CN117392363A - Land-sea remote sensing image partition correction method, system, equipment and medium - Google Patents
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Abstract
The invention provides a land-sea remote sensing image partition correction method, a system, equipment and a medium, wherein the correction method comprises the following steps: acquiring an original satellite image, and grouping the original satellite image according to a vector boundary of the original satellite image to acquire an inland region image, a coastal zone image and a far sea region image; respectively carrying out orthographic correction on an inland region image, a coastal zone image and an open sea region image based on different refined RPC parameters and rational function models to generate an inland region orthographic image, a coastal zone orthographic image and an open sea orthographic image; and splicing the inland area orthographic image, the coastal zone orthographic image and the open sea orthographic image to generate a land-sea remote sensing image. The invention realizes the uniform precision of one map of land-sea remote sensing, thereby improving the precision and quality of the orthorectification of the sea-related image and improving the production efficiency of one map of land-sea remote sensing.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to a land-sea remote sensing image partition correction method, a land-sea remote sensing image partition correction system, land-sea remote sensing image partition correction equipment and land-sea remote sensing image partition correction medium.
Background
The method is characterized in that information is extracted based on satellite remote sensing images, the remote sensing images are firstly required to be included in a certain coordinate system, certain geographic references are given to the remote sensing images, ground object entities correspond to image positions, and analysis and comparison of the extracted information and corresponding time-space trend research are carried out on the basis. Therefore, combining the existing basic data, performing orthographic correction on the acquired original remote sensing image, and embedding to form a sea Liu Yingxiang map is the primary work for developing the marine application of the remote sensing technology.
The original satellite remote sensing image is often distorted due to the influence of the system error of the acquisition sensor and the random error of external factors, and the distortion condition must be improved through certain processing. Satellite remote sensing image orthographic correction is generally carried out by combining a group of ground control points and a Digital Elevation Model (DEM) of a corresponding area, and the image after orthographic correction is incorporated into a target coordinate system and image distortion is corrected.
Currently, image orthographic correction for a large area is mainly based on the existing digital orthographic image (DOM) product as a reference image acquisition control point, and satellite image orthographic correction is performed by combining with DEM and based on a Rational Function Model (RFM). The method utilizes the acquired control points, DEM data and RPC parameters of the original satellite image to calculate an imaging geometric model of the image by utilizing adjustment, finally eliminates geometric distortion caused by self errors and random errors, and endows the image with an image target coordinate system to finish the correction result meeting the requirements. Because a land-sea remote sensing image has the characteristics of the image, the requirement of achievements is difficult to meet when the traditional orthographic correction method is utilized, and if the conditions of insufficient control points of the existing DOM achievements, uneven distribution of the control points and no control points of the far-sea images exist in coastal zone areas and the far-sea areas, the phenomenon that geometrical positions are inconsistent at the image junction positions of the land-sea satellite image orthographic correction occurs, and finally the research and application of the image product of the image are influenced.
Disclosure of Invention
The embodiment of the invention provides a land-sea remote sensing image partition correction method, which aims to solve the problems of the related technology, and has the following technical scheme:
in a first aspect, an embodiment of the present invention provides a land-sea remote sensing image partition correction method, including:
a land-sea remote sensing image partition correction method comprises the following steps:
acquiring an original satellite image, and grouping the original satellite image according to a vector boundary of the original satellite image to acquire an inland region image, a coastal zone image and a far sea region image;
respectively carrying out orthographic correction on an inland region image, a coastal zone image and an open sea region image based on different refined RPC parameters and rational function models to generate an inland region orthographic image, a coastal zone orthographic image and an open sea orthographic image;
and splicing the inland area orthographic image, the coastal zone orthographic image and the open sea orthographic image to generate a land-sea remote sensing image.
In one embodiment, the original satellite images include full-color satellite images and multispectral satellite images.
In one embodiment, the method for correcting the orthographic incidence of the inland region image includes:
reading reference remote sensing data, wherein the reference remote sensing data comprises a reference DOM and a reference DEM;
obtaining geometrical coordinates of the same name point in the reference DOM and the full-color satellite image according to the reference DOM; carrying out refinement treatment on the acquired RPC parameters through least square adjustment to obtain refined RPC parameters, and correcting geometric coordinates of the full-color satellite image by adopting the refined RPC parameters and a rational function model to generate a corrected first full-color satellite image;
determining image coordinates of the same name points of the first full-color satellite image and the multispectral satellite image according to the first full-color satellite image and the multispectral satellite image, and performing image matching processing on the first full-color satellite image and the multispectral satellite image based on the image coordinates to obtain a first image after matching processing;
and carrying out projection difference correction on the first image based on the reference DEM to generate an inland area orthographic image.
In one embodiment, an orthographic correction method for coastal zone images includes:
acquiring a plurality of ground control points in a land area of the coastal zone image, acquiring the space projection coordinates of the ground control points according to the reference DOM and the space projection model, and establishing a relation between the image coordinates of the coastal zone image and the space projection coordinates of the ground control points;
performing refinement on the acquired RPC parameters according to a relational expression between the image coordinates of the coastal zone image and the space projection coordinates of the ground control points, and performing geometric correction on the full-color satellite image based on the refined RPC parameters and the rational function model to obtain a second full-color satellite image;
determining image coordinates of the same name points of the second full-color satellite image and the multispectral satellite image according to the second full-color satellite image and the multispectral satellite image, and performing image matching processing on the second full-color satellite image and the multispectral satellite image to obtain a matched second image;
and carrying out projection difference correction on the second image based on the reference DEM to generate an orthographic image of the coastal zone.
In one embodiment, an orthographic correction method for an open sea orthographic image includes:
acquiring RPC parameters of an open source DEM and an original satellite image;
performing geometric correction on the full-color satellite image based on RPC parameters of the original satellite image and a rational function model to obtain a third full-color satellite image;
determining the homonymous point image coordinates of the third full-color satellite image and the multispectral satellite image according to the third full-color satellite image and the multispectral satellite image, and performing image matching processing on the third full-color satellite image and the multispectral satellite image to obtain a matched third image;
and carrying out projection difference correction on the third image based on the open source DEM to generate an offshore orthographic image.
In one embodiment, the image matching process includes:
registering the geometrically corrected panchromatic satellite image and the multispectral satellite image based on a geometric registration model to generate a registered panchromatic satellite image and a registered multispectral satellite image;
and carrying out image fusion on the registered panchromatic satellite image and the registered multispectral satellite image.
In one embodiment, the method further comprises:
color processing is performed on an inland area orthographic image, a coastal zone orthographic image and an open sea orthographic image.
In a second aspect, an embodiment of the present invention provides a land-sea remote sensing image correction system, which executes the land-sea remote sensing image partition correction method as described above.
In a third aspect, an embodiment of the present invention provides an electronic device, including: memory and a processor. Wherein the memory and the processor are in communication with each other via an internal connection, the memory is configured to store instructions, the processor is configured to execute the instructions stored by the memory, and when the processor executes the instructions stored by the memory, the processor is configured to perform the method of any one of the embodiments of the above aspects.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program, the method of any one of the above embodiments being performed when the computer program is run on a computer.
The advantages or beneficial effects in the technical scheme at least comprise:
the method carries out regional correction on inland, coast and far sea areas in the original satellite images, carries out orthographic correction on each regional image by adopting RPC parameter refinement methods under different conditions, ensures the orthographic correction precision under the conditions of less control and no control, finally produces one image of land-sea remote sensing images by uniformly embedding the orthographic images, and realizes the problem that the geometric consistency of one image of land-sea remote sensing images is difficult to ensure compared with the traditional method Liu Haiyi, thereby improving the orthographic correction precision and quality of the sea-related images and improving the production efficiency of one image of land-sea remote sensing images.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will become apparent by reference to the drawings and the following detailed description.
Drawings
In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
FIG. 1 is a flow chart of the land-sea remote sensing image partition correction method of the invention;
FIG. 2 is a diagram of the process of refining RPC parameters in the coastal zone of the present invention;
FIG. 3 is a final stitched land-sea remote sensing image of the present invention;
FIG. 4 is a schematic block diagram of the land-sea remote sensing image partition correction system of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Example 1
The embodiment provides a land-sea remote sensing image partition correction method, which is used for respectively carrying out orthorectification on an inland area, a coastal zone and a far-sea area under the condition that a coastal zone lacks control points to the sea area according to the manufacturing requirement of one image of the land-sea remote sensing image, so as to generate a complete land-sea remote sensing image.
Referring to fig. 1, the land-sea remote sensing image partition correction method specifically includes the following steps:
step S1: acquiring an original satellite image, and grouping the original satellite image according to a vector boundary of the original satellite image to acquire an inland region image, a coastal zone image and a far sea region image;
step S2: respectively carrying out orthographic correction on an inland region image, a coastal zone image and an open sea region image based on different refined RPC parameters and rational function models to generate an inland region orthographic image, a coastal zone orthographic image and an open sea orthographic image;
step S3: and splicing the inland area orthographic image, the coastal zone orthographic image and the open sea orthographic image to generate a land-sea remote sensing image.
In step S1, first, the original image data of the original satellite image participating in the production of a map of the land-sea remote sensing image is automatically grouped according to the spatial position relationship between the vector boundary of each image data and the vector boundary of the existing inland region, coastal zone and offshore zone, the original satellite image is divided into the inland region image, coastal zone image and offshore zone image, and then the images of different zones are respectively subjected to orthographic correction to improve the image precision.
The correction method of the images of different areas comprises the following steps:
step S21: for the inland region image, the existing reference DOM, DEM and original satellite image RPC parameters are utilized, and the inland region image orthographic correction is carried out based on a rational function model. Specifically:
step S211: the method comprises the steps of reading an original satellite image, and loading a full-color satellite image and a multispectral satellite image from the original satellite image, wherein the full-color satellite image is mainly used for realizing precision, and the multispectral satellite image is mainly used for realizing color, and combining the full-color satellite image and the multispectral satellite image to realize result data with high precision and color information.
Step S212: reading a reference DOM and a reference DEM, wherein the reference DEM is elevation data based on a digital elevation model, and the digital elevation model (Digital Elevation Model, abbreviated as DEM) realizes the digital simulation of the ground terrain (namely the digital expression of the surface morphology of the terrain) through limited terrain elevation data;
the reference DOM is based on the image data of the digital orthographic image, and the digital orthographic image (Digital Orthophoto Map abbreviated as DOM)) is the image data generated by performing projection difference correction on the digital aerial photo/remote sensing image (monochrome/color) of the scanning processing by utilizing a digital elevation model, performing mosaic according to the image, and cutting according to the image range.
Step S213: and searching for homonymous points on the reference DOM and the loaded panchromatic satellite image by using a homonymous point matching algorithm based on the reference DOM, and performing automatic positioning point matching of the reference DOM and the panchromatic satellite image. The homonymy point matching algorithm mainly comprises a gray-scale-based template matching algorithm and a feature-based matching algorithm; template matching is to search sub-images similar to the template image from another image according to the known template image; the feature matching algorithm is to extract the features of the images, regenerate feature descriptors, and finally match the features of the two images according to the similarity of the descriptors. The homonymy point matching algorithm is disclosed in the prior art and its algorithm content is not described in detail here.
Step S214: based on the matching positioning points, acquiring spatial coordinates and image coordinates of the same-name points in the reference DOM and the full-color satellite image, carrying out RPC parameter refinement according to an RPC model by utilizing a least squares adjustment principle, and acquiring refined rational function model coefficients.
Step S215: and carrying out geometric correction on the full-color satellite image based on the RPC parameter refinement result and the rational function model to obtain a first full-color satellite image after geometric correction. Wherein, the RPC parameters are the mapping relation from the ground object space to the satellite image space is simulated by using a rational function model, and the method of implementing geometric correction by using the rational function model is disclosed in the prior art and will not be described in detail herein.
Step S216: and searching for homonymous points on the geometrically corrected first full-color satellite image and the geometrically corrected multispectral image by using a homonymous point matching algorithm again, and performing automatic positioning point matching of the geometrically corrected first full-color satellite image and the geometrically corrected multispectral satellite image.
Step S217: and based on the matched positioning points, acquiring the homonymous point image coordinates of the geometrically corrected first panchromatic satellite image and the multispectral satellite image, and realizing the registration of the first panchromatic satellite image and the multispectral satellite image by utilizing a geometric registration model. Wherein registration is the finding of a spatial mapping between one image pixel to another.
Step S218: and after determining the mapping relation, performing image fusion on the geometrically registered first panchromatic satellite image and the multispectral satellite image.
Step S219: and carrying out projection difference correction on the registered and fused images based on the reference DEM data by utilizing a rational function model to finish satellite image orthographic correction, thereby generating an inland region orthographic image.
The essence of the above-mentioned RPC model is a rational function model for correlating the ground point coordinates (x, y, z) with their corresponding image point coordinates (r, c) using a ratio polynomial, which is defined as follows:
；
wherein p is 1 ，p 2 ，p 3 ，p 4 The (r, c) and (X, Y, Z) resolution is a normalized coordinate of the image coordinates and the ground coordinates after translation and scaling, the powers of the respective coordinate components X, Y, Z of each term are not more than 3 at maximum, and each polynomial is of the form:
；
wherein a 1-a 20 are each coefficient of the rational function model.
The RPC model is divided into 3 forms according to the denominator relationship:the method comprises the steps of carrying out a first treatment on the surface of the Each form is further divided into 3 forms according to the difference of polynomial coefficients, so that the RPC model has 9 forms in total.
Step S22: and aiming at the coastal zone region image, performing coastal zone region image orthographic correction by utilizing fusion reference DOM and fusion DEM and RPC parameter solving based on a space projection model.
Because the coastal zone image often has a land area in the local part of the image, other areas are sea surfaces or islands, enough ground control points are difficult to be effectively extracted or the distribution of the control points is uneven, and the correction precision is difficult to reach the achievement requirement by using the traditional method based on the rational function model, so the remote sensing image geometric correction method based on the rational function model has limitations.
In order to solve the problem, an RPC parameter refinement method based on a space projection model is provided by referring to an orthographic correction thought of a rational function mode, and the reference DOM and DEM of the existing land area are expanded to the ocean to cover the area range of the original satellite image so as to finish orthographic correction. The specific implementation method is as follows:
step S221: reading an original satellite image, loading a full-color satellite image and a multispectral satellite image, and synchronously reading RPC parameters;
step S222: determining a space projection formula:
；
linearizing the space projection formula to obtain:
；
the inverse solution formula is:
；
in the above-mentioned formula(s),
；
；
；
；
wherein P is 1 For the period of earth rotation, P 2 The satellite operation period is;the spatial projection transformation precision is obtained; />To transform dimensions; />Is the long radius of the ellipsoid of the earth; e is the first eccentricity of the earth; i is the satellite orbit tilt.
Step S223: acquiring more than 4 control points in a land area of the coastal zone image, and selecting check points; the control points are selected according to the principles of clarity of ground object marks, stability of ground objects, uniform distribution, sufficient quantity and the like, and corresponding control points and elevation values of check points are acquired from the DEM; referring to fig. 2, the spatial projection coordinates of the ground control point are obtained by using the spatial projection model and its linearization formula in step S222, and the relationship between the spatial projection coordinates of the ground point and the corresponding image point coordinates in the coastal zone image is established by using a polynomial, which has the following form:
；
wherein a 0-a 5, b 0-b 5 are each coefficient of the polynomial function.
And obtaining a conversion coefficient by using the corresponding control point coordinates through the polynomial, thereby establishing a corresponding relation between the image coordinates and the space projection model coordinates.
Step S224: and carrying out RPC parameter refinement by using a corresponding relation between the image coordinates and the space projection model coordinates.
Step S225: and carrying out geometric correction on the full-color satellite image based on the RPC parameter refinement result and the rational function model to obtain a second full-color satellite image after geometric correction.
Step S226: and searching for homonymous points on the geometrically corrected second full-color satellite image and the geometrically corrected multispectral image by utilizing a homonymous point matching algorithm based on the geometrically corrected second full-color satellite image, and performing automatic positioning point matching of the geometrically corrected second full-color satellite image and the geometrically corrected multispectral satellite image.
Step S227: and based on the matched positioning points, acquiring the homonymous point image coordinates of the geometrically corrected second panchromatic satellite image and the multispectral satellite image, and realizing the registration of the second panchromatic satellite image and the multispectral satellite image by utilizing a geometric registration model.
Step S228: and performing image fusion on the geometrically registered second full-color satellite image and the multispectral satellite image.
Step S229: and (3) carrying out projection difference correction on the fused image based on the reference DEM data by utilizing a rational function model to register the fusion result, and completing satellite image orthographic correction to produce the coastal zone orthographic image.
Step S23: aiming at the image of the open sea area, the open source DEM and the RPC parameters of the original satellite image are utilized to correct the orthographic emission of the open sea area based on a rational function model.
The open sea image mainly takes the sea water surface as the main part, no ground control point exists, the open source DEM and the RPC parameters of the original satellite image are adopted, the open sea area orthographic correction is carried out based on a rational function model, and the specific implementation method is as follows:
step S231: reading an original satellite image, loading a full-color satellite image and a multispectral satellite image, and synchronously reading RPC parameters;
step S232: reading an open source DEM;
step S233: performing geometric correction on the full-color satellite image based on the RPC parameter result and the rational function model to obtain a geometrically corrected third full-color satellite image;
step S234: searching for homonymous points on the geometrically corrected third full-color satellite image and the geometrically corrected multispectral image by utilizing a homonymous point matching algorithm based on the geometrically corrected third full-color satellite image, and performing automatic positioning point matching of the geometrically corrected third full-color satellite image and the geometrically corrected multispectral satellite image;
step S235: based on the matching positioning points, obtaining the homonymous point image coordinates of the geometrically corrected third full-color satellite image and the geometrically corrected multispectral satellite image, and realizing the registration of the third full-color satellite image and the multispectral satellite image by utilizing a geometric registration model;
step S236: performing image fusion on the geometrically registered third full-color satellite image and the multispectral satellite image;
step S237: and (3) utilizing a rational function model to register the fusion result, completing orthographic correction to correct the projection difference of the fused image based on the reference DEM data, completing satellite image orthographic correction, and producing an open sea area orthographic image.
In step S3, the corrected images of the inland region, the coastal zone region and the open sea region are color processed and spliced and embedded into a complete orthographic image result, as shown in fig. 3, to form a map of the land-sea remote sensing image, and finally the land-sea remote sensing image is output.
The correction method of the land-sea remote sensing image solves the problem of orthographic correction of satellite remote sensing images under the conditions of a small number of ground control points and no control points from a coastal zone to a far sea area, and the RPC parameter refinement methods under different conditions are designed through the grouping of the original satellite image areas, so that the precision of orthographic correction under the conditions of less control and no control is ensured, compared with the problem that the geometrical consistency of Liu Haiyi images is difficult to ensure in the traditional method, the precision uniformity of one image of land-sea remote sensing is realized, the precision and quality of orthographic correction of sea-related images are improved, and the production efficiency of one image of land-sea remote sensing is improved.
Example two
The present embodiment provides a land-sea remote sensing image correction system, which executes the land-sea remote sensing image partition correction method described in the first embodiment. As shown in fig. 4, the system includes:
the grouping module is used for acquiring an original satellite image, and grouping the original satellite image according to the vector boundary of the original satellite image to acquire an inland region image, a coastal zone image and a far sea region image;
the correction module is used for respectively carrying out orthographic correction on the inland region image, the coastal zone image and the far sea region image through a rational function model based on different refined RPC parameters to generate an inland region orthographic image, a coastal zone orthographic image and a far sea orthographic image;
and the splicing module is used for splicing the inland area orthographic image, the coastal zone orthographic image and the open sea orthographic image to generate a land-sea remote sensing image.
The functions of each module in the system of the embodiment of the present invention may be referred to the corresponding descriptions in the above method, and will not be repeated here.
Example III
Fig. 5 shows a block diagram of an electronic device according to an embodiment of the invention. As shown in fig. 5, the electronic device includes: memory 100 and processor 200, and memory 100 stores a computer program executable on processor 200. The processor 200, when executing the computer program, implements the land-sea remote sensing image division correction method in the above embodiment. The number of memory 100 and processors 200 may be one or more.
The electronic device further includes:
the communication interface 300 is used for communicating with external equipment and performing data interaction transmission.
If the memory 100, the processor 200, and the communication interface 300 are implemented independently, the memory 100, the processor 200, and the communication interface 300 may be connected to each other and perform communication with each other through buses. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (Peripheral Component Interconnect, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 100, the processor 200, and the communication interface 300 are integrated on a chip, the memory 100, the processor 200, and the communication interface 300 may communicate with each other through internal interfaces.
The embodiment of the invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the method provided in the embodiment of the invention.
The embodiment of the invention also provides a chip, which comprises a processor and is used for calling the instructions stored in the memory from the memory and running the instructions stored in the memory, so that the communication equipment provided with the chip executes the method provided by the embodiment of the invention.
The embodiment of the invention also provides a chip, which comprises: the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the invention.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (digital signal processing, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), field programmable gate arrays (fieldprogrammablegate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be a processor supporting an advanced reduced instruction set machine (advanced RISC machines, ARM) architecture.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EPROM (EEPROM), or a flash memory, among others. Volatile memory can include random access memory (random access memory, RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, static RAM (SRAM), dynamic RAM (dynamic random access memory, DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous DRAM (SLDRAM), and direct memory bus RAM (DR RAM).
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present invention are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Any process or method description in a flowchart or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process. And the scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed in a substantially simultaneous manner or in an opposite order from that shown or discussed, including in accordance with the functions that are involved.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the methods of the embodiments described above may be performed by a program that, when executed, comprises one or a combination of the steps of the method embodiments, instructs the associated hardware to perform the method.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules described above, if implemented in the form of software functional modules and sold or used as a stand-alone product, may also be stored in a computer-readable storage medium. The storage medium may be a read-only memory, a magnetic or optical disk, or the like.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The land-sea remote sensing image partition correction method is characterized by comprising the following steps of:
acquiring an original satellite image, and grouping the original satellite image according to a vector boundary of the original satellite image to acquire an inland region image, a coastal zone image and a far sea region image;
respectively carrying out orthographic correction on the inland region image, the coastal zone image and the far sea region image based on different refined RPC parameters and rational function models to generate an inland region orthographic image, a coastal zone orthographic image and a far sea orthographic image;
and splicing the inland region orthographic image, the coastal zone orthographic image and the open sea orthographic image to generate a land-sea remote sensing image.
2. The land-sea remote sensing image partition correction method according to claim 1, wherein the original satellite image comprises a full-color satellite image and a multispectral satellite image.
3. The land-sea remote sensing image partition correction method according to claim 2, wherein the orthographic correction method of an inland region image comprises:
reading reference remote sensing data, wherein the reference remote sensing data comprises a reference DOM and a reference DEM;
obtaining geometrical coordinates of the reference DOM and homonymous points in the full-color satellite image according to the reference DOM; carrying out refinement treatment on the acquired RPC parameters through least square adjustment to obtain refined RPC parameters, and correcting geometric coordinates of the full-color satellite image by adopting the refined RPC parameters and a rational function model to generate a corrected first full-color satellite image;
determining image coordinates of homologous points of the first panchromatic satellite image and the multispectral satellite image according to the first panchromatic satellite image and the multispectral satellite image, and performing image matching processing on the first panchromatic satellite image and the multispectral satellite image based on the image coordinates to obtain a first image after matching processing;
and carrying out projection difference correction on the first image based on the reference DEM to generate the inland region orthographic image.
4. The land-sea remote sensing image partition correction method according to claim 2, wherein the orthographic correction method of the coastal zone image comprises:
acquiring a plurality of ground control points in a land area of the coastal zone image, acquiring the space projection coordinates of the ground control points according to a reference DOM and a space projection model, and establishing a relational expression between the image coordinates of the coastal zone image and the space projection coordinates of the ground control points;
performing refinement on the acquired RPC parameters according to a relational expression between the image coordinates of the coastal zone image and the space projection coordinates of the ground control points, and performing geometric correction on the full-color satellite image based on the refined RPC parameters and a rational function model to obtain a second full-color satellite image;
determining image coordinates of the same name points of the second full-color satellite image and the multispectral satellite image according to the second full-color satellite image and the multispectral satellite image, and performing image matching processing on the second full-color satellite image and the multispectral satellite image to obtain a second image after the matching processing;
and carrying out projection difference correction on the second image based on the reference DEM to generate the coastal zone orthographic image.
5. The land-sea remote sensing image partition correction method according to claim 2, wherein the orthographic correction method of the open-sea orthographic image comprises:
acquiring an RPC parameter of the original satellite image;
performing geometric correction on the full-color satellite image based on the RPC parameters and the rational function model of the original satellite image to obtain a third full-color satellite image;
determining the coordinates of homonymous point images of the third full-color satellite image and the multispectral satellite image according to the third full-color satellite image and the multispectral satellite image, and performing image matching processing on the third full-color satellite image and the multispectral satellite image to obtain a matched third image;
and carrying out projection difference correction on the third image based on the open source DEM to generate the open sea orthographic image.
6. The land-sea remote sensing image partition correction method according to any one of claims 3, 4, or 5, wherein the image matching process includes:
registering the geometrically corrected panchromatic satellite image and the multispectral satellite image based on a geometric registration model to generate a registered panchromatic satellite image and a registered multispectral satellite image;
and carrying out image fusion on the registered full-color satellite image and the registered multispectral satellite image.
7. The land-sea remote sensing image partition correction method according to claim 1, further comprising:
and performing color processing on the inland region orthographic image, the coastal zone orthographic image and the open sea orthographic image.
8. A land-sea remote sensing image correction system, characterized in that the land-sea remote sensing image partition correction method according to any one of claims 1 to 7 is performed.
9. An electronic device, comprising: a processor and a memory storing instructions that are loaded and executed by the processor to implement the land-sea telemetry image partition correction method of any one of claims 1 to 7.
10. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, which when executed by a processor, implements the land-sea remote sensing image partition correction method according to any one of claims 1 to 7.
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