color bar in fig. 5 represents the Sea Ice concentration (Sea Ice concentration), and as the concentration value is higher, it is easier to distinguish whether the region is Sea water or Sea Ice, and the region where the edge position of the Sea Ice and Sea water is located can be clearly seen from fig. 5 by the constraint. Specifically, sea ice edge radar altimeter waveform data is extracted through longitude and latitude matching. And calculating to obtain a waveform characteristic parameter pulse crest value through waveform data. And dividing sea ice with the pulse crest value larger than 3 and seawater with the pulse crest value smaller than 3, and performing fine ice-water separation on the observation data near the edge of the sea ice to obtain the area of the edge position.
And S218, correcting the initial classification result in the region to obtain the final classification result of the sea ice and the sea water in the target ocean region.
In a specific implementation, the original rough classification result (the result obtained in step S206) is corrected by using the fine classification result (the result obtained in step S216) of the ice water edge, and a more accurate ice water extraction and classification result is obtained by combining the radar altimeter with the corrected radiometric data.
According to the remote sensing classification method for sea ice and seawater, the wave data in the radar altimeter, the backscattering coefficient, the brightness temperature value data in the correction radiometer, the seawater intensity data and other multi-aspect data are combined, so that the remote sensing classification method for sea ice and seawater is obtained, and the accuracy and the efficiency of classification and extraction of sea ice and seawater are improved.
EXAMPLE III
Corresponding to the above method embodiment, an embodiment of the present invention further provides a remote sensing classification apparatus for sea ice and sea water, as shown in fig. 6, the apparatus includes:
and the data acquisition module 61 is used for acquiring radar altimeter data, corrected radiometer data and sea ice intensity data in the designated time of the target sea area.
And a first classification module 62 for determining an initial classification result of the sea ice and the sea water according to the radar altimeter data and the corrected radiometer data.
The sea ice edge determining module 63 is used for performing graphical processing on the sea ice density data to obtain the area where the edge positions of the sea ice and the sea water are located;
and the correcting module 64 is used for correcting the initial classification result in the region where the sea ice sea water is located to obtain a final classification result of the sea ice sea water in the target sea region.
The first classification module 62 is further configured to: the radar altimeter data includes waveform data, first position data, and first time data; the corrected radiation count data includes brightness temperature value data, second position data, and second time data; according to the first position data, the second position data, the first time data and the second time data, time and space matching is conducted on the waveform data and the brightness temperature value data, and the clustering distribution range of the sea ice and the sea water in the target ocean area is obtained; and dividing the cluster distribution range according to a preset rule to obtain an initial classification result.
Further, the first classification module 62 is further configured to: calculating according to the waveform data to obtain a backscattering coefficient; and matching the backscattering coefficient and the brightness temperature value data which are the same in position and time to obtain a clustering distribution range.
Further, the sea ice edge determining module 63 is further configured to perform data screening on the sea ice density data to obtain screened density data; carrying out binarization processing on the screened density data by utilizing a graphics processing technology to obtain a processing result; according to the processing result, the area of the edge position of the sea ice and the sea water is obtained.
Further, the sea ice edge determining module 63 is further configured to perform corrosion calculation on the edge of the sea ice and the edge of the sea water in the screened density data according to a preset rule, so as to obtain a first corrosion result and a second corrosion result; and obtaining a processing result according to the difference value of the first corrosion result and the second corrosion result.
Further, the sea ice edge determining module 63 is further configured to obtain a pulse peak of the waveform characteristic parameter through waveform data calculation, and constrain a processing result according to the pulse peak to obtain a region where the edge position is located.
Further, the sea ice edge determining module 63 is further configured to calculate a pulse peak value of the waveform characteristic parameter through the waveform data, and includes the specific steps of:
wherein PP represents the pulse peak; pmaxMaximum echo energy values of 21 st to 108 th range gates representing waveform data;
represents the sum of all energy values between the 21 st to 108 th range gates in the waveform data; 88 represents the number of range gates.
The implementation principle and the generated technical effect of the remote sensing classification device for sea ice and seawater provided by the embodiment of the invention are the same as those of the embodiment of the remote sensing classification method for sea ice and seawater, and for brief description, corresponding contents in the embodiment of the method can be referred to where the embodiment of the device is not mentioned.
Example four
An embodiment of the present invention further provides an electronic device, which is shown in fig. 7 and includes a processor 101 and a memory 100, where the memory 100 stores machine executable instructions that can be executed by the processor, and the processor executes the machine executable instructions to implement any one of the above remote sensing classification methods for sea ice and sea water.
Further, the electronic device shown in fig. 7 further includes a bus 102 and a communication interface 103, and the processor 101, the communication interface 103, and the memory 100 are connected through the bus 102.
The Memory 100 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used. The bus 102 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
The processor 101 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 101. The Processor 101 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100, and completes the steps of the method of the foregoing embodiment in combination with the hardware thereof.
The embodiment of the present invention further provides a machine-readable storage medium, where the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are called and executed by a processor, the machine-executable instructions cause the processor to implement the method for remote sensing classification of sea ice and seawater.
The remote sensing classification method and device for sea ice and seawater and the computer program product of the electronic device provided by the embodiment of the invention comprise a computer readable storage medium storing program codes, instructions included in the program codes can be used for executing the method in the previous method embodiment, and specific implementation can refer to the method embodiment, and is not described herein again.
This functionality, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A remote sensing classification method for sea ice and seawater is characterized by comprising the following steps:
acquiring radar altimeter data, corrected radiometer data and sea ice intensity data in a designated time of a target sea area;
determining an initial classification result of sea ice and sea water according to the radar altimeter data and the corrected radiometer data;
performing graphical processing on the sea ice density data to obtain the area of the edge positions of the sea ice and the sea water;
and correcting the initial classification result in the region to obtain a final classification result of the sea ice and the sea water in the target ocean region.
2. The method of claim 1, wherein the radar altimeter data comprises waveform data, first position data of the waveform data, and first time data of the waveform data; the corrected radiation count data includes brightness temperature value data, second position data of the brightness temperature value data, and second time data of the brightness temperature value data;
the step of determining an initial classification result of sea ice and sea water according to the radar altimeter data and the corrected radiometer data includes:
according to the first position data, the second position data, the first time data and the second time data, performing time and space matching on the waveform data and the brightness temperature value data to obtain a clustering distribution range of sea ice and seawater in the target ocean area;
and dividing the cluster distribution range according to a preset rule to obtain the initial classification result.
3. The method according to claim 2, wherein the step of matching the waveform data and the brightness temperature data in time and space according to the first and second position data and the first and second time data to obtain the clustered distribution range of the sea ice and the sea water in the target sea area comprises:
calculating a backscattering coefficient according to the waveform data;
and matching the backscattering coefficients with the same position and time with the brightness temperature value data to obtain the cluster distribution range.
4. The method of claim 1, wherein the step of graphically processing the sea ice density data to obtain the area of the edge locations of the sea ice and the sea water comprises:
performing data screening on the sea ice density data to obtain screened density data;
carrying out binarization processing on the screened density data by utilizing a graphic processing technology to obtain a processing result;
and obtaining the area of the edge positions of the sea ice and the sea water according to the processing result.
5. The method as claimed in claim 4, wherein the step of performing binarization processing on the filtered intensity data by using a graphics processing technique to obtain a processing result comprises:
respectively carrying out corrosion calculation on the edge of the sea ice and the edge of the sea water in the screened density data according to a preset rule to obtain a first corrosion result and a second corrosion result;
and obtaining the processing result according to the difference value of the first corrosion result and the second corrosion result.
6. The method of claim 4, wherein the step of obtaining the area of the edge position of the sea ice and the sea water according to the processing result comprises:
and calculating to obtain a pulse peak value of the waveform characteristic parameters through waveform data, and constraining the processing result according to the pulse peak value to obtain the region of the edge position.
7. The method according to claim 6, wherein the step of calculating the pulse peak value of the waveform characteristic parameter from the waveform data comprises:
wherein PP represents the pulse peak; pmaxMaximum echo energy values of 21 st to 108 th range gates representing the waveform data;
represents the sum of all energy values between the 21 st to 108 th range gates in the waveform data; 88 represents the number of range gates.
8. A remote sensing classification apparatus for sea ice and sea water, the apparatus comprising:
the data acquisition module is used for acquiring radar altimeter data, corrected radiometer data and sea ice intensity data in the designated time of the target ocean area;
the first classification module is used for determining an initial classification result of sea ice and sea water according to the radar altimeter data and the correction radiometer data;
the sea ice edge determining module is used for carrying out graphical processing on the sea ice density data to obtain the area of the edge positions of the sea ice and the sea water;
and the correction module is used for correcting the initial classification result in the area to obtain a final classification result of the sea ice and seawater in the target ocean area.
9. The apparatus of claim 8, wherein the radar altimeter data comprises waveform data, first position data, and first time data; the corrected radiometric data includes brightness temperature value data, second location data, and second time data;
the first classification module is further to: according to the first position data, the second position data, the first time data and the second time data, time and space matching is conducted on waveform data and brightness temperature value data, and the clustering distribution range of sea ice and sea water in the target ocean area is obtained;
and dividing the cluster distribution range according to a preset rule to obtain the initial classification result.
10. An electronic device comprising a processor and a memory, the memory storing machine executable instructions executable by the processor, the processor executing the machine executable instructions to implement the method of remotely classifying sea ice and seawater of any one of claims 1 to 7.
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Inventor after: Jiang Chengfei Inventor after: Zeng Tao Inventor after: Lin Mingsen Inventor after: Cao Ruixue Inventor after: Ma Xiaofeng Inventor after: Jia Yongjun Inventor after: Wang Qimao Inventor after: Shi Lijian Inventor after: An Wentao Inventor after: Cui Limin Inventor before: Jiang Chengfei Inventor before: Zeng Tao Inventor before: Lin Mingsen Inventor before: Cao Ruixue Inventor before: Ma Xiaofeng Inventor before: Jia Yongjun Inventor before: Wang Qimao Inventor before: Shi Lijian Inventor before: An Wentao Inventor before: Cui Limin |