CN109029368B - Image space compensation remote sensing image/SAR image high-precision geometric positioning post-processing method - Google Patents
Image space compensation remote sensing image/SAR image high-precision geometric positioning post-processing method Download PDFInfo
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Abstract
The invention provides a post-processing method for carrying out geometric positioning on a ground object point by using a remote sensing image/Synthetic Aperture Radar (SAR) image. Aiming at the current situation that the space position information of the earth surface and the ground object is extracted by using a remote sensing image or an SAR image, the point location precision cannot meet the application requirements of large and medium scale mapping and the like, a linearized error equation is established according to a nonlinear mapping function of the image side coordinates and the object side coordinates of the earth surface and a whole set of processing flow and method for acquiring the high-precision space position of the earth surface and the ground object according to the digital image are established by applying a model parameter estimation theory of which variables contain errors (errors-in-variables, EIV). The method is suitable for processing the block adjustment data of the satellite-borne optical remote sensing and satellite-borne SAR digital image area network, and can effectively solve the problem of low positioning precision.
Description
Technical Field
The invention relates to a method for acquiring geometric coordinates of earth surface points and precision evaluation thereof in the industries of space photogrammetry, surveying and mapping, computational mathematics and the like, in particular to a high-precision geometric positioning post-processing method of remote sensing images/SAR images with image space compensation.
Background
The geometric positioning of the earth surface points by using the satellite-borne remote sensing images/satellite-borne SAR images is an important application of the spatial information technology in the field of geography, and in recent years, along with the construction of a high-resolution earth observation system in China, the acquisition of the earth surface points by using the spatial information technology becomes a main technical means for acquiring geographic information data. Meanwhile, the prior art adopts photogrammetry theory and technology to carry out geometric positioning of earth surface points, needs large-scale earth surface control measurement, and solves the earth surface point positions by means of a collinearity equation. The method has the defects that the operation flow is complex, relevant parameters of hardware equipment need to be known in the process of processing internal work data, and production operation cannot be carried out in overseas areas lacking surface control points or in severe environments where surface operation cannot be carried out. The traditional theory and process of photogrammetry cannot meet the requirements of people on acquiring geospatial information with high precision and high efficiency.
Disclosure of Invention
The purpose of the invention is: the image space compensation remote sensing image/SAR image high-precision geometric positioning post-processing method is designed, the existing photogrammetry operation process is improved and simplified, and the image space compensation and EIV data processing model-based remote sensing image/SAR image high-precision geometric positioning post-processing method is established under the condition of no ground control point or rare ground control point.
The principle of the invention is as follows: the method comprises the steps of establishing a linear mapping relation between an image point coordinate and an object point coordinate according to a Rational Function Model (RFM), establishing an EIV parameter model taking the image point coordinate and the object point coordinate observation error into consideration by using a linearized mapping model and taking the image point coordinate and the object point coordinate as observed values and the correction number of the mapping function parameter (RPC) and the object point coordinate correction number in an image attached file as variables, correcting the influence of system errors on the RPC parameter, realizing high-precision adjustment of a remote sensing image or an SAR image area network through accurate compensation of an image space, and improving the precision of the space coordinate of the earth surface point through digital image interpretation.
The technical solution of the invention is as follows: the method comprises the steps of constructing a mapping relation between image point coordinates and object point coordinates through an EIV model, linearizing an RFM function by applying an RPC parameter initial value in an image attached file, analyzing the ill-condition and tolerance of the constructed mapping relation model, and establishing an earth surface point location model for solving the object point coordinates by the image point coordinates and a solving process thereof while ensuring the estimation accuracy of model parameters. Then, dividing images with higher positioning accuracy in the digital images of different satellite-borne platforms into virtual homonymous points constructed by different elevation surfaces, taking the virtual homonymous control points as the basis for positioning and orienting the whole area network, and performing multi-source digital image area network adjustment by an EIV (edge image correlation) model of the RFM.
The method comprises the following specific steps:
step1, acquiring RPC parameter information in a satellite-borne optical remote sensing stereopair or a satellite-borne SAR interference image and an image attached file, and laying a field control network;
step2, checking the image quality, and checking the accuracy and quality of the control network;
step3, checking the RPC parameter value according to the RFM function by using the image point coordinates and the object point coordinates of the same-name point, and carrying out precision evaluation on the RPC parameter value;
step4, linearizing the RFM function by taking the RPC parameter in the attached file as an initial value, constructing an EIV model, and evaluating the ill-conditioned and poor resistance of the constructed RFM function EIV model;
step5, carrying out unbiased estimation on the RFM function parameters considering the influences of the EIV model ill-condition and tolerance factors;
step6, correcting errors of image point coordinates and object point coordinates of the same name points compensated by an image space, and constructing a block adjustment model of a digital image area network;
step7, under the condition of no ground control or rare ground control, the overall adjustment of the multi-source remote sensing image is realized.
In Step1, acquiring a digital image comprises constructing a stereopair/interference image pair by the digital image acquired by a homologous spaceborne remote sensing platform or a spaceborne SAR platform, and constructing the stereopair/interference image pair by digital images of different sources; the control network layout is to obtain the image side coordinate and the object side coordinate of the same-name point, GNSS static control measurement or RTK measurement is adopted, and the operation mode is judged by the resolution of the digital image.
In Step2, checking the image resolution by the digital image quality checking side, repairing the original image by applying a steady-state re-imaging geometric model, and establishing an RFM model; the method comprises the following steps of ground control network adjustment and precision evaluation, wherein the precision evaluation of the control network comprises the internal coincidence precision evaluation between control points and the external coincidence precision evaluation between newly-added control points of the control network and control points of a target coordinate system; and the coordinates of the ground control points are simultaneously used as the evaluation basis of the geometric positioning precision of the image.
In Step3, the RPC parameters in the image attached file are evaluated in precision, the precision index is the error in the statistical parameters, and the system error possibly contained in the RPC parameters is detected by combining an RFM function and a probability statistical method.
In Step4, taking RPC parameters in the detected attached file which meet the precision requirement as initial values, applying Taylor series to linearize the nonlinear RFM, and analyzing the rounding errors of the RFM expanded in different series; expanding a Gauss-Markov error model (G-M) of the RFM into an EIV model considering the error of a model coefficient matrix, and determining a random model of the EIV model according to the prior information related to the image calculated in Step1-Step 3.
In Step5, judging the condition number of an EIV model method matrix of the RFM, and determining the critical value of the condition number of the method matrix when the model is ill; constructing a regularization strategy and a regularization function of the morbid RFM model; performing EIV model tolerance analysis of RFM, determining the maximum collapse rate of the model, and constructing a tolerance estimation strategy of the model; establishing an unbiased estimation iteration process of the RFM parameters by fusing a random model post-test estimation algorithm such as minimum variance estimation and the like;
in Step6, establishing an RFM parameter correction number unbiased estimation method based on image space compensation according to the coordinate correction value of the image point at the same name point; the coordinates of the object points of the same name points adopt a geodetic coordinate system, and the initial values of the coordinates are obtained by converting a WGS-84 ellipsoid into space three-dimensional rectangular coordinates (difference) of control points in a WGS-84 coordinate system, wherein the space three-dimensional rectangular coordinates (difference) are obtained by field control measurement; and (3) adding constraint to the coordinate correction number of the image point of the same name point, and establishing a digital image area network adjustment model by taking the RPC parameter and the geodetic coordinate correction number of the same name point as target parameters.
In Step7, when no homonymous control point or rare homonymous control points are caused by the fact that control measurement cannot be performed on the ground due to factors such as harsh field operation environment, virtual homonymous points are constructed in different elevation planes through images with high positioning accuracy in remote sensing data from different sources, and the virtual homonymous control points are used as the basis for positioning and orienting the area network, so that the accuracy of adjustment of the area network of the multi-source digital image data is improved.
The invention has the following advantages:
1. the method realizes the streamlined operation of carrying out the geometric positioning of the ground point by the remote sensing image/SAR image.
2. Establishing a digital image space point location and object space point location mapping irrelevant to hardware equipment parameters
And the mathematical model is adopted to simplify the complexity of image processing.
3. And establishing a totally-enclosed analytic formula considering the observation error of the coordinates of the image control points and the observation error of the coordinates of the object control points, and realizing the high-precision calculation of the relevant parameters of the geometric positioning by applying the digital images.
4. The high-precision adjustment of the digital image area network is realized under the condition of no ground control point or rare ground control point.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The technical implementation of the present invention is described below by taking the MODIS satellite remote sensing image processing as an example, but the technical implementation is not to be construed as a limitation.
Step1, constructing a stereo pair by using the satellite-borne remote sensing image or constructing an interference image by using the satellite-borne SAR image, and acquiring and establishing RPC parameters in the image attached file and a database thereof; the GNSS deployment and survey field control network is applied, a static control network is adopted in the network deployment form, or an RTK operation form is adopted to acquire the coordinates of control points, and the operation flow is in accordance with the corresponding national standard requirement;
step2, checking the image resolution by the digital image quality checking side, repairing the original image by applying a steady-state re-imaging geometric model, and establishing an RFM (radio frequency mass spectrometry) model, wherein the digital image checking standard meets the requirement of the industry specification; controlling the processing of the measurement data and the precision evaluation thereof, wherein the final result of the control points obtained by the control measurement is the coordinate of the control points or the coordinate difference between the control points under the WGS-84 reference frame, and evaluating the precision thereof;
step3, checking the RPC parameter value in the image attached file by using a function model, and emphasizing the application of an RFM function, but not limiting the function; detecting system errors possibly contained in RPC parameters by applying a probability statistical function, and applying t test with emphasis, but not limited to the test function;
step4, according to the RPC parameters checked in Step3, a Taylor series (not limited to the Taylor series) is applied to linearize the RFM function, and the specific series expanded is judged according to the precision requirement in practical application; constructing a linearized RFM function G-M error model, considering errors of observation elements in a coefficient matrix, expanding the G-M model into an EIV model, and determining a random model according to prior information obtained by checking the observation elements of the model in step.1-3;
step5, judging the ill-conditioned character of the model according to the condition number of the equation matrix, and when the RFM model presents the ill-conditioned character, realizing the regularization of the RFM model by applying a biased estimation algorithm; identifying gross errors contained in the observation elements according to the correction numbers of the observation elements; carrying out robust estimation on model parameters by applying random model post-test estimation and an equivalent weight function (applying an IGG weight function in a side-weighted manner, but not limited to the function);
step6, calculating the coordinates of the object points and evaluating the precision by applying the RFM model parameter estimation and using the image point coordinate correction as the minimum constraint and the object point coordinate correction as the variable digital image block adjustment model;
and Step7, when no ground control or rare ground control exists, constructing virtual control points according to the RFM model by using the images with higher positioning precision in the digital images, and using the virtual control points as the basis for positioning and orienting adjacent images so as to carry out adjustment of the whole area network.
Claims (1)
1. The image space compensation remote sensing image or SAR image high-precision geometric positioning post-processing method is characterized by comprising the following steps: establishing a mapping relation between an image space coordinate and an object space coordinate through an EIV model, linearizing an RFM rational function model by applying an RPC mapping function parameter initial value in an image attached file, analyzing the ill-condition and tolerance of the established mapping relation model, and establishing a surface point location model for resolving the object space coordinate from the image space coordinate and a resolving process thereof while ensuring the estimation precision of model parameters; dividing images with higher positioning precision in digital images of different satellite-borne platforms into virtual homonymous points constructed by different elevation surfaces, taking the virtual homonymous control points as the basis for positioning and orienting the whole area network, and performing multi-source digital image area network adjustment by an EIV (edge equalization) model of an RFM (rational function model); the method comprises the following specific steps:
step1, acquiring RPC mapping function parameter information in a satellite-borne optical remote sensing stereopair or a satellite-borne SAR interference image and an image attached file, and laying out a field control network; the digital image acquisition not only comprises a stereopair constructed by digital images acquired by a homologous spaceborne remote sensing platform and an interference image constructed by a homologous spaceborne SAR platform, but also comprises stereopair or interference images constructed by different homologous digital images; the control network layout is to obtain the image side coordinate and the object side coordinate of the same-name point, adopt GNSS static control measurement or RTK measurement, and the operation mode is judged by the resolution of the digital image;
step2, checking the image quality, and checking the accuracy and quality of the control network; the digital image quality checking emphasizes checking the image resolution, restores the original image by applying a steady-state re-imaging geometric model, and simultaneously establishes an RFM rational function model; the method comprises the following steps of ground control network adjustment and precision evaluation, wherein the precision evaluation of the control network comprises the internal coincidence precision evaluation between control points and the external coincidence precision evaluation between newly-added control points of the control network and control points of a target coordinate system; the coordinates of the ground control points are simultaneously used as evaluation basis of geometric positioning precision of the image;
step3, checking RPC mapping function parameters according to the RFM rational function model by using the image space coordinates and the object space coordinates of the same-name points, and carrying out precision evaluation on the RPC mapping function parameters; the method comprises the steps of evaluating the accuracy of RPC mapping function parameters in an image attached file, detecting system errors possibly contained in the RPC mapping function parameters by combining an RFM rational function model and a probability statistical method, wherein the accuracy index is errors in statistical parameters;
step4, linearizing the RFM rational function model by taking the RPC mapping function parameter in the attached file as an initial value, constructing an EIV model, and evaluating the morbidity and the tolerance of the EIV model of the constructed RFM rational function model; the method comprises the following steps of (1) taking RPC mapping function parameters in a detected attached file which meets the precision requirement as initial values, applying Taylor series to linearize a nonlinear RFM rational function model, and analyzing rounding errors of the RFM rational function model expanded in different series; expanding a Gauss-Markov error model (G-M) of the RFM rational function model into an EIV model considering model coefficient matrix errors, and determining a random model of the EIV model according to image related prior information calculated in Step1-Step 3;
step5, taking the influences of EIV model pathology and tolerance into consideration, and carrying out unbiased estimation on the RFM rational function model; judging the condition number of an EIV model method matrix of the RFM rational function model, and determining the critical value of the condition number of the EIV model method matrix when the model is ill; constructing a regularization strategy and a regularization function of the pathological RFM rational function model; carrying out EIV model tolerance analysis on the RFM rational function model, determining the maximum collapse rate of the model, and constructing a tolerance estimation strategy of the model; establishing an unbiased estimation iteration flow of the RFM rational function model by fusing a random model post-test estimation algorithm such as minimum variance estimation and the like;
step6, correcting errors of image side coordinates and object side coordinates of the same-name points compensated by the image side, and constructing a digital image block adjustment model; establishing an unbiased estimation method of RFM rational function model correction number based on image space compensation according to the image space coordinate correction value of the same-name point; the object space coordinates of the homonymous points are obtained by adopting a geodetic coordinate system, and the initial values of the object space coordinates are obtained by converting WGS-84 ellipsoids from the space three-dimensional rectangular coordinates of control points in a WGS-84 coordinate system, which are obtained by field control measurement; adding constraint on the image space coordinate correction number of the same-name point, and establishing a digital image area network adjustment model by taking an RPC mapping function parameter and the geodetic coordinate correction number of the same-name point as target parameters;
step7, under the condition that ground control measurement cannot be carried out or the number of ground control points is insufficient, the overall adjustment of the multi-source remote sensing image is carried out; when no homonymous control point or rare homonymous control points are caused by the fact that control measurement cannot be carried out on the ground due to factors such as severe field operation environment and the like, virtual homonymous points are constructed in different elevation planes through images with high positioning accuracy in remote sensing data from different sources, the virtual homonymous control points are used as the basis for positioning and orienting the area network, and the accuracy of adjustment of the area network of the multi-source digital image data is improved.
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| CN110500995B (en) * | 2019-07-12 | 2020-06-23 | 武汉大学 | Method for establishing high-resolution satellite image equivalent geometric imaging model by using RPC parameters |
| CN111121727A (en) * | 2020-01-10 | 2020-05-08 | 西南交通大学 | Image control point target system and method for unmanned aerial vehicle measurement and airborne laser radar |
| CN111538044B (en) * | 2020-04-10 | 2023-03-07 | 苏州市高新北斗导航平台有限公司 | Low-cost RTK receiver positioning accuracy testing method |
| CN112258422B (en) * | 2020-08-17 | 2023-04-28 | 中国人民解放军61540部队 | Automatic refinement method for rational polynomial parameters (RPC) of stereoscopic image |
| TWI735390B (en) * | 2020-12-01 | 2021-08-01 | 財團法人金屬工業研究發展中心 | Method for real-time positioning compensation of image positioning system and image positioning system capable of real-time positioning compensation |
| CN113255740B (en) * | 2021-05-07 | 2024-04-19 | 北京市遥感信息研究所 | Multi-source remote sensing image adjustment positioning accuracy analysis method |
| CN113899387B (en) * | 2021-09-27 | 2023-09-22 | 武汉大学 | Post-verification compensation-based optical satellite remote sensing image area network adjustment method and system |
| CN117151991B (en) * | 2023-07-06 | 2024-05-31 | 北京数慧时空信息技术有限公司 | Remote sensing image RPC repairing method |
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Application publication date: 20181218 Assignee: JIANGSU TS TRAFFIC DESIGN & RESEARCH INSTITUTE CO.,LTD. Assignor: HUAIYIN NORMAL University Contract record no.: X2021980014385 Denomination of invention: High precision geometric positioning post-processing method of remote sensing image / SAR image based on image square compensation Granted publication date: 20200908 License type: Common License Record date: 20211213 |
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