CN116522690B - Scientific data simulation method and device for new-generation marine satellite water color and temperature scanner - Google Patents
Scientific data simulation method and device for new-generation marine satellite water color and temperature scanner Download PDFInfo
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Abstract
The invention provides a new generation of scientific data simulation method and device for a marine satellite water color and temperature scanner, comprising the following steps: basic data of an area to be simulated is obtained, and a wave band to be interpolated is determined based on the wave band to which each pixel point in the basic data belongs; determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each band to be interpolated; and carrying out band interpolation on the basic data by utilizing the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated. The invention can effectively solve the problems of complex simulation process, huge calculation amount, larger implementation difficulty and the like in the existing simulation method of scientific data.
Description
Technical Field
The invention relates to the technical field of ocean engineering, in particular to a new generation of scientific data simulation method and device for an ocean satellite water color and temperature scanner.
Background
The simulation technology of the satellite scientific data aims at simulating various in-orbit behaviors of the satellite through the simulation of the satellite scientific data before the satellite is formally transmitted, and can reproduce the whole process of satellite observation. Generating 0-level data required by system software development and debugging such as data preprocessing, product generation and the like. Thereby verifying whether the design of satellite load meets the index requirements.
However, the current simulation method for the scientific data of the marine water color satellite is mostly based on a forward simulation method of radiation transmission, the method needs to calculate the contribution of each component in the atmosphere to the total zenith radiance item by item, and relates to various physical processes in the atmosphere, so that the simulation process is complex, and part of environmental parameters need to be measured in the field and are difficult to obtain, so that the current simulation method for the scientific data has the problems of complex simulation process, huge calculation amount, high implementation difficulty and the like.
Disclosure of Invention
In view of the above, the invention aims to provide a new generation of scientific data simulation method and device for a marine satellite water color and temperature scanner, which can effectively solve the problems of complex simulation process, huge calculation amount, higher implementation difficulty and the like in the existing scientific data simulation method.
In a first aspect, an embodiment of the present invention provides a new generation of scientific data simulation method for a marine satellite water color and temperature scanner, including:
basic data of an area to be simulated is obtained, and a wave band to be interpolated is determined based on a wave band to which each pixel point in the basic data belongs;
determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each wave band to be interpolated;
and carrying out band interpolation on the basic data by utilizing the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated.
In one embodiment, determining a band to be interpolated based on a band to which each pixel point in the basic data belongs includes:
traversing the basic data to obtain the invalid value row number of the basic data;
for each pixel point in the basic data, if the value of the pixel point is an invalid value, determining a target pixel point from the pixel points contained in the basic data according to the number of the invalid value rows, and assigning the value of the target pixel point to the pixel point to obtain updated basic data;
Performing resolution interpolation processing on the updated basic data to obtain the interpolated basic data;
performing format conversion on the interpolated basic data to obtain the basic data in a specified format;
if a wave band matched with a preset wave band exists in the wave band to which each pixel point belongs in the basic data in a specified format, determining the value of the wave band matched with the preset wave band as simulation scientific data; if the wave band matched with the preset wave band does not exist in the wave band of each pixel point in the basic data in the appointed format, determining the preset wave band as the wave band to be interpolated.
In one embodiment, determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated includes:
performing format conversion processing on the simulation model construction data of the region to be simulated to obtain simulation model construction data in a specified format;
based on the simulation model construction data in the specified format, determining remote sensing reflectivity average values corresponding to a plurality of ground object types;
and fitting an interpolation model corresponding to each ground object type based on the mapping relation between the remote sensing reflectivity average value corresponding to the ground object type and the wavelength.
In one embodiment, determining the average value of the remote sensing reflectances corresponding to the plurality of ground object types based on the simulation model construction data in the specified format includes:
determining geographic ranges corresponding to a plurality of ground object types in the simulation model construction data in the specified format;
and for each ground object type, extracting the remote sensing reflectivity of a pixel point corresponding to the ground object type from the simulation model construction data in the specified format according to the geographic range corresponding to the ground object type, and determining the average value of the remote sensing reflectivity of each pixel point as the average value of the remote sensing reflectivity of the ground object type.
In one embodiment, determining, based on each interpolation model, a set of interpolation coefficients corresponding to each of the feature types includes:
taking a band closest to each band to be interpolated in the basic data in a specified format as a normalized base band corresponding to each band to be interpolated;
substituting each wave band to be interpolated into the interpolation model corresponding to the ground object type to obtain a fitting value of each wave band to be interpolated; substituting the normalized base number wave bands corresponding to each wave band to be interpolated into the interpolation model corresponding to the ground feature type to obtain the normalized base number of each wave band to be interpolated;
And determining the quotient of the fitting value and the normalized base number of each band to be interpolated as an interpolation coefficient corresponding to each band to be interpolated, so as to construct and obtain an interpolation coefficient set corresponding to the ground object type based on the interpolation coefficient corresponding to each band to be interpolated.
In one embodiment, performing band interpolation on the basic data by using the interpolation coefficient set corresponding to each feature type to obtain simulation scientific data corresponding to the to-be-interpolated band, where the method includes:
for each pixel point in the basic data, determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point;
and collecting the interpolation coefficient set corresponding to the ground object type to which the pixel point belongs, wherein the product of the interpolation coefficient corresponding to each band to be interpolated and the value of the band to which the pixel point belongs is used as simulation scientific data corresponding to the band to be interpolated.
In one embodiment, the surface feature type includes one or more of a cloud type, a land type, and a water body type; determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point comprises the following steps:
If the remote sensing reflectivity corresponding to the pixel point is larger than the Yun Yaogan reflectivity average value, determining that the pixel point belongs to the cloud body type;
if the remote sensing reflectivity corresponding to the pixel point is smaller than the Yun Yaogan reflectivity average value and larger than the land remote sensing reflectivity average value, determining that the pixel point belongs to the land type;
and if the remote sensing reflectivity corresponding to the pixel point is smaller than the land remote sensing reflectivity average value, determining that the pixel point belongs to the water body type.
In one embodiment, the basic data is data acquired by a visible infrared imaging radiometer, and the simulation model construction data is data acquired by a sea Liu Sedu radiometer.
In a second aspect, the embodiment of the invention also provides a new generation of scientific data simulation device for a marine satellite water color and temperature scanner, which comprises:
the wave band determining module is used for acquiring basic data of the area to be simulated and determining a wave band to be interpolated based on the wave band of each pixel point in the basic data;
the coefficient determining module is used for determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the area to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each wave band to be interpolated;
And the interpolation module is used for carrying out band interpolation on the basic data by utilizing the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated.
In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory storing computer-executable instructions executable by the processor to implement the method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspects.
According to the scientific data simulation method and device for the new generation marine satellite water color and water temperature scanner, after basic data of a region to be simulated are obtained, a wave band to be interpolated is determined based on the wave band to which each pixel point in the basic data belongs, then interpolation models corresponding to a plurality of ground object types are determined based on simulation model construction data of the region to be simulated, then interpolation coefficient sets (comprising interpolation coefficients corresponding to each wave band to be interpolated) corresponding to each ground object type are determined based on each interpolation model, and finally wave band interpolation is carried out on the basic data by utilizing the interpolation coefficient sets corresponding to each ground object type, so that simulated scientific data corresponding to the wave band to be interpolated is obtained. According to the method, after the wave band to be interpolated is determined based on the wave band to which each pixel point in the basic data belongs, the simulation model is used for constructing the interpolation model corresponding to a plurality of ground object types, further, the interpolation coefficient corresponding to each ground object type and each wave band to be interpolated is determined, and the wave band interpolation is carried out on the basic data by using the interpolation coefficient.
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 practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more 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 that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a new generation of scientific data simulation method for a marine satellite water color and temperature scanner provided by the embodiment of the invention;
FIG. 2 is a schematic flow chart of another method for simulating scientific data of a new generation of marine satellite water color and temperature scanner according to the embodiment of the invention;
FIG. 3 is a schematic structural diagram of a new generation of scientific data simulator for a marine satellite water color and temperature scanner according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the existing simulation method of the scientific data has the problems of complex simulation process, huge calculation amount, larger implementation difficulty and the like, and based on the simulation method and the device, the invention provides a new generation of simulation method and device for the scientific data of the marine satellite water color and temperature scanner, which can effectively relieve the problems of complex simulation process, huge calculation amount, larger implementation difficulty and the like in the existing simulation method of the scientific data.
For the convenience of understanding the present embodiment, first, a new generation of scientific data simulation method for a marine satellite water color and temperature scanner disclosed in the present embodiment of the present invention will be described in detail, referring to a flow chart of a new generation of scientific data simulation method for a marine satellite water color and temperature scanner shown in fig. 1, the method mainly includes the following steps S102 to S106:
Step S102, basic data of a region to be simulated is obtained, and a wave band to be interpolated is determined based on the wave band to which each pixel point in the basic data belongs. The basic data may also be referred to as basic model construction data, where the basic data is data collected by a visible light infrared imaging radiometer (Suomi NPP VIIRS, hereinafter referred to as VIIRS), so that the basic data is VIIRS SDR data; the wave band to be interpolated is the wave band which can not directly obtain the corresponding value from the basic data.
In one embodiment, the preprocessing may be performed on the basic data, where the preprocessing includes a stripe removal process, a resolution interpolation and a format conversion, and then the preprocessed basic data is matched with a preset wave band, where the preset wave band is determined based on a wave band design requirement of a new generation marine satellite water color water temperature scanner, if a wave band to which a pixel point in the preprocessed basic data belongs exists and corresponds to the preset wave band, the wave band data in the basic data may be directly used as scientific simulation data, and if a wave band to which the pixel point in the preprocessed basic data does not exist and corresponds to the preset wave band, the preset wave band may be determined to be the wave band to be interpolated.
Step S104, determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model. The simulation model construction data are data collected by a Sentinel No. 3 satellite sea Liu Sedu instrument (Sentinel-3 OLCI), so that the basic data are Sentinel-3L 1A data; the ground object type may include one or more of a cloud type, a land type, and a water body type; the interpolation coefficient set comprises interpolation coefficients corresponding to each band to be interpolated.
In one embodiment, data can be built based on a simulation model, and the average value of the remote sensing reflectivity of various ground object types in each wave band can be determined; determining interpolation models of various ground object types based on remote sensing reflectivity average values of various ground object types; and determining interpolation coefficients of different to-be-interpolated wave bands of the various ground object types based on interpolation models of the various ground object types.
And S106, performing band interpolation on the basic data by utilizing the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated.
In one embodiment, the pixel points in the basic data can be classified into three types of cloud, land and water to obtain a basic data classification result; and based on the basic data classification result, calculating according to interpolation coefficients of different to-be-interpolated wave bands of various ground object types and values of corresponding wave bands of the basic data to obtain final simulation scientific data.
According to the scientific data simulation method for the new generation ocean satellite water color and water temperature scanner, after the wave band to be interpolated is determined based on the wave band to which each pixel point in the basic data belongs, the simulation model is utilized to construct interpolation models corresponding to a plurality of ground object types, further interpolation coefficients corresponding to each ground object type and each wave band to be interpolated are determined, and the wave band interpolation is carried out on the basic data by utilizing the interpolation coefficients.
In order to facilitate understanding of the above embodiments, the embodiment of the present invention provides a specific implementation manner of a new generation of scientific data simulation method for a marine satellite water color and temperature scanner.
For the foregoing step S102, when the step of acquiring the basic data of the area to be simulated and determining the band to be interpolated based on the band to which each pixel point in the basic data belongs is performed, the following steps a1 to a5 may be referred to:
and a1, acquiring basic data of a region to be simulated and simulation model construction data.
The new generation of marine water color satellites are used for continuing the existing HY-1C/D task, and compared with the existing marine satellites, the new generation of marine water color satellites are added with 6 wave bands, so that sea surface temperature monitoring can be carried out, and atmospheric correction can be carried out more accurately.
According to the channel configuration of a new-generation ocean water color satellite water color water temperature scanner, currently selected satellite loads are Sentinel-3 OLCI (sea Liu Sedu instrument) and Suomi NPP VIIRS (visible light infrared imaging radiation instrument, VIIRS for short) which are taken as satellite data input sources. As the orbit height and the observation geometry of the satellite are similar to those of the new-generation ocean water color and temperature scanner, the full coverage of the wave bands of the new-generation ocean water color and temperature scanner can be realized through the wave band combination of the load.
Sentinel-3, suomi NPPs carrying VIIRS SDR adopt solar synchronous tracks, and the track heights are 814.5km and 830km respectively; the new generation ocean water color and temperature scanner adopts a solar synchronous track, and the track height is 782km. It can be approximately considered that Sentinel-3 OLCI,VIIRS SDR,HY-1C has similar observation geometry as the new-generation ocean water temperature scanner, and under the same observation scene, the observation data of the new-generation ocean water temperature scanner should have similar performance as the satellite data. Therefore, the satellite data can be used for realizing scientific data simulation of a new generation of ocean water color and temperature scanners.
The Sentinel-3 OLCI data band range is 400 nm-10200 nm, and the VIIRS SDR data band range is 400nm-13000nm. The data combination can realize the full coverage of the wave band requirement of the new generation ocean water color and water temperature scanner, but can not completely and accurately correspond to certain wave bands, and can be obtained by interpolating the corresponding values of the existing wave bands when inverting the wave bands which are critical to the inversion of three elements (chlorophyll, sediment and yellow substances) of the ocean water color and the new wave band of the new generation ocean water color and water temperature scanner.
In addition, the space resolution of the Sentinel-3 OLCI and VIIRS SDR is different from that of a new generation ocean water color water temperature scanner, the space resolution of the dots under the satellite of the Sentinel-3 OLCI full resolution data is 300M, 5 high resolution image channels (I wave band, resolution is 375M) and 6 medium resolution channels (M wave band, resolution is 750M) are arranged in the VIIRS SDR data. The above-mentioned load data needs to be interpolated to match the spatial resolution requirement (i.e., 500 m) of the scientific data of the new generation ocean water color and temperature scanner.
Step a2, traversing the basic data to obtain the invalid value row number of the basic data; and for each pixel point in the basic data, if the value of the pixel point is an invalid value, determining a target pixel point from the pixel points contained in the basic data according to the number of invalid value rows, and giving the value of the target pixel point to the pixel point to obtain updated basic data.
The step a2 is strip removal. In specific implementation, the VIIRS SDR data distributed by NOAA CLASS is used as original data, stripe removal processing is carried out on the original data, firstly, the original data is traversed, the number of invalid value lines is counted and stored into an invalid value line number array, and after the traversing is finished, the maximum value is selected from the invalid value line number array to be used as the number of invalid value lines.
Further, traversing the original data, judging whether each pixel point is an invalid value, if so, assigning the value obtained by counting the number of the invalid value lines of the pixel point to the pixel point. For example, the number of invalid value rows isThe +.>The value of each pixel is assigned to that pixel. Specific:
；
wherein, the liquid crystal display device comprises a liquid crystal display device,for stripe removed data, < >>For the original data +. >For line number, ->For column number->Is the number of invalid value rows.
And a3, performing resolution interpolation processing on the updated basic data to obtain the interpolated basic data. And adjusting the spatial resolution of the updated basic data, namely performing resolution interpolation processing on the basic data. In one embodiment, the ratio of the spatial resolution of the basic data to the spatial resolution of the scientific data of the new generation ocean water color and temperature scanner can be calculated, the ratio is used as a coefficient, and the coefficient is multiplied by the length and the width of the basic data so as to achieve the purpose of adjusting the spatial resolution of the basic data.
The step a3 is resolution interpolation processing. For example, as the spatial resolution of M band of the VIIRS SDR data is 750M, and the spatial resolution of scientific data of a new generation ocean water color water temperature scanner is 500M, spatial interpolation processing is required to be performed on the VIIRS SDR data, that is, the spatial resolution of M band of the VIIRS SDR data is 750M divided by the required spatial resolution of 500M to obtain a coefficient, and the coefficient is multiplied by the length and width of the VIIRS SDR data to obtain the length and width of the interpolated data, the length and width of the interpolated data are used for interpolation on original data, and the GDAL toolkit in python is used for interpolation on the data.
；
；
Wherein the method comprises the steps ofFor the length of the interpolated data, +.>For the width of the interpolated data, +.>For the length of the original data, +.>Is wide of the original data.
And a4, performing format conversion on the interpolated basic data to obtain basic data with a specified format, wherein the specified format can be a tiff format.
Wherein, the step a4 is format conversion processing. In one embodiment, the interpolated base data may be stored directly in tiff format by the GDAL tool in python.
Step a5, if a wave band matched with a preset wave band exists in the wave band to which each pixel point belongs in the basic data in the appointed format, determining the value of the wave band matched with the preset wave band as simulation scientific data; if the wave band matched with the preset wave band does not exist in the wave band to which each pixel point belongs in the basic data in the appointed format, the preset wave band is determined to be the wave band to be interpolated.
The preset wave band is determined according to the wave band design requirement of the new-generation marine satellite water color and temperature scanner, and the wave band design requirement comprises a plurality of preset wave bands. When the method is concretely implemented, according to the wave band design requirement of a new-generation marine satellite water color and temperature scanner, the wave band of the basic data VIIRS SDR is compared, and if the wave band of the VIIRS SDR corresponds to the wave band, the wave band data is used as scientific simulation data; for the band (i.e., the band to be interpolated) which does not correspond to the VIIRS SDR data band, interpolation processing is required. Illustratively, the total 7 compared bands to be interpolated are:
360nm、385nm、520nm、620nm、681nm、705nm、1640nm。
For the foregoing step S104, the embodiment of the present invention further provides an implementation manner of determining interpolation models corresponding to a plurality of feature types based on simulation model construction data of a region to be simulated, see the following steps b1 to b3:
and b1, performing format conversion processing on simulation model construction data of the region to be simulated to obtain simulation model construction data in a specified format. In one embodiment, in order to facilitate the subsequent processing and avoid re-reading data every time of processing, the data of the Sentinel-3L 1A distributed by the European space agency is subjected to format conversion processing, converted from nc format to tiff format and stored, specifically, the projection information and the data size of each wave band are read from the data of the Sentinel-3L 1A nc format, and the data is stored as tiff format by using the GDAL toolkit in python.
And b2, constructing data based on a simulation model in a specified format, and determining remote sensing reflectivity average values corresponding to a plurality of ground object types. In one embodiment, three types of features can be chosen from the preprocessed simulation model construction data: determining the geographic coordinates of the three types of ground object ranges in the geographic ranges of the cloud body, the water body and the land; based on the geographic coordinates of the three types of ground object ranges, in the preprocessed simulation model construction data, calculating the remote sensing reflectivity average value of the three types of ground object types.
In a specific implementation, reference may be made to the following steps b2-1 to b2-2:
and b2-1, determining geographic ranges corresponding to a plurality of ground object types in simulation model construction data in a specified format.
In order to obtain a more accurate result, the remote sensing reflectivity average value of various ground object types in the data needs to be constructed according to a simulation model to classify the data to be interpolated so as to more accurately interpolate according to different ground object types, thereby obtaining a more accurate result.
Based on the three types of ground objects are selected in the preprocessed simulation model construction data: and determining the geographic coordinates of the three types of ground object ranges in the geographic ranges of the cloud body, the water body and the land, and obtaining row and column numbers in the image according to the geographic coordinates.
And b2-2, for each ground object type, extracting the remote sensing reflectivity of the pixel point corresponding to the ground object type from simulation model construction data in a specified format according to the geographic range corresponding to the ground object type, and determining the average value of the remote sensing reflectivity of each pixel point as the average value of the remote sensing reflectivity of the ground object type.
In one embodiment, in the preprocessed simulation model construction data, remote sensing reflectivity corresponding to each pixel point of three types of ground object types can be obtained according to the row and column numbers of the three types of ground object ranges, and the remote sensing reflectivity of all the pixel points of each type of ground object type is added and divided by the total number of the pixel points to obtain the average value of the remote sensing reflectivity of the type of ground object type in each wave band. The calculation formula of the remote sensing reflectivity average value is as follows:
；
；
；
Wherein, the liquid crystal display device comprises a liquid crystal display device,、/>、/>the average value of the remote sensing reflectivities of the cloud body, the land and the water body in each wave band is +.>、/>、/>Remote sensing reflectivity values of each pixel point of cloud, land and water respectively, < ->、/>、/>The total number of pixels of the cloud, the land and the water body respectively.
And b3, for each ground object type, fitting an interpolation model corresponding to the ground object type based on the mapping relation between the remote sensing reflectivity average value corresponding to the ground object type and the wavelength. Wherein, the mapping relation can be a linear extrapolation relation.
In one embodiment, for three classes of features: and constructing a primary linear extrapolation model between a remote sensing reflectivity average value corresponding to each ground object type and a wavelength by the cloud, the land and the water body, and determining interpolation models corresponding to three ground object types. Wherein the interpolation model is as follows:
;
;
;
wherein the method comprises the steps of、/>、/>Interpolation models of cloud, land and water respectively,>for a linear extrapolation->For wavelength, < >>、/>、/>Respectively a cloud body,The average value of the remote sensing reflectivity of land and water in each wave band.
For the foregoing step S104, the embodiment of the present invention further provides an implementation manner of determining, based on each interpolation model, the interpolation coefficient set corresponding to each feature type, see the following steps c1 to c3:
Step c1, taking the wave band closest to each wave band to be interpolated in the basic data in the appointed format as the normalized base wave band corresponding to each wave band to be interpolated. In one embodiment, for each band to be interpolated, the band closest to the band in the simulation model construction data may be selected as the normalized radix band.
Continuing with the above example, assume that the total 7 to-be-interpolated bands are: 360nm, 385nm, 520nm, 620nm, 681nm, 705nm, 1640nm.
Because the VIIRS SDR data wave band and the wave band to be interpolated are not in one-to-one correspondence, the wave band closest to the VIIRS SDR data wave band is required to be selected as a normalized base wave band, namely the normalized base wave band corresponding to the wave band to be interpolated is: 412nm, 488nm, 640nm, 672nm, 1610nm.
Step c2, substituting each band to be interpolated into the interpolation model corresponding to the ground object type for the interpolation model corresponding to each ground object type to obtain a fitting value of each band to be interpolated; substituting the normalized base number wave bands corresponding to each wave band to be interpolated into the interpolation model corresponding to the ground object type to obtain the normalized base number of each wave band to be interpolated.
With continued reference to the above example, 412nm, 488nm, 640nm, 672nm, 1610nm are brought into the interpolation model corresponding to the three types of terrain、/>、/>Obtaining normalized base numbers of each wave band to be interpolated, and360nm, 385nm, 520nm, 620nm, 681nm, 705nm and 1640nm are brought into interpolation models corresponding to three types of ground object types>、/>、/>And obtaining fitting values of each wave band to be interpolated.
And c3, determining the quotient of the fitting value of each wave band to be interpolated and the normalized base number as an interpolation coefficient corresponding to each wave band to be interpolated, and constructing and obtaining an interpolation coefficient set corresponding to the ground object type based on the interpolation coefficient corresponding to each wave band to be interpolated.
In one embodiment, the fitting value of the band to be interpolated is divided by the normalized base, so as to obtain the interpolation coefficient of each band to be interpolated, where the interpolation coefficient is as shown in the following formula:
;
;
;
wherein the method comprises the steps of、/>、/>Interpolation coefficients of each wave band of cloud, land and water respectively, ++>、/>、/>Interpolation models of cloud, land and water respectively,>for wavelength, < >>To normalize the wavelengths of the radix band.
For the foregoing step S106, the embodiment of the present invention further provides an implementation manner of performing band interpolation on the basic data by using the interpolation coefficient set corresponding to each feature type to obtain simulation scientific data corresponding to the to-be-interpolated band, which is described in the following steps d1 to d2:
Step d1, for each pixel point in the basic data, determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point.
In one embodiment, the type of feature to which the pixel point belongs may be determined according to the following (1) to (3):
(1) If the remote sensing reflectivity corresponding to the pixel point is larger than the average value of the Yun Yaogan reflectivities, determining that the pixel point belongs to Yun Ti type;
(2) If the remote sensing reflectivity corresponding to the pixel point is smaller than the Yun Yaogan reflectivity average value and larger than the land remote sensing reflectivity average value, determining that the pixel point belongs to a land type;
(3) And if the remote sensing reflectivity corresponding to the pixel point is smaller than the land remote sensing reflectivity average value, determining that the pixel point belongs to the water body type.
In specific implementation, since the fundamental data VIIRS M2 band, the M7 band is easy to distinguish between the cloud and the land, the two band data are used for distinguishing. Specifically, traversing the basic data, and identifying the pixel (namely the pixel point) as a cloud body when the remote sensing reflectivity of the VIIRS M2 wave band is larger than the average value of the Yun Yaogan reflectivity; otherwise, judging whether the remote sensing reflectivity of the pixel VIIRS M7 wave band is larger than the average value of the remote sensing reflectivities of the land, if so, identifying the land, and otherwise, identifying the water body.
And d2, collecting interpolation coefficients corresponding to the ground object type to which the pixel point belongs, and taking the product of the interpolation coefficient corresponding to each band to be interpolated and the value of the band to which the pixel point belongs as simulation scientific data corresponding to the band to be interpolated.
In one embodiment, for the classified pixels, according to different types of ground objects, according to interpolation coefficients of different wave bands of various ground objects and values of corresponding wave bands of basic data, final simulation scientific data of each wave band is obtained through calculation, namely, the interpolation coefficients of different wave bands of various ground objects are multiplied by the values of corresponding wave bands of the basic data, wherein the following formula is shown:
；
；
；
wherein the method comprises the steps of、/>、/>Scientific simulation data of cloud, land and water respectively, < >>Values of the respective bands of the basic data VIIRS, < >>、/>、/>Interpolation coefficients of each wave band of cloud, land and water respectively, ++>Is the wavelength.
In summary, according to the scientific data simulation method for the new-generation marine satellite water color and water temperature scanner provided by the embodiment of the invention, the scientific data of the new-generation water color scanner is simulated based on the existing satellite data VIIRS SDR data and the Sentinel-3L 1A data, and the orbit heights are 814.5km and 830km respectively because the data adopt solar synchronous orbits; the new generation ocean water color and temperature scanner adopts a solar synchronous track, and the track height is 782km. It can be approximately considered that Sentinel-3, viirs has similar observation geometry as the new-generation ocean water temperature scanner, and under the same observation scene, the observation data of the new-generation ocean water temperature scanner should have similar performance as the satellite data. Therefore, the satellite data can be used for realizing scientific data simulation of a new generation of ocean water color and temperature scanners. The method avoids a large amount of calculation of forward algorithm by using the radiation transmission model in the prior art, obtains a simple and efficient effective data simulation method, and solves the technical problem that scientific data of a new-generation marine satellite water color and water temperature scanner are difficult to simulate simply and quickly in the prior art.
In order to facilitate understanding, the embodiment of the present invention further provides another new generation of scientific data simulation method for a marine satellite water color and water temperature scanner, referring to a flow chart of another new generation of scientific data simulation method for a marine satellite water color and water temperature scanner shown in fig. 2, the method mainly includes the following steps S202 to S210:
step S202, basic data of a region to be simulated and simulation model construction data are obtained, wherein the basic data are as follows: VIIRS SDR data, wherein simulation model construction data are Sentinel-3L 1A data;
step S204, constructing data based on a simulation model, and determining remote sensing reflectivity average values of various ground object types in each wave band;
step S206, determining interpolation models of various ground objects based on remote sensing reflectivity average values of various ground object types in each wave band;
step S208, determining interpolation coefficients of different wave bands of various ground objects based on interpolation models of various ground objects;
step S210, based on interpolation coefficients of different wave bands of various ground objects, wave band interpolation is carried out on the basic data, and final simulation scientific data are determined.
The embodiment of the invention achieves the purpose of simple and rapid simulation of the scientific data of the new-generation marine satellite water color and water temperature scanner, and further solves the technical problem that the scientific data of the new-generation marine satellite water color and water temperature scanner is difficult to simulate simply and rapidly in the prior art, thereby realizing the technical effect of simulating the scientific data of the new-generation marine satellite water color and water temperature scanner simply and rapidly.
For the new generation of scientific data simulation method for the marine satellite water color and water temperature scanner provided by the foregoing embodiment, the embodiment of the invention provides a new generation of scientific data simulation device for the marine satellite water color and water temperature scanner, see a structural schematic diagram of the new generation of scientific data simulation device for the marine satellite water color and water temperature scanner shown in fig. 3, where the device mainly includes the following parts:
the wave band determining module 302 is configured to obtain basic data of an area to be simulated, and determine a wave band to be interpolated based on a wave band to which each pixel point in the basic data belongs;
the coefficient determining module 304 is configured to determine interpolation models corresponding to a plurality of feature types based on simulation model construction data of the region to be simulated, and determine an interpolation coefficient set corresponding to each feature type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each band to be interpolated;
and the interpolation module 306 is configured to perform band interpolation on the basic data by using the interpolation coefficient set corresponding to each feature type, so as to obtain simulation scientific data corresponding to the band to be interpolated.
According to the scientific data simulation device for the new-generation marine satellite water color and water temperature scanner, after the wave band to be interpolated is determined based on the wave band to which each pixel point in basic data belongs, the simulation model is utilized to construct interpolation models corresponding to a plurality of ground object types, further interpolation coefficients corresponding to each ground object type and each wave band to be interpolated are determined, and the wave band interpolation is carried out on the basic data by utilizing the interpolation coefficients.
In one embodiment, band determination module 302 is further configured to:
traversing the basic data to obtain the invalid value row number of the basic data;
for each pixel point in the basic data, if the value of the pixel point is an invalid value, determining a target pixel point from the pixel points contained in the basic data according to the number of invalid value rows, and giving the value of the target pixel point to the pixel point to obtain updated basic data;
performing resolution interpolation processing on the updated basic data to obtain interpolated basic data;
performing format conversion on the interpolated basic data to obtain basic data in a specified format;
if a wave band matched with a preset wave band exists in the wave band to which each pixel point belongs in the basic data in the appointed format, determining the value of the wave band matched with the preset wave band as simulation scientific data; if the wave band matched with the preset wave band does not exist in the wave band to which each pixel point belongs in the basic data in the appointed format, the preset wave band is determined to be the wave band to be interpolated.
In one embodiment, the coefficient determination module 304 is further configured to:
performing format conversion processing on simulation model construction data of a region to be simulated to obtain simulation model construction data in a specified format;
Based on simulation model construction data in a specified format, determining remote sensing reflectivity average values corresponding to a plurality of ground object types;
and fitting an interpolation model corresponding to each ground object type based on the mapping relation between the remote sensing reflectivity average value corresponding to the ground object type and the wavelength.
In one embodiment, the coefficient determination module 304 is further configured to:
determining geographic ranges corresponding to a plurality of ground object types in simulation model construction data in a specified format;
for each ground object type, extracting the remote sensing reflectivity of the pixel point corresponding to the ground object type from simulation model construction data in a specified format according to the geographic range corresponding to the ground object type, and determining the average value of the remote sensing reflectivity of each pixel point as the average value of the remote sensing reflectivity of the ground object type.
In one embodiment, the coefficient determination module 304 is further configured to:
taking a wave band closest to each wave band to be interpolated in the basic data in the appointed format as a normalized base wave band corresponding to each wave band to be interpolated;
substituting each band to be interpolated into the interpolation model corresponding to the ground object type to obtain a fitting value of each band to be interpolated; substituting the normalized base number wave bands corresponding to each wave band to be interpolated into the interpolation model corresponding to the ground object type to obtain the normalized base number of each wave band to be interpolated;
And determining the quotient of the fitting value of each band to be interpolated and the normalization base as an interpolation coefficient corresponding to each band to be interpolated, so as to construct and obtain an interpolation coefficient set corresponding to the ground object type based on the interpolation coefficient corresponding to each band to be interpolated.
In one embodiment, the interpolation module 306 is further configured to:
for each pixel point in the basic data, determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point;
and collecting interpolation coefficients corresponding to the ground object type to which the pixel point belongs, and taking the product of the interpolation coefficient corresponding to each band to be interpolated and the value of the band to which the pixel point belongs as simulation scientific data corresponding to the band to be interpolated.
In one embodiment, the ground object type includes one or more of a cloud type, a land type, and a water body type; the interpolation module 306 is also configured to:
if the remote sensing reflectivity corresponding to the pixel point is larger than the average value of the Yun Yaogan reflectivities, determining that the pixel point belongs to Yun Ti type;
if the remote sensing reflectivity corresponding to the pixel point is smaller than the Yun Yaogan reflectivity average value and larger than the land remote sensing reflectivity average value, determining that the pixel point belongs to a land type;
And if the remote sensing reflectivity corresponding to the pixel point is smaller than the land remote sensing reflectivity average value, determining that the pixel point belongs to the water body type.
In one embodiment, the base data is data acquired by a visible infrared imaging radiation instrument (Suomi NPP VIIRS), and the simulation model construction data is data acquired by a Sentinel No. 3 satellite sea Liu Sedu instrument (Sentinel-3 OLCI).
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, the processor 40, the communication interface 43 and the memory 41 being connected by the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The memory 41 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and the at least one other network element is achieved via at least one communication interface 43 (which may be wired or wireless), which may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 42 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 41 is configured to store a program, and the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40 or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 40. The processor 40 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks 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 embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 41 and the processor 40 reads the information in the memory 41 and in combination with its hardware performs the steps of the method described above.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, 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 this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A new generation of scientific data simulation method for a marine satellite water color and temperature scanner is characterized by comprising the following steps:
basic data of an area to be simulated is obtained, and a wave band to be interpolated is determined based on a wave band to which each pixel point in the basic data belongs;
Determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each wave band to be interpolated;
performing band interpolation on the basic data by using the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated;
determining a wave band to be interpolated based on the wave band to which each pixel point in the basic data belongs, including:
traversing the basic data to obtain the invalid value row number of the basic data;
for each pixel point in the basic data, if the value of the pixel point is an invalid value, determining a target pixel point from the pixel points contained in the basic data according to the number of the invalid value rows, and assigning the value of the target pixel point to the pixel point to obtain updated basic data;
performing resolution interpolation processing on the updated basic data to obtain the interpolated basic data;
performing format conversion on the interpolated basic data to obtain the basic data in a specified format;
If a wave band matched with a preset wave band exists in the wave band to which each pixel point belongs in the basic data in a specified format, determining the value of the wave band matched with the preset wave band as simulation scientific data; if the wave band matched with the preset wave band does not exist in the wave band of each pixel point in the basic data in the appointed format, determining the preset wave band as the wave band to be interpolated.
2. The new generation marine satellite water color and temperature scanner scientific data simulation method according to claim 1, wherein determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the region to be simulated comprises:
performing format conversion processing on the simulation model construction data of the region to be simulated to obtain simulation model construction data in a specified format;
based on the simulation model construction data in the specified format, determining remote sensing reflectivity average values corresponding to a plurality of ground object types;
and fitting an interpolation model corresponding to each ground object type based on the mapping relation between the remote sensing reflectivity average value corresponding to the ground object type and the wavelength.
3. The new generation marine satellite water color and temperature scanner scientific data simulation method according to claim 2, wherein determining remote sensing reflectivity averages corresponding to a plurality of ground object types based on the simulation model construction data of the specified format comprises:
Determining geographic ranges corresponding to a plurality of ground object types in the simulation model construction data in the specified format;
and for each ground object type, extracting the remote sensing reflectivity of a pixel point corresponding to the ground object type from the simulation model construction data in the specified format according to the geographic range corresponding to the ground object type, and determining the average value of the remote sensing reflectivity of each pixel point as the average value of the remote sensing reflectivity of the ground object type.
4. The new generation marine satellite water color and temperature scanner scientific data simulation method according to claim 1, wherein determining the interpolation coefficient set corresponding to each ground object type based on each interpolation model comprises:
taking a band closest to each band to be interpolated in the basic data in a specified format as a normalized base band corresponding to each band to be interpolated;
substituting each wave band to be interpolated into the interpolation model corresponding to the ground object type to obtain a fitting value of each wave band to be interpolated; substituting the normalized base number wave bands corresponding to each wave band to be interpolated into the interpolation model corresponding to the ground feature type to obtain the normalized base number of each wave band to be interpolated;
And determining the quotient of the fitting value and the normalized base number of each band to be interpolated as an interpolation coefficient corresponding to each band to be interpolated, so as to construct and obtain an interpolation coefficient set corresponding to the ground object type based on the interpolation coefficient corresponding to each band to be interpolated.
5. The new generation marine satellite water color and water temperature scanner scientific data simulation method according to claim 1, wherein the band interpolation is performed on the basic data by using the interpolation coefficient set corresponding to each ground object type to obtain the simulation scientific data corresponding to the band to be interpolated, including:
for each pixel point in the basic data, determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point;
and collecting the interpolation coefficient set corresponding to the ground object type to which the pixel point belongs, wherein the product of the interpolation coefficient corresponding to each band to be interpolated and the value of the band to which the pixel point belongs is used as simulation scientific data corresponding to the band to be interpolated.
6. The new generation marine satellite water color and temperature scanner scientific data simulation method according to claim 5, wherein the ground object type comprises one or more of cloud type, land type and water body type; determining the type of the ground object to which the pixel point belongs according to the remote sensing reflectivity corresponding to the pixel point comprises the following steps:
If the remote sensing reflectivity corresponding to the pixel point is larger than the Yun Yaogan reflectivity average value, determining that the pixel point belongs to the cloud body type;
if the remote sensing reflectivity corresponding to the pixel point is smaller than the Yun Yaogan reflectivity average value and larger than the land remote sensing reflectivity average value, determining that the pixel point belongs to the land type;
and if the remote sensing reflectivity corresponding to the pixel point is smaller than the land remote sensing reflectivity average value, determining that the pixel point belongs to the water body type.
7. The new generation marine satellite water color and temperature scanner scientific data simulation method according to any one of claims 1-6, wherein the basic data is data collected by a visible light infrared imaging radiometer (Suomi NPP VIIRS), and the simulation model construction data is data collected by a Sentinel No. 3 satellite sea Liu Sedu meter (Sentinel-3 OLCI).
8. A new generation ocean satellite water color temperature scanner scientific data simulation device is characterized in that the device comprises:
the wave band determining module is used for acquiring basic data of the area to be simulated and determining a wave band to be interpolated based on the wave band of each pixel point in the basic data;
the coefficient determining module is used for determining interpolation models corresponding to a plurality of ground object types based on simulation model construction data of the area to be simulated, and determining interpolation coefficient sets corresponding to each ground object type based on each interpolation model; the interpolation coefficient set comprises interpolation coefficients corresponding to each wave band to be interpolated;
The interpolation module is used for carrying out band interpolation on the basic data by utilizing the interpolation coefficient set corresponding to each ground object type to obtain simulation scientific data corresponding to the band to be interpolated;
the band determination module is further configured to:
traversing the basic data to obtain the invalid value row number of the basic data;
for each pixel point in the basic data, if the value of the pixel point is an invalid value, determining a target pixel point from the pixel points contained in the basic data according to the number of the invalid value rows, and assigning the value of the target pixel point to the pixel point to obtain updated basic data;
performing resolution interpolation processing on the updated basic data to obtain the interpolated basic data;
performing format conversion on the interpolated basic data to obtain the basic data in a specified format;
if a wave band matched with a preset wave band exists in the wave band to which each pixel point belongs in the basic data in a specified format, determining the value of the wave band matched with the preset wave band as simulation scientific data; if the wave band matched with the preset wave band does not exist in the wave band of each pixel point in the basic data in the appointed format, determining the preset wave band as the wave band to be interpolated.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1 to 7.
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