CN113469896A - Method for improving geometric correction precision of geosynchronous orbit satellite earth observation image - Google Patents
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Abstract
The invention relates to a method for improving the geometric correction precision of a geosynchronous orbit satellite earth observation image, which comprises the following steps: calculating the longitude and latitude of the pixel-by-pixel central position in advance according to the orbit data of the geosynchronous orbit satellite, and storing the longitude and latitude in a grid data file; high-resolution digital elevation data is used as geometric fine correction datum data, and high-density landmark points are generated along a coastline in a geosynchronous orbit satellite scanning area; reading original remote sensing image data of a geosynchronous orbit satellite through an open source scientific calculation library, and outputting the data in a geospatial data GeoTIFF format; updating the acquired raster data file information and the related geographical projection information into a GeoTIFF file storing a satellite image in a geospatial metadata mode according to an open source scientific computation library; and the geometric correction of the remote sensing image of the geosynchronous orbit satellite is completed by taking the geospatial data as basic information of the re-projection of the remote sensing image.
Description
Technical Field
The invention relates to the field of satellite earth observation image processing or data analysis, in particular to a method for improving geometric correction precision of a satellite earth observation image of a geosynchronous orbit.
Background
Geosynchronous orbit satellites (Geosynchronous satellites), also known as geostationary satellites, are artificial earth satellites having a period of operation that is the same as the period of rotation of the earth. Unlike a sun-synchronous orbiting satellite, which is commonly used for earth observation, a geosynchronous orbiting satellite travels over the same place on earth at the same time each day. For a ground observer, geosynchronous orbit satellites are relatively stationary in position, always in the same zenith and azimuth directions.
The images acquired by satellite earth observation are called remote sensing images. The remote sensing images acquired by both the sun-synchronous orbit satellite and the geosynchronous orbit satellite have certain degree of geometric deformation, including systematic geometric deformation and non-systematic geometric deformation. Systematic geometric deformation comes from the satellite sensors themselves, and can be corrected by using a mathematical model established by a specific sensor; the non-systematic geometric deformation has large uncertainty and complicated source. The change of the instantaneous attitude of the satellite in flight can cause the geometric deformation of the remote sensing image, and the irregular displacement of the pixels of the remote sensing image can be formed by the curvature of the earth, the air refraction, the terrain change and the like to form the geometric deformation. The height of the geosynchronous orbit satellite is usually 35,786 km, and small changes of any of the above factors can cause large irregular geometric deformation of the remote sensing image, so that the pixel spatial position of the remote sensing image acquired by the geosynchronous orbit satellite is deviated by hundreds of meters or even kilometers. Geometric correction is a process of establishing a complex mathematical model and eliminating systematic and non-systematic geometric deformation of the remote sensing image through a specific algorithm.
Commercial remote sensing image processing software generally provides three common geometric correction modules, such as Affine transform (Affine transform), Polynomial (Polynomial), local Triangulation network (Delaunay Triangulation), and the like, so that a user can complete geometric correction of a satellite image in a graphical interface in a Ground Control Point (GCP) mode.
(1) Affine transformation is two-dimensional coordinate linear transformation in a Cartesian coordinate system, the relative position between remote sensing image pixels is kept unchanged in the geometric transformation process, and the flatness and the parallelism of the whole image are kept. The homogeneous coordinate matrix representation form is as follows:
(2) the polynomial transformation is to select a quadratic or higher polynomial to approximately describe the coordinate relationship of corresponding pixels before and after the remote sensing image is corrected, utilize the specific map projection coordinate of the control point and the coordinate in the reference coordinate system, solve the coefficient of the polynomial by adopting the principle of least square method, and then geometrically correct the remote sensing image by the polynomial of the determined coefficient. A commonly used polynomial model of the degree of multiparity can be expressed as:
(3) the local triangulation geometric correction method is to divide the remote sensing image space into a plurality of blocks by adopting a local triangulation, and different correction functions are used for each block. Firstly, determining the size of an image block according to the landform complexity of a remote sensing image and the spatial distribution of GCP points; then selecting partial GCP points to form local triangles, wherein the vertex of each triangle is a control point; after establishing a functional relation between coordinates before and after geometric correction of each triangle vertex, locally correcting image pixels of an area contained in the triangle; and finally, performing geometric correction on all local triangular areas in the image.
The three methods have the advantages that the controllability is strong, and as long as the GCP geometric accuracy provided by a user is high enough and the number of control points is enough, the geometric correction of the remote sensing image can be completed by pixel-by-pixel resampling in a translation or rubber stretching mode; the method has the disadvantages that the process needs a large amount of user interactive operation, is time-consuming and high in labor cost, is easy to introduce human errors, and is difficult to be used for processing mass satellite image data.
The method is based on the open source scientific computation library h5py, GDAL and Numpy, can complete automatic geometric correction of geosynchronous orbit satellite remote sensing data under the condition of no ground control point and manual participation of a user, and supports parallel operation in a high-performance computing environment. Firstly, pre-calculating the longitude (lon) and the latitude (lat) of pixel-by-pixel central positions by using a mathematical model, and completing geometric rough correction of a geosynchronous orbit satellite remote sensing image by using satellite ephemeris data; and then, using high-precision digital elevation Data (DEM) or rasterized large-scale coastline space data as a reference, and adopting a phase correlation correction algorithm to carry out dynamic geometric fine correction on the geosynchronous orbit satellite remote sensing data. In the whole process, control point GCP data does not need to be provided, and man-machine interaction is also not needed, so that the efficiency of geometric correction of the satellite remote sensing image is greatly improved; meanwhile, due to the adoption of frequency domain space phase characteristic matching, a better signal-to-noise ratio can be obtained and the maximum peak value can be output.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a method for improving the geometric correction precision of geosynchronous orbit satellite earth observation images.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for improving the geometric correction precision of geosynchronous orbit satellite earth observation images comprises the following steps:
calculating the longitude and latitude of the pixel-by-pixel central position in advance according to the orbit data of the geosynchronous orbit satellite, and storing the longitude and latitude in a grid data file;
high-resolution digital elevation data is used as geometric fine correction datum data, and high-density landmark points are generated along a coastline in a geosynchronous orbit satellite scanning area;
reading original remote sensing image data of a geosynchronous orbit satellite through an open source scientific calculation library, and outputting the data in a geospatial data GeoTIFF format;
updating the acquired raster data file information and the related geographical projection information into a GeoTIFF file storing a satellite image in a geospatial metadata mode according to an open source scientific computation library;
and the geometric correction of the remote sensing image of the geosynchronous orbit satellite is completed by taking the geospatial data as basic information of the re-projection of the remote sensing image.
In a preferred embodiment of the present invention, the method further comprises:
converting the generated remote sensing image data of the geosynchronous orbit satellite and the reference data from a space domain to a frequency domain space through Fourier transform, and calculating the cross power spectrum of the remote sensing image data of the geosynchronous orbit satellite in the Fourier frequency domain space;
obtaining the displacement of the geosynchronous orbit landing remote sensing image and the reference data in the (x, y) direction through inverse Fourier transform;
after the displacement calculation of each landmark point in the (x, y) direction is completed, the average displacement of the remote sensing image is further calculated,
and converting the number of the displacement pixel floating point types into image coordinate values to finish the geometric secondary fine correction of the geosynchronous orbit satellite remote sensing image.
In a preferred embodiment of the present invention, the method for reading raw remote sensing image data of geosynchronous orbit satellites through an open source scientific computation library and outputting the data in a geospatial data GeoTIFF format specifically comprises:
reading data through an open source scientific calculation library, and converting the wave band data of the geosynchronous orbit satellite remote sensing image into a Numpy array of the open source scientific calculation library;
converting the original numerical value of the specific wave band into the data of the reflectivity of the atmosphere top and the brightness temperature of the atmosphere top by adopting the advanced index function of Numpy;
and storing the converted wave band data of the reflectivity of the atmosphere roof and the brightness temperature of the atmosphere roof into a GeoTiff format which is universal for a geographic information system and remote sensing by using an open source scientific calculation library.
In a preferred embodiment of the present invention, the calculating the longitude and latitude of the pixel-by-pixel center position of the remote sensing image according to the orbit data of the geosynchronous orbit satellite specifically includes:
s101: the row and column numbers (col, row) of the remote sensing image are converted into (x, y).
The calculation formula is as follows:
COFF, CFAC, LOFF and LFAC are respectively a column offset, a column scale factor, a row offset and a row scale factor of the geosynchronous orbit satellite remote sensing image pixel by pixel;
calculating an intermediate variable S from (x, y) calculated in S101d，Sn，S1，S2，S3，Sxy；
And calculating the longitude and latitude of the pixel-by-pixel center point.
In a preferred embodiment of the invention, the intermediate variable S is calculated based on the calculated (x, y)d，Sn，S1，S2，S3，Sxy；
The calculation formula is as follows:
S1＝h-Sn×cos(x)×cos(y)
S2＝Sn×sin(x)×cos(y)
S3＝-Sn×sin(y)
in the formula, ea is the earth's major semi-axis (6378.137km), eb is the earth's minor semi-axis (6356.7523km), and h is the distance from the earth's center to the center of mass of the satellite (42164 km).
In a preferred embodiment of the present invention, the longitude and latitude (lon, lat) of the pixel-by-pixel center point is calculated as follows:
in the formula, λDThe longitude of the satellite subsatellite point.
In a preferred embodiment of the invention, the remote sensing image f is1(x, y) and reference spatial data f2(x, y) obtaining amplitude information f after Fourier transform1(ωx，ωy) And phase spectrum information f2(ωx，ωy)；
Wherein (x, y) is the geographic coordinate value of the remote sensing image and the reference space data, (omega)x，ωy) Frequency phase information converted into the frequency domain.
When the remote sensing image and the reference space data have the same amplitude value, that is
f2(x，y)＝f1(x-x0，y-x0)，
The two have a phase difference in the frequency domain, i.e.
And obtaining a phase difference by calculating a cross power spectrum of the remote sensing image and the reference space data:
in the formula, QR(ωx,ωyAnd theta) is a cross-power spectral density function when the phase difference between the remote sensing image of the geosynchronous orbit satellite and the reference data is theta, which is called cross-power density for short.
And obtaining the displacement of the geosynchronous orbit landing remote sensing image and the reference data in the (x, y) direction through inverse Fourier transform. After the displacement calculation of each landmark point in the (x, y) direction is completed, the average displacement of the remote sensing image is further calculated,
and converting the number of the displacement pixel floating point types into image coordinate values to finish the geometric fine correction of the geosynchronous orbit satellite remote sensing image.
In a preferred embodiment of the present invention, the longitude and latitude of the pixel-by-pixel central position are pre-calculated according to the orbit data of the geosynchronous orbit satellite, and a longitude and latitude grid is prepared;
carrying out pixel-by-pixel spatial positioning on the geosynchronous orbit satellite remote sensing image;
cutting the data set according to a preset space range, and removing a deformed part of a remote sensing image of the geosynchronous orbit satellite;
the correct map projection information is updated in the metadata of the target data set.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) the method for improving the geometric correction precision of the geosynchronous orbit satellite earth observation image does not need to provide ground control point GCP information, and does not need a user to manually participate in the input and output of information in the data processing process. Because the ephemeris data of the geosynchronous orbit satellite is directly combined with high-precision digital elevation Data (DEM), the geometric correction precision of the satellite remote sensing data is ensured.
(2) The auxiliary data set of the invention only needs one-time calculation and can be repeatedly used, thereby greatly reducing the requirement on calculation resources in the process of geometric correction of the geosynchronous orbit satellite, and leading users without large-scale computing equipment to carry out geometric correction and preprocessing tasks of massive satellite remote sensing images through a small-scale server.
(3) The method provided by the invention can be completed by using the open source scientific calculation library, thereby avoiding the high software licensing cost required by using commercial remote sensing software. Another advantage of using open source scientific computation libraries is that the algorithms and models involved in the data processing process can be updated in time, ensuring that the geosynchronous orbit satellite remote sensing data processing is optimized at any time.
(4) The method provided by the invention supports the adoption of a parallel computing mode to improve the data processing efficiency of the geosynchronous orbit satellite in a high performance computing environment (HPC).
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic diagram illustrating a geometric fine correction process of a geosynchronous orbit satellite remote sensing image according to the present invention;
FIG. 2 shows a projection view of a remote sensing image of a geosynchronous orbit satellite;
FIG. 3 illustrates a computationally generated latticed data diagram;
FIG. 4 shows a computationally generated graph of longitude grid data;
FIG. 5 shows an original remote sensing image of a geosynchronous orbit satellite;
FIG. 6 shows a schematic diagram of the amplitude component after Fourier transformation of a satellite remote sensing image;
FIG. 7 is a schematic diagram showing a phase component after Fourier transformation of a satellite remote sensing image;
FIG. 8 shows the registration result of the satellite remote sensing image and the vector data after geometric correction;
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 is a schematic view showing a process for improving geometric correction of geosynchronous orbit satellite earth observation images according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides a method for improving the accuracy of geometric correction of geosynchronous orbit satellite earth observation images, including:
s1, calculating longitude (lon) and latitude (lat) of pixel-by-pixel central position in advance according to orbit data of the geosynchronous orbit satellite, and storing the longitude (lon) and the latitude (lat) in a grid data file for later use;
s2, using the high-resolution digital elevation data as geometric fine correction datum data, and generating high-density (125 x 125 pixels) landmark points along a coastline for later use in a geosynchronous orbit satellite scanning area;
and S3, reading and outputting original remote sensing image data of the geosynchronous orbit satellite into a geospatial data GeoTIFF format by using an open source scientific computation library h5py and GDAL.
S4, updating the raster data file information acquired in the step S1 and the related geographical projection information into a GeoTIFF file storing satellite images in a geospatial Metadata (Metadata) mode by using an open source science computation library GDAL;
s5, using the API of the open source science computation library GDAL and the metadata of the GeoTIFF file as the basic information of the remote sensing image reprojection to complete the geometric rough correction of the geosynchronous orbit satellite remote sensing image;
and S6, converting the remote sensing image data of the geosynchronous orbit satellite and the reference data generated in the steps S2 and S5 into a frequency domain space from a space domain by using Fourier transform, and calculating the cross-power spectrum of the remote sensing image data and the reference data in the Fourier frequency domain space. And obtaining the displacement of the geosynchronous orbit landing remote sensing image and the reference data in the (x, y) direction through inverse Fourier transform. After the displacement calculation of each landmark point (125 x 125 pixels) in the (x, y) direction is completed, further calculating the average displacement of the remote sensing image, and finally converting the floating point type number of the displacement pixels into image coordinate values to complete the geometric fine correction of the geosynchronous orbit satellite remote sensing image;
a geosynchronous orbit satellite projection scheme, according to an embodiment of the invention, is shown in figure 2.
S101, S102 and S103, the geosynchronous orbit satellite projection belongs to a polar declined projection, which defines a sphere with the earth, a focal plane as an equatorial plane, and a view point as a north pole. The method is characterized in that the process of solving a binary nonlinear trigonometric equation is carried out by reversely calculating corresponding longitude and latitude values (lat, lon) of specific pixels of a known geosynchronous orbit satellite remote sensing image, and a numerical solution is required to be used for searching the solution. Firstly, inverse translation and reduction transformation of coordinate values (x, y) of an original image are carried out, so that specific pixels on the image are normalized to a projection plane. Therefore, COFF, CFAC, LOFF and LFAC are needed in S101, which are column offset, column scale factor, row offset and row scale factor of geosynchronous orbit satellite remote sensing images pixel by pixel, respectively. This information may be read from the satellite ephemeris.
As shown in fig. 3, the present invention discloses a computationally generated latitude grid data map, and as shown in fig. 4, the present invention discloses a computationally generated longitude grid data map.
It should be noted that, in general, the geosynchronous orbit satellite data distribution mechanism provides a calculated longitude and latitude grid, i.e., a stationary orbit nominal projection defined by CGMS LRIT/HRIT (WGS84 reference ellipsoid) specification, for a user to query image pixels and corresponding longitude and latitude. But the remote sensing image processing process needs to occupy a large amount of CPU computing resources and memory, and is not suitable for processing geosynchronous orbit satellite data under the condition of limited computing resources by an individual user. The method has the advantages that the auxiliary data can be repeatedly used after being prepared once, and does not need to occupy computing resources and memories frequently in the process of geometric correction and only exists in a numerical file format.
S301, reading data by using an API of an open source scientific computation library hd5py, and directly converting the wave band data of the remote sensing image of the geosynchronous orbit satellite into a Numpy array of the open source scientific computation library. In the process, the original numerical value of a specific wave band is converted into atmospheric top reflectivity (solar reflection wave band without unit scalar) and atmospheric top brightness temperature data (short wave infrared and thermal infrared wave bands, unit: Kelvin degree) with physical significance by adopting the advanced indexing function of Numpy.
And S302, storing the converted wave band data with specific physical significance (including the atmospheric top reflectivity and the brightness temperature) into a GeoTiff format commonly used by Geographic Information System (GIS) and Remote Sensing (RS) software by using the API of the open source scientific computation library GDAL. The invention directly uses the function of osgeo. GDAL _ array, avoids the complicated multi-step output method of the traditional GDAL API, and improves the data processing efficiency. An API is a calling interface that an operating system leaves for an application program, which causes the operating system to execute commands of the application program by calling the API of the operating system.
S401, using an open source science computation library GDAL, using the geographical projection and the related spatial information of the original remote sensing image data of the geosynchronous orbit satellite generated in the step S1 to update the step S302, and generating metadata of a GTiff file.
It should be noted that, here, only the longitude and latitude information of the remote sensing image data pixel by pixel is one-to-one corresponding to the data layer stored in the GeoTiff, and no actual calculation is performed. The method employed in S401 by the present invention is similar to the Swath data without geographic coordinates of polar orbiting satellite MODIS processed by the national space administration (NASA). The difference is that the Swath data processing algorithm developed by NASA is not general, but the open source science computing library based method used by the present invention can be used to process any geosynchronous orbit satellite data (including himwari in japan, GEOS in the united states, Meteosat in the european space, and high score four in china). Meanwhile, the invention also provides a new way for developing a universal polar orbit satellite to process the remote sensing data at the Swath level. By using the method, the method can be applied to carry out high-precision geometric correction as long as polar orbit satellite original data provided by a satellite data distribution mechanism and Swath geographic coordinate data corresponding to the polar orbit satellite original data are obtained.
As shown in FIG. 5, the invention discloses an original remote sensing image map of a geosynchronous orbit satellite.
S501, converting the GTiff file with the updated metadata into image data with geospatial significance by using an open source scientific computation library GDAL.
It should be noted that this step is a key link requiring a large amount of computing resources in the geometric correction of the geosynchronous orbit satellite remote sensing data. The invention adopts the open source scientific computation library GDAL to support concurrent data processing and parallel processing in a high-performance computing environment (cluster computing or super computing). The traditional commercial remote sensing software is often strictly limited in the number of software licenses in the link, and the data processing capacity cannot be flexibly controlled according to the change of computing resources. In the processing process, firstly, a longitude and latitude grid prepared in S101, S102 and S103 is used for carrying out pixel-by-pixel spatial positioning on the geosynchronous orbit satellite remote sensing image; meanwhile, cutting the data set according to a preset space range, and removing a part with larger deformation of a remote sensing image of the geosynchronous orbit satellite (namely the satellite apparent zenith angle is more than 60 degrees); finally, the correct map projection information is updated in the metadata of the target data set.
According to an embodiment of the invention, the spatial reference marking system used is defined by the standards of the European Petroleum mapping Group (EPSG), which is a set of standards developed for mapping, surveying and geodetic data storage, namely EPSG: 4326. the user may also choose the way in which the georeferenced coordinate system is defined using WKT, i.e., Well-Known Text reporting of Spatial Reference Systems, i.e., OpenGIS. The user may also choose to use the proj.4 string for metadata updates because proj.4 is the best known map projection library of open source GIS, and proj ections of software such as GRASS GIS, MapServer, PostGIS, Thuban, OGDI, Mapnik, TopoCad, GDAL/OGR, and the like, all use proj.4 directly or indirectly.
The corresponding proj.4 character strings in the embodiment of the present invention are:
“+proj＝longlat+datum＝WGS84+no_defs”
s601. in the embodiment of the invention, the remote sensing image f1(x, y) and reference spatial data f2(x, y) obtaining amplitude information f after Fourier transform1(ωx,ωy) Andphase spectrum information f2(ωx,ωy) See fig. 6 and 7.
The fourier transform is to map information of the remote sensing image in the spatial domain to the frequency domain space to obtain a magnitude spectrum and a phase spectrum of the remote sensing image, wherein the magnitude spectrum represents image gray information, and the phase spectrum is image position information, such as a contour. The embodiment of the invention mainly uses the phase spectrum information, and can calculate the offset of the image by comparing the phase spectrum of the geosynchronous orbit image with the phase spectrum of corresponding reference Data (DEM).
It should be noted that the frequency domain map obtained by fourier transform is actually a distribution map of image gradients, and there is no one-to-one correspondence relationship with each pixel of the remote sensing image, and the magnitude of the difference between any point and the neighborhood in fig. 7 is the gradient. Usually, the center of the spectrogram is the average brightness of the original image, the frequency is 0, and from the center of the image, the frequency increases, and the brightness is high, which indicates that the frequency characteristic is obvious. Meanwhile, in the frequency domain, the obvious frequency change direction of the image center is vertical to the distribution direction in the original remote sensing image.
S602, when the remote sensing image and the reference space data have the same amplitude value, namely
f2(x,y)＝f1(x-x0,y-x0)，
The two have phase difference in frequency domain, i.e.
S603, calculating a cross power spectrum of the remote sensing image and the reference space data to obtain a phase difference:
and S604, obtaining the displacement of the geosynchronous orbit landing remote sensing image and the reference data in the (x, y) direction through inverse Fourier transform. After the displacement calculation of each landmark point (125 x 125 pixels) in the (x, y) direction is completed, the average displacement of the remote sensing image is further calculated, and finally the floating point type number of the displacement pixels is converted into image coordinate values, so that the geometric fine correction of the geosynchronous orbit satellite remote sensing image is completed, as shown in fig. 8.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, 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.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within 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 (8)
1. A method for improving the geometric correction precision of a geosynchronous orbit satellite earth observation image is characterized by comprising the following steps:
calculating the longitude and latitude of the pixel-by-pixel central position in advance according to the orbit data of the geosynchronous orbit satellite, and storing the longitude and latitude in a grid data file;
high-resolution digital elevation data is used as geometric fine correction datum data, and high-density landmark points are generated along a coastline in a geosynchronous orbit satellite scanning area;
reading original remote sensing image data of a geosynchronous orbit satellite through an open source scientific calculation library, and outputting the data in a geospatial data GeoTIFF format;
updating the acquired raster data file information and the related geographical projection information into a GeoTIFF file storing a satellite image in a geospatial metadata mode according to an open source scientific computation library;
and the geometric correction of the remote sensing image of the geosynchronous orbit satellite is completed by taking the geospatial data as basic information of the re-projection of the remote sensing image.
2. The method for improving the accuracy of geometric correction of geosynchronous orbit satellite earth observation images according to claim 1, further comprising:
converting the generated remote sensing image data of the geosynchronous orbit satellite and the reference data from a space domain to a frequency domain space through Fourier transform, and calculating the cross power spectrum of the remote sensing image data of the geosynchronous orbit satellite in the Fourier frequency domain space;
obtaining the displacement of the geosynchronous orbit clinical remote sensing image and the reference data in the (x, y) direction (rows and columns) through inverse Fourier transform;
and after the displacement calculation of each landmark point in the (x, y) direction is finished, further calculating the average displacement of the remote sensing image, converting the floating point type number of the displacement pixels into image coordinate values, and finishing the geometric secondary fine correction of the geosynchronous orbit satellite remote sensing image.
3. The method for improving the geometric correction precision of the geosynchronous orbit satellite earth observation image according to claim 1, wherein the method comprises the following steps of reading original remote sensing image data of the geosynchronous orbit satellite through an open source scientific computing base and outputting the data in a geospatial data GeoTIFF format:
reading data through an open source scientific calculation library, and converting the wave band data of the geosynchronous orbit satellite remote sensing image into a Numpy array of the open source scientific calculation library;
converting the original numerical value of the specific wave band into the data of the reflectivity of the atmosphere top and the brightness temperature of the atmosphere top by adopting the advanced index function of Numpy;
and storing the converted wave band data of the reflectivity of the atmosphere roof and the brightness temperature of the atmosphere roof into a GeoTiff format which is universal for a geographic information system and remote sensing by using an open source scientific calculation library.
4. The method for improving the geometric correction precision of the geosynchronous orbit satellite earth observation image according to claim 1, wherein the calculating the longitude and latitude of the pixel-by-pixel center position of the remote sensing image according to the orbit data of the geosynchronous orbit satellite specifically comprises:
s101: the row and column numbers (col, row) of the remote sensing image are converted into (x, y).
The calculation formula is as follows:
COFF, CFAC, LOFF and LFAC are respectively a column offset, a column scale factor, a row offset and a row scale factor of the geosynchronous orbit satellite remote sensing image pixel by pixel;
calculating an intermediate variable S from (x, y) calculated in S101d，Sn，S1，S2，S3，Sxy；
And calculating the longitude and latitude of the pixel-by-pixel center point.
5. The method of claim 4, wherein the intermediate variable S is calculated according to the calculated (x, y)d，Sn，S1，S2，S3，Sxy；
The calculation formula is as follows:
S1＝h-Sn×cos(x)×cos(y)
S2＝Sn×sin(x)×cos(y)
S3＝-Sn×sin(y)
in the formula, ea is the earth's major semi-axis (6378.137km), eb is the earth's minor semi-axis (6356.7523km), and h is the distance from the earth's center to the center of mass of the satellite (42164 km).
6. The method of claim 5, wherein the longitude and latitude (lon, lat) of the pixel-by-pixel center point are calculated according to the following formula:
in the formula, λDThe longitude of the satellite subsatellite point.
7. The method for improving the accuracy of geometric correction of geosynchronous orbit satellite earth observation images according to claim 5, wherein the remote sensing image f is1(x, y) and reference spatial data f2(x, y) obtaining amplitude information f after Fourier transform1(ωx,ωy) And phase spectrum information f2(ωx,ωy)；
Wherein (x, y) is the geographic coordinate value of the remote sensing image and the reference space data, (omega)x,ωy) Frequency phase information converted into the frequency domain.
The reference space data is usually directly high-precision digital elevation Data (DEM) or rasterized by using large-scale coastline data as a space reference.
When the remote sensing image and the reference space data have the same amplitude value, that is
f2(x,y)＝f1(x-x0,y-x0)，
The two have a phase difference in the frequency domain, i.e.
And obtaining a phase difference by calculating a cross power spectrum of the remote sensing image and the reference space data:
in the formula, QR(ωx,ωyAnd theta) is a cross-power spectral density function when the phase difference between the remote sensing image of the geosynchronous orbit satellite and the reference data is theta, which is called cross-power density for short.
And obtaining the displacement of the remote sensing image of the geosynchronous orbit satellite and the reference data in the (x, y) direction through inverse Fourier transform. After the displacement calculation of each landmark point in the (x, y) direction is completed, the average displacement of the remote sensing image is further calculated,
and converting the number of the displacement pixel floating point types into image coordinate values to finish the geometric fine correction of the geosynchronous orbit satellite remote sensing image.
8. The method for improving the geometric correction precision of the geosynchronous orbit satellite earth observation image according to claim 1, wherein the longitude and latitude of the pixel-by-pixel central position are calculated in advance according to the orbit data of the geosynchronous orbit satellite, and a longitude and latitude grid is prepared;
carrying out pixel-by-pixel spatial positioning on the geosynchronous orbit satellite remote sensing image;
cutting the data set according to a preset space range, and removing a deformed part of a remote sensing image of the geosynchronous orbit satellite;
the correct map projection information is updated in the metadata of the target data set.
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