Refining method for multi-mode space and air remote sensing cooperative surveying and mapping regional network

Abstract

The invention provides a multi-mode aerospace remote sensing cooperative surveying and mapping regional network refinement method. The method comprises the following steps: constructing an aviation optics and SAR image rigorous positioning model under a geocentric rectangular coordinate system; extracting a rational polynomial model parameter RPC under a satellite optics and SAR image geocentric rectangular coordinate system; constructing a positioning model and a distance equation of a satellite laser height measurement remote sensing target; extracting the position of the light spot energy center on the light spot image, and enhancing the definition of the light spot image; homonymy point matching of a multi-mode remote sensing target is achieved through image gradient feature matching, homonymy point extraction between optical images is achieved through sift feature or least square matching, and matching between laser height measurement data and remote sensing images is achieved through light spot energy center position extraction and template matching; extracting homonymy points between the SAR and the Lidar data by using an SAR simulation image matching technology; constructing a multi-modal remote sensing area network joint adjustment model; and integrally solving the refined parameters, and solving the coordinates of the encryption points.

Description

Technical Field

[0001] The invention relates to a surveying and mapping remote sensing data processing method, in particular to a multi-modal aerospace remote sensing collaborative surveying and mapping regional network refinement method. Background Art

[0002] Remote sensing mapping sensors are key devices for obtaining information about the earth's surface. They can be divided into several types according to their working principles and application scopes: (1) Optical sensors: Optical sensors are sensors that use energy from electromagnetic bands such as visible light, infrared light, and ultraviolet light to obtain information about the earth's surface. Common optical sensors include cameras and imaging spectrometers carried on spacecraft. They can obtain information such as the color, texture, and morphology of the earth's surface and are widely used in the fields of surface cover classification, environmental monitoring, and urban planning. An optical panchromatic sensor is a sensor that can obtain high-resolution monochrome images across the entire visible spectrum. Compared with multispectral sensors, optical panchromatic sensors only collect data within one wavelength range, usually in the blue to near-infrared band of the visible spectrum (0.4 - 0.9 microns).

[0003] (2) Synthetic Aperture Radar (SAR) Sensor: SAR sensors use radar technology to obtain microwave reflection signals from the ground surface. They are independent of weather and lighting conditions and are therefore suitable for monitoring and mapping the ground surface in various environments. SAR sensors can obtain high-resolution images of the ground surface and provide information on altitude, vegetation structure and other ground features. Synthetic aperture radar generates high-resolution radar images by receiving radar signals reflected from the ground using synthetic aperture technology. Compared with optical remote sensing, SAR remote sensing has the advantage of not being restricted by weather conditions such as clouds, rain and fog, and is suitable for monitoring and mapping of land surfaces, ocean surfaces and ice and snow areas.

[0004] (3) LiDAR sensor: LiDAR sensor uses the principle of laser beam emission and reception to measure the distance and elevation information of surface objects. LiDAR sensors have the characteristics of high precision and high resolution, and can be used to generate digital elevation models (DEM) and digital terrain models (DTM), as well as terrain and geomorphology analysis, urban planning and other applications. Satellite laser altimetry uses a satellite-borne LiDAR system to measure the elevation information of the surface and terrain. By emitting laser pulses to the ground and measuring the time required from the emission to the reception of the laser pulse, the elevation information of the surface point can be calculated. Laser altimetry data has the characteristics of high precision and high vertical resolution, and can be used to construct digital terrain models (DTM), digital surface models (DSM), and extract terrain features. At present, many countries and regions have launched satellites equipped with laser altimetry systems. These satellites provide high-resolution surface elevation data on a global scale, providing important data support for earth science research and applications.

[0005] According to the differences in sensor carrier platforms, surveying and mapping remote sensing can be mainly divided into ground surveying and mapping remote sensing, aerial surveying and mapping remote sensing and space surveying and mapping remote sensing. For example, aircraft platforms using aerial surveying and mapping remote sensing and space satellite platforms using space surveying and mapping remote sensing are the most common remote sensing platforms for surveying and mapping remote sensing.

[0006] (1) Aerial surveying and remote sensing is the use of remote sensing sensors carried by aircraft or other aircraft to obtain remote sensing data of the earth's surface by flying in the atmosphere. Since aircraft are closer to the ground, aerial surveying and remote sensing can obtain high-resolution remote sensing images, which are usually higher than satellite remote sensing data. Aerial surveying and remote sensing is widely used in urban planning, land use monitoring, environmental protection, resource exploration and other fields. Due to its high resolution, aerial surveying and remote sensing is particularly suitable for application scenarios with small areas and high requirements for details.

[0007] (2) Space surveying and remote sensing is the use of remote sensing sensors carried by satellites or other spacecraft to obtain remote sensing data of the earth's surface by orbiting the earth in space. The resolution of space surveying and remote sensing is usually lower than that of aerial surveying and remote sensing, but its coverage is wider and can achieve global earth observation. Space surveying and remote sensing is widely used in global-scale application fields such as meteorological monitoring, environmental change monitoring, and disaster monitoring and assessment. Due to its advantages of wide coverage and continuous observation, space surveying and remote sensing plays an important role in global environmental monitoring and climate change research.

[0008] Aerial surveying and remote sensing and space surveying and remote sensing use different data acquisition platforms and resolution characteristics, and are used in earth observation and remote sensing applications in different scopes and fields. Aerial surveying and remote sensing is suitable for small-scale high-resolution application scenarios, while space surveying and remote sensing is suitable for large-scale global environmental monitoring and resource management.

[0009] Joint remote sensing mapping is to use multiple sensor data to complement and verify each other to improve the accuracy and reliability of remote sensing information extraction. By integrating multi-source data, the shortcomings of a single sensor in terms of spatial resolution, remote sensing information acquisition capability and data acquisition cycle can be compensated, thereby achieving a more comprehensive and detailed description and analysis of surface features. Joint remote sensing mapping technology has broad application prospects in geological exploration, environmental monitoring, disaster assessment and other fields. Furthermore, in remote sensing mapping, it is crucial to achieve high-precision positioning, especially in the absence of control points or in the absence of control points. The control points are usually landmarks or objects with known locations, which are used for map registration and image correction to improve the geographic positioning accuracy of remote sensing data. However, in some remote, sparsely populated areas or emerging development areas, control points may be extremely scarce or non-existent. In this case, the use of multimodal joint remote sensing technology can make up for this deficiency. Multimodal joint remote sensing technology can achieve high-precision positioning in the absence of control points or in the absence of control points. Through data fusion, cross-positioning and other means, the geographic positioning accuracy of remote sensing data can be improved, providing reliable geographic information support for the application field of remote sensing mapping.

[0010] In aerospace surveying and remote sensing, it is also necessary to consider orbit refinement, attitude refinement and sensor parameter refinement. The orbit refinement refers to further correction and optimization of satellite orbit parameters to improve the accuracy of orbit positioning, which includes improving the orbit prediction model, using ground measurement data for orbit correction, and using precise satellite dynamics modeling. Through orbit refinement, the positioning error of the satellite on the orbit can be reduced, thereby improving the geographic positioning accuracy of remote sensing images.

[0011] The attitude refinement refers to the accurate estimation and correction of the attitude parameters of the satellite in space to ensure the accuracy of the sensor's observation direction, which includes the use of on-board attitude sensor data for attitude solution, parameter calibration of the attitude control system, and attitude correction using inter-satellite measurement technology. Through attitude refinement, the influence of attitude errors on the geometric positioning of remote sensing images can be reduced, and the geographic positioning accuracy of images can be improved.

[0012] The sensor parameter refinement refers to the precise calibration and estimation of the internal and external parameters of the remote sensing sensor to improve the accuracy and consistency of the sensor observation data, which includes precise calibration of the sensor's camera internal parameters, external parameters, distortion parameters, etc., sensor calibration using calibration plates or special scenes, and sensor external calibration using image registration technology. Through sensor parameter refinement, the sensor system error can be eliminated and the geometric positioning accuracy of remote sensing images can be improved.

[0013] Multimodal remote sensing makes full use of the advantages of high-precision angle measurement of optical images, high-precision distance measurement of SAR and lidar, and builds a robust air-space ranging and angle measurement triangulation network through the relationship between multi-source remote sensing. It also improves the accuracy of remote sensing orbit, attitude and sensor parameters through refinement methods, thereby achieving high-precision positioning of the target. Summary of the invention

[0014] The present invention provides a method for refining a regional network for multi-modal space remote sensing collaborative surveying and mapping, which solves the problem of realizing multi-modal space remote sensing collaboration by using high-precision angle measurement of optical images, high-precision distance measurement of SAR and lidar, and the problem of improving the accuracy of orbit refinement, attitude refinement and sensor parameter refinement of aerospace surveying and mapping remote sensing. The technical solution is as follows: A method for refining a multi-modal air-space remote sensing collaborative mapping regional network comprises the following steps: S1; Construct a rigorous positioning model for aero-optical and SAR images in a geocentric rectangular coordinate system, including the collinearity equation for aero-optical images and the range-coplanarity model for aero-SAR images; S2; extract the rational polynomial model parameters RPC of satellite optical and SAR images in the geocentric rectangular coordinate system according to the satellite optical RFM geometric model and the satellite SARRFM geometric model; S3: Construct the positioning model and distance equation of satellite laser altimetry remote sensing targets in the geocentric rectangular coordinate system, including the laser altimetry positioning model and the laser altimetry distance equation; S4: For the spot image of satellite laser, the position of the spot energy center on the spot image is extracted by fitting method, and the clarity of the spot image is increased by spot suppression and image enhancement methods; S5: Matching of homonymous points of multimodal remote sensing targets is achieved through image gradient feature matching, extraction of homonymous points between optical images is achieved through SIFT features or least squares matching, and matching between laser altimetry data and remote sensing images is achieved through extraction of spot energy center position and template matching; S6: Based on Lidar DSM data, matching of Lidar and SAR images and extraction of same-name points are achieved indirectly through SAR simulated image matching, or through template matching between Lidar DSM and InSAR DSM when InSAR DSM is available; S7: constructing a multi-modal remote sensing regional network joint adjustment model under a unified geocentric rectangular coordinate system benchmark, wherein the multi-modal remote sensing control network joint adjustment model is composed of an error equation group established by a multi-modal remote sensing data model; S8: Reduce the number of unknowns through error equation formalization and ground point coordinate modification, and solve the refined parameters and encrypted point coordinates as a whole.

[0015] Furthermore, in step S1, in the geocentric rectangular coordinate system, the rigorous model of the aerial optical image is based on the POS and attitude data, and the colinearity equation of the aerial optical image in the geocentric rectangular coordinate system is constructed through the conversion matrix between the section space rectangular coordinate system and the geocentric rectangular coordinate system; in the geocentric rectangular coordinate system, the rigorous model of the aerial SAR image is for the aerial SAR image, and the distance-coplanarity model of the aerial SAR image is constructed based on the position, velocity and attitude information of the SAR sensor in the terrain rectangular coordinate system.

[0016] Further, in step S2, according to the satellite optical RFM geometric model and the satellite SARRFM geometric model, the rational polynomial model parameters RPC of the satellite optical and SAR images in the geocentric rectangular coordinate system are extracted, including the following steps: S11: The acquisition of satellite optical image RPC in the geocentric rectangular coordinate system is constructed based on the geometric model of pixel sight vector and the earth ellipsoid model with elevation parameters when the optical satellite image provides the physical model parameters; S12: The acquisition of RPC of satellite SAR images in the geocentric rectangular coordinate system is constructed based on the geometric model of range Doppler (RD) and the earth ellipsoid model with elevation parameters when the satellite SAR image provides the physical model parameters; S13: When satellite optical images and satellite SAR images provide RPC in geographic coordinate system, they are constructed based on the RFM geometric model in geographic coordinate system and the earth ellipsoid model with elevation parameters; S14: Divide the image layer into grids and the elevation layer into elevation intervals, and use the above-mentioned geometric model and the ellipsoid model with elevation parameters to calculate the spatial positioning coordinates of the image point coordinates of each grid point on different elevation layers to form virtual control points in the geocentric rectangular coordinate system, and use these virtual control points to fit the RPC parameters of the satellite image RFM geometric positioning in the geocentric rectangular coordinate system.

[0017] Furthermore, in step S3, the laser altimetry positioning model is a positioning model of a satellite laser altimetry remote sensing target in a geocentric rectangular coordinate system. The positioning model obtains the line of sight vector of the laser beam through the sensor position and attitude, and combines the laser ranging length after atmospheric and tidal correction to construct a laser altimetry remote sensing target positioning model in a geocentric rectangular coordinate system; the distance model is constructed based on the ground point position coordinates and the sensor position coordinates in the geocentric rectangular coordinate system.

[0018] Further, in step S5, the matching of the same-name points of the multimodal remote sensing target is achieved through image gradient feature matching, the extraction of the same-name points between the optical images is achieved through SIFT features or least squares matching, and the matching between the laser altimetry data and the remote sensing image is achieved through spot energy center position extraction and template matching, including the following processing: (1) Extracting the same-name points between optical images through SIFT features or least squares matching. When there are only optical images in the overlapping area, the least squares or SIFT matching method can be used to match the multi-source optical images, thereby extracting the same-name points between the images. (2) The matching of same-name points of multimodal remote sensing targets is achieved through image gradient feature matching. When the overlapping area includes SAR images, the SAR image matching can be achieved through the normalized local directional gradient histogram HOG feature, as well as the matching of multimodal images including SAR images; (3) The matching between laser altimeter data and remote sensing images is achieved through the extraction of the spot energy center position and template matching. The position of the spot energy center on the remote sensing image is obtained through the position of the spot energy center on the spot image. The relationship between the spot image and the remote sensing image is combined to achieve the matching between the laser altimeter data and the remote sensing image, and the extraction of the same-name points between the laser altimeter data and the remote sensing image is achieved.

[0019] Furthermore, in step S6, the simulated image matching refers to using the DSM data of the Lidar to simulate imaging of the DSM data with the same imaging parameters as the SAR image, obtaining the relationship between the simulated SAR image and the real SAR image, and then matching the simulated SAR with the real SAR image, and inverting the positions of the same-name points of the simulated image on the Lidar DSM according to the geometric relationship between the simulated SAR image points and the DSM points, thereby realizing the construction of the relationship between the same-name points of the Lidar data and the SAR image, thereby obtaining the same-name points between the SAR image and the Lidar DSM.

[0020] Furthermore, in step S7, the adjustment model reconstructs different remote sensing geometric models in the geocentric rectangular coordinate system, and for different sensor images, the error equation is established based on different multimodal remote sensing data models: For satellite optical and SAR remote sensing images, the error equations are constructed using the RFM model in the geocentric rectangular coordinate system; For aerial optical images, the error equations are constructed using the collinearity equation model in the geocentric rectangular coordinate system; For aerial SAR images, the error equation is constructed using the range coplanarity equation in the geocentric rectangular coordinate system and the orbit attitude refinement model; For satellite laser altimetry data, the error equation is constructed using the laser point positioning model or distance model in the geocentric rectangular coordinate system; For airborne Lidar data, it is directly used as control data in the space network; For ground control points, they are converted to the geocentric rectangular coordinate system as known values: The model positioning of optical and SAR image RFM adopts the refined model of image-space affine transformation; the sensor position and attitude refined model of image and laser altimetry data rigorous model adopts the quadratic polynomial model of time.

[0021] Furthermore, the sensor position and posture refinement model is as follows:

[0022] in( a i , b i , c i , e i , f i , g i )( i =0,1,2) are the polynomial model coefficients of the sensor position and attitude system error, t is the time parameter, (X0,Y0,Z0) are the initial values ​​of the sensor position, is the initial value of the sensor attitude, (X S ,Y S ,Z S ) is the refined value of the sensor position, It is the refined value of sensor attitude.

[0023] Furthermore, in step S8, before the overall solution of the multimodal remote sensing refined parameters, the error equation constructed by the multimodal remote sensing is normalized and the ground point coordinates are modified. Before solving, the multimodal remote sensing error equation group of the same points with the same name is used as a unit, and the unknown variables of the ground point coordinates are eliminated. After the correction values ​​of each remote sensing orientation parameter are obtained, the ground point coordinates are obtained by intersection calculation to obtain the encrypted point information.

[0024] The multi-modal aerospace remote sensing collaborative mapping regional network refinement method is suitable for the processing of all or part of the above types of remote sensing data such as satellite optics, satellite SAR, satellite laser altimetry, aero-optics, aero-SAR, and aero-Lidar.

[0025] The multi-modal aerospace remote sensing collaborative mapping regional network refinement method provided by the present invention takes into account the main aerospace mapping remote sensing data. In practical applications, not all of the mentioned remote sensing data must be involved at the same time. Any combination of the different types of remote sensing data mentioned can be collaboratively processed in the framework and method designed by the present invention, providing an effective way for the collaborative mapping and processing of multi-modal aerospace remote sensing data. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1It is a schematic diagram of the overall process of the multi-modal aerospace remote sensing collaborative mapping regional network refinement method; Figure 2 It is a working schematic diagram of the multi-modal aerospace remote sensing collaborative mapping regional network refinement method. DETAILED DESCRIPTION

[0027] like Figure 1 and Figure 2 As shown, the multi-modal air-space remote sensing collaborative mapping regional network refinement method comprises the following steps: Step S1: constructing a rigorous positioning model of aero-optical and SAR images in a geocentric rectangular coordinate system, including the collinearity equation of the aero-optical image and the distance-coplanarity model of the aero-SAR image; In the geocentric rectangular coordinate system, the rigorous model of aerial optical images is based on POS and attitude data, and the construction of the collinear equation of aerial optical images in the geocentric rectangular coordinate system is realized through the conversion matrix between the section space rectangular coordinate system and the geocentric rectangular coordinate system. In the geocentric rectangular coordinate system, the rigorous model of aerial SAR images is based on the position, velocity and attitude information of the SAR sensor in the terrain rectangular coordinate system to construct the distance-coplanar model of aerial SAR images.

[0028] Specifically, for aerial optical images, the rectangular coordinate system of the section space refers to the coordinate system where the attitude parameters obtain the POS attitude. According to the transformation matrix between the rectangular coordinate system of the section space and the geocentric rectangular coordinate system R p E , the collinear equation of the aero-optical image in the geocentric rectangular coordinate system can be constructed. Furthermore, the sight vector of the CCD pixel of the satellite sensor in the geocentric rectangular coordinate system is 。 Then, in the geocentric rectangular coordinate system: Let R = R c E R' c E =[ m ij ](i,j=0,1,2),m ij As matrix elements, there are collinear equations for aerial optical images in the geocentric rectangular coordinate system (ECEF):

[0029] In formula (1), (X S ,Y S ,Z S) is the refined value of the sensor position, which is abbreviated as the sensor position later. (X, Y, Z) is the ground target position corresponding to the image point. (x0, y0) is the image principal point coordinate, (x, y) is the image point coordinate, f is the focal length.

[0030] For aerial SAR images, a distance coplanar model is constructed in the geocentric rectangular coordinate system:

[0031] In formula (2), the parameter R0 is the initial slant distance corresponding to the first column of image coordinates of the aerial SAR image, M r is the image slant range resolution of the aerial SAR image, M a is the image azimuth resolution of the aerial SAR image. (X S ,Y S ,Z S ) is the sensor position, and (X, Y, Z) is the ground target position corresponding to the image point. f D is the Doppler parameter, [V Xs ,V Ys ,V Zs ] T is the speed of the sensor, are the attitude parameters in the geocentric rectangular coordinate system, is the wavelength.

[0032] Step S2: extracting the rational polynomial model coefficients RPC of satellite optical and SAR images in the geocentric rectangular coordinate system according to the satellite optical RFM geometric model and the satellite SARRFM geometric model; The geometric processing parameters attached to satellite remote sensing images usually include two categories, one is the satellite ephemeris attitude and sensor imaging parameters, including the physical model parameters provided by satellite optical images and the physical model parameters provided by satellite SAR images, and the other is the RPC parameters in the geographic coordinate system. In view of the differences between the two types of sensor imaging parameters and sensor categories, the present invention classifies and provides a basic model and technical method for geometric positioning of the rational polynomial (RFM) model parameter RPC extraction of satellite optical and SAR images in the geocentric rectangular coordinate system, including the following steps: S11: The acquisition of satellite optical image RPC in the geocentric rectangular coordinate system is based on the geometric model of the sight vector of the CCD pixel of the sensor and the earth ellipsoid model with elevation parameters when the optical satellite image provides the physical model parameters, where: The geometric model of the pixel sight vector is as follows:

[0033] In formula (3), [X, Y, Z] is the position of the ground target corresponding to the image point, [Xs, Ys, Zs] is the sensor position, The three parameters are the conversion matrix from the orbital coordinate system to the geocentric rectangular coordinate system, the conversion matrix from the satellite's body coordinate system to the orbital coordinate system, and the conversion matrix from the sensor coordinate system to the satellite's body coordinate system. is the scale factor, (x,y) is the image point coordinates, f is the focal length.

[0034] The earth ellipsoid model with elevation parameters is as follows:

[0035] In formula (4), H is the ground point elevation parameter, a , b are the major and minor axes of the Earth's ellipsoid.

[0036] Combining the above two equations, we can obtain the coordinates (X, Y, Z) of the ground point corresponding to the image point coordinates in the geocentric rectangular coordinate system.

[0037] S12: The acquisition of RPC of satellite SAR images in the geocentric rectangular coordinate system is based on the range Doppler (RD) geometric model and the earth ellipsoid model with elevation parameters when the satellite SAR image provides physical model parameters. The earth ellipsoid model with elevation parameters is the same as above. The RD model is in the following form:

[0038] In formula (5), the first equation expresses the distance equation. The left side is the distance extracted from the image information. The parameter R0 is the initial slant distance corresponding to the first column of image coordinates of the aerial SAR image. r is the image slant range resolution, and on the right is the sensor position (X S ,Y S ,Z S ) and the spatial distance between the ground target position (X, Y, Z) corresponding to the image point.

[0039] The second equation expresses the Doppler equation, and the left side is the Doppler parameters used for imaging. f D , the right side is based on the speed of the sensor [V Xs ,V Ys ,V Zs ] T , relative position between sensor and target [XX S ,YY S ,ZZ S ] T The Doppler value calculated by the relationship, is the wavelength and R is the slant distance.

[0040] Combining formula (5) with the earth ellipsoid model with elevation parameters, we can obtain the coordinates of the ground point corresponding to the image point coordinates in the geocentric rectangular coordinate system, that is, the image point corresponding to the ground target position (X, Y, Z).

[0041] S13: When satellite optical images and satellite SAR images provide RPC in the geographic coordinate system, they are constructed based on the RFM geometric model in the geographic coordinate system and the earth ellipsoid model with elevation parameters. The form of the RFM model is as follows: When the elevation H is known, the latitude and longitude elevation coordinates (L, B, H) of the ground point in the geographic coordinate system corresponding to the image point coordinates can be calculated, and the geographic coordinates can be converted to the geocentric rectangular coordinate system to obtain the ground target position (X, Y, Z) corresponding to the image point, where L represents longitude and B represents latitude.

[0042] S14: Divide the image layer into grids and the elevation layer into elevation intervals, and use the above-mentioned geometric model and the ellipsoid model with elevation parameters to calculate the spatial positioning coordinates of the image point coordinates of each grid point on different elevation layers to form virtual control points in the geocentric rectangular coordinate system, and use these virtual control points to fit the RPC parameters of the satellite image RFM geometric positioning in the geocentric rectangular coordinate system.

[0043] Step S3: constructing a positioning model and distance equation of a satellite laser altimetry remote sensing target in a geocentric rectangular coordinate system, including a laser altimetry positioning model and a laser altimetry distance equation; The laser altimetry positioning model is a positioning model of a satellite laser altimetry remote sensing target in a geocentric rectangular coordinate system. The positioning model obtains the line of sight vector of the laser beam through the sensor position and attitude, and combines the laser ranging length after atmospheric and tidal correction to construct a laser altimetry remote sensing target positioning model in a geocentric rectangular coordinate system.

[0044] Assuming that the distance after atmospheric correction and system correction of laser ranging is L, the positioning model of the center of the laser height measurement spot in the geocentric rectangular coordinate system is:

[0045] In formula (6), [X, Y, Z] is the position of the ground target corresponding to the image point. They are the transformation matrix from the orbital coordinate system to the geocentric rectangular coordinate system, the transformation matrix from the body coordinate system to the orbital coordinate system, and the transformation matrix from the sensor coordinate system to the body coordinate system. [Xs, Ys, Zs] is the sensor position; The distance model is constructed based on the ground point position coordinates and the sensor position coordinates in the geocentric rectangular coordinate system.

[0046] In the geocentric rectangular coordinate system, the laser height measurement distance equation is:

[0047] In formula (7), [X, Y, Z] is the ground target position corresponding to the image point, and [Xs, Ys, Zs] is the sensor position.

[0048] Step S4: for the spot image of the satellite laser, the position of the spot energy center on the spot image is extracted by a fitting method, and the clarity of the spot image is increased by a spot suppression and image enhancement method; According to the energy corresponding to the two-dimensional plane coordinates of the spot image, the quadratic surface is used as a model to fit the precise coordinates of the spot energy center on the spot image. At the same time, the clarity of the spot base image can be improved by performing wavelet transform denoising, spot suppression, image enhancement and other operations on the spot image, so as to achieve spot image enhancement and provide a basis for high-quality matching between the spot image and the remote sensing image.

[0049] Step S5: matching the same-name points of multimodal remote sensing targets by image gradient feature matching, extracting the same-name points between optical images by SIFT features or least squares matching, and matching the laser altimetry data with the remote sensing images by extracting the center position of the light spot energy and template matching; Among them, the extraction of same-name points between optical images is achieved through SIFT features or least squares matching. When there are only optical images in the overlapping area, the least squares or SIFT matching method can be used to achieve matching between multi-source optical images, thereby realizing the extraction of same-name points in the images.

[0050] The gradient feature matching is mainly based on the normalized local directional gradient histogram HOG feature in the dense grid, and the matching and same-name point extraction between SAR images and heterogeneous remote sensing images containing SAR images are realized through template matching. The same-name point matching of multimodal remote sensing targets is realized through image gradient feature matching, that is, when the overlapping area includes SAR images, SAR image matching and multimodal image matching including SAR images can be realized through the normalized local directional gradient histogram HOG feature.

[0051] The matching between laser altimetry data and remote sensing images is achieved through spot energy center position extraction and template matching. The position of the spot energy center on the remote sensing image is obtained through the position of the spot energy center on the spot image, and the relationship obtained by matching the spot image and the remote sensing image is combined to achieve the matching between the laser altimetry data and the remote sensing image, and realize the extraction of the same-name points between the laser altimetry data and the remote sensing image.

[0052] Step S6: Based on the Lidar DSM data, matching of Lidar and SAR images and extraction of same-name points are indirectly achieved through SAR simulation image matching, or in the case of InSAR DSM, template matching between Lidar DSM and InSAR DSM; The present invention provides two methods for matching airborne Lidar data with SAR image data. One is to use Lidar data to simulate SAR images with real SAR imaging parameters, and to obtain the same-name points of the real SAR and Lidar data by using the matching results between the simulated SAR and the real SAR images and the geometric relationship between the simulated SAR and Lidar DSM; Specifically, the simulated image matching refers to using the DSM data of Lidar to simulate imaging of the DSM data with the same imaging parameters as the SAR image, and using DSM to establish a mapping relationship between the ground coordinate system and the slant range coordinate system to simulate the SAR image, thereby obtaining the relationship between the simulated SAR image and the real SAR image; then, by matching the simulated SAR with the real SAR image, and according to the geometric relationship between the simulated SAR image points and the DSM points, inverting the positions of the same-name points of the simulated image on the LidarDSM, the relationship between the same-name points of the Lidar data and the SAR image is constructed, thereby obtaining the same-name points between the SAR image and the Lidar DSM.

[0053] The other is to obtain the same-name points of Lidar data and SAR images through the matching results between Lidar DSM and InSAR DSM and the geometric relationship between SAR and InSAR DSM.

[0054] Specifically, the matching of Lidar and SAR images and the extraction of homonymous points refer to using the three-dimensional coordinate homonymous points matched between Lidar DSM and InSAR DSM, converting InSAR DSM points to SAR images according to SAR imaging geometry, and extracting homonymous points according to the correspondence between Lidar DSM three-dimensional points and SAR image two-dimensional image points.

[0055] Step S7: Under a unified geocentric rectangular coordinate system, a multimodal remote sensing regional network joint adjustment model is constructed, wherein the multimodal remote sensing control network joint adjustment model is composed of an error equation group formed by a multimodal remote sensing data model; The adjustment model reconstructs different remote sensing geometric models in the geocentric rectangular coordinate system. The geocentric rectangular coordinate system not only provides a unified benchmark support for multi-source remote sensing geometric models, but is also not limited by the adjustment range. For different sensor images, the error equation is constructed based on different multimodal remote sensing data models: for satellite optical and SAR remote sensing images, the error equation is constructed using the RFM model in the geocentric rectangular coordinate system; for aerial optical images, the error equation is constructed using the collinear equation model in the geocentric rectangular coordinate system; for aerial SAR images, the error equation is constructed using the distance coplanarity equation and orbit attitude refinement model in the geocentric rectangular coordinate system; for satellite laser altimetry data, the error equation is constructed using the laser point positioning model or distance model in the geocentric rectangular coordinate system; for airborne Lidar data, the positioning accuracy is usually high and can be directly used as control data in the air-space network; for ground control points, they are converted to the geocentric rectangular coordinate system as known values.

[0056] In the embodiment, different multimodal remote sensing data models are specifically described as follows: For satellite optical and SAR images, the geocentric rectangular coordinate system RFM model extracted in step S1 is used as the basis, and the image-side affine transformation model coefficients are used as unknowns. For different scene images in the long orbit, a set of image-side translation parameters a0, b0 are shared; For aerial optical images, based on the collinear equations of the geocentric rectangular coordinate system extracted in step S2 and the orbit attitude refinement model, the sensor orbit attitude refinement function parameters and sensor refinement parameters are constructed as unknowns; For aerial SAR images, based on the geocentric right angle distance coplanarity equation extracted in step S2 and the orbit attitude refinement model, the sensor orbit attitude refinement function parameters and sensor refinement parameters are constructed as unknowns; For satellite laser altimetry data, the geocentric rectangular coordinate system laser altimetry positioning model and orbit attitude refinement model constructed in step S3 are used as the basis, and the sensor orbit refinement function parameters, the attitude refinement function parameters related to the positioning model, and the sensor refinement parameters are used as unknowns to construct; when the laser altimetry positioning plane accuracy is not high, the distance equation and the sensor position refinement model are used as the basis, and the sensor orbit refinement function parameters and the sensor refinement parameters are used as unknowns to construct; For airborne Lidar data, due to its high accuracy, the extracted points with the same name are usually used as control points and are no longer refined.

[0057] The system parameters to be solved in the joint adjustment model of the multi-modal remote sensing control network are: for the optical and SAR image RFM models, the image-space corrected affine transformation six-parameter model is used; for the sensor position and attitude refinement model of the image and laser altimetry data rigorous model, the low-order polynomial of time is used to express it, which is:

[0058] in( a i , b i , c i , e i , f i , g i )( i =0,1,2) are the polynomial model coefficients of the sensor position and attitude system error, t is the time parameter, (X0,Y0,Z0) are the initial values ​​of the sensor position, is the initial value of the sensor attitude, (X S ,Y S ,Z S ) is the refined value of the sensor position, It is the refined value of sensor attitude.

[0059] The above error equations are constructed based on the same-name points, and all the same-name points construct the error equation group, forming the basis of the overall adjustment model for the collaborative processing of multi-modal remote sensing data.

[0060] Step S8: Reduce the number of unknowns by using the error equation method and the ground point coordinate modification method, and solve the refined parameters and the encrypted point coordinates as a whole.

[0061] In order to reduce the pressure on the size of the normal equation matrix caused by the overall solution of large-scale remote sensing data, the error equation constructed by multimodal remote sensing is normalized and the ground point coordinates are modified before the overall solution of multimodal remote sensing refined parameters. Before solving, the multimodal remote sensing error equation group of the same point with the same name is used as a unit, and the unknown number of the ground point coordinates is eliminated. After solving the correction value of each remote sensing orientation parameter, the ground point coordinates are obtained by intersection calculation and the encrypted point information is obtained. Finally, the result is output.

[0062] The multi-modal aerospace remote sensing collaborative mapping regional network refinement method provided by the present invention takes into account the main aerospace mapping remote sensing data. In practical applications, not all of the mentioned remote sensing data must be involved at the same time. Any combination of the different types of remote sensing data mentioned can be collaboratively processed in the framework and method designed by the present invention, providing an effective way for the collaborative mapping and processing of multi-modal aerospace remote sensing data.

Claims

1. A method for refining a multi-modal air-space remote sensing collaborative mapping regional network, comprising the following steps: S1; Construct a rigorous positioning model for aero-optical and SAR images in a geocentric rectangular coordinate system, including the collinearity equation for aero-optical images and the range-coplanarity model for aero-SAR images; S2; extract the rational polynomial model parameters RPC of satellite optical and SAR images in the geocentric rectangular coordinate system according to the satellite optical RFM geometric model and the satellite SARRFM geometric model; S3: Construct the positioning model and distance equation of satellite laser altimetry remote sensing targets in the geocentric rectangular coordinate system, including the laser altimetry positioning model and the laser altimetry distance equation; S4: For the spot image of satellite laser, the position of the spot energy center on the spot image is extracted by fitting method, and the clarity of the spot image is increased by spot suppression and image enhancement methods; S5: Matching of homonymous points of multimodal remote sensing targets is achieved through image gradient feature matching, extraction of homonymous points between optical images is achieved through SIFT features or least squares matching, and matching between laser altimetry data and remote sensing images is achieved through extraction of spot energy center position and template matching; S6: Based on Lidar DSM data, matching of Lidar and SAR images and extraction of same-name points are achieved indirectly through SAR simulated image matching, or through template matching between Lidar DSM and InSAR DSM when InSAR DSM is available; S7: constructing a multi-modal remote sensing regional network joint adjustment model under a unified geocentric rectangular coordinate system benchmark, wherein the multi-modal remote sensing control network joint adjustment model is composed of an error equation group established by a multi-modal remote sensing data model; S8: Reduce the number of unknowns through error equation formalization and ground point coordinate modification, and solve the refined parameters and encrypted point coordinates as a whole.

2. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S1, in the geocentric rectangular coordinate system, the rigorous model of the aerial optical image is based on the POS and attitude data, and the colinearity equation of the aerial optical image in the geocentric rectangular coordinate system is constructed through the conversion matrix between the section space rectangular coordinate system and the geocentric rectangular coordinate system; in the geocentric rectangular coordinate system, the rigorous model of the aerial SAR image is for the aerial SAR image, and the distance-coplanarity model of the aerial SAR image is constructed based on the position, velocity and attitude information of the SAR sensor in the terrain rectangular coordinate system.

3. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S2, according to the satellite optical RFM geometric model and the satellite SARRFM geometric model, the rational polynomial model parameters RPC of the satellite optical and SAR images in the geocentric rectangular coordinate system are extracted, including the following steps: S11: The acquisition of satellite optical image RPC in the geocentric rectangular coordinate system is constructed based on the geometric model of pixel sight vector and the earth ellipsoid model with elevation parameters when the optical satellite image provides the physical model parameters; S12: The acquisition of RPC of satellite SAR images in the geocentric rectangular coordinate system is constructed based on the geometric model of range Doppler and the earth ellipsoid model with elevation parameters when the satellite SAR image provides the physical model parameters; S13: When satellite optical images and satellite SAR images provide RPC in geographic coordinate system, they are constructed based on the RFM geometric model in geographic coordinate system and the earth ellipsoid model with elevation parameters; S14: Divide the image layer into grids and the elevation layer into elevation intervals, and use the above-mentioned geometric model and the ellipsoid model with elevation parameters to calculate the spatial positioning coordinates of the image point coordinates of each grid point on different elevation layers to form virtual control points in the geocentric rectangular coordinate system, and use these virtual control points to fit the RPC parameters of the satellite image RFM geometric positioning in the geocentric rectangular coordinate system.

4. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S3, the laser altimetry positioning model is a positioning model of a satellite laser altimetry remote sensing target in a geocentric rectangular coordinate system. The positioning model obtains the line of sight vector of the laser beam through the sensor position and attitude, and combines the laser ranging length after atmospheric and tidal correction to construct a laser altimetry remote sensing target positioning model in a geocentric rectangular coordinate system; the distance model is constructed according to the ground point position coordinates and the sensor position coordinates in the geocentric rectangular coordinate system.

5. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S5, the matching of the same-name points of the multimodal remote sensing target is achieved through image gradient feature matching, the extraction of the same-name points between the optical images is achieved through SIFT features or least squares matching, and the matching between the laser altimetry data and the remote sensing image is achieved through spot energy center position extraction and template matching, including the following processing: (1) Extracting the same-name points between optical images through SIFT features or least squares matching. When there are only optical images in the overlapping area, the least squares or SIFT matching method can be used to match the multi-source optical images, thereby extracting the same-name points between the images. (2) The matching of same-name points of multimodal remote sensing targets is achieved through image gradient feature matching. When the overlapping area includes SAR images, the SAR image matching can be achieved through the normalized local directional gradient histogram HOG feature, as well as the matching of multimodal images including SAR images; (3) The matching between laser altimeter data and remote sensing images is achieved through the extraction of the spot energy center position and template matching. The position of the spot energy center on the remote sensing image is obtained through the position of the spot energy center on the spot image. The relationship between the spot image and the remote sensing image is combined to achieve the matching between the laser altimeter data and the remote sensing image, and the extraction of the same-name points between the laser altimeter data and the remote sensing image is achieved.

6. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S6, the simulated image matching refers to using the DSM data of the Lidar to simulate imaging of the DSM data with the same imaging parameters as the SAR image, obtaining the relationship between the simulated SAR image and the real SAR image, and then matching the simulated SAR with the real SAR image, and inverting the positions of the same-name points of the simulated image on the LidarDSM according to the geometric relationship between the simulated SAR image points and the DSM points, thereby realizing the construction of the relationship between the same-name points of the Lidar data and the SAR image, thereby obtaining the same-name points between the SAR image and the Lidar DSM.

7. The method for refining a multi-modal space-air remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S7, the adjustment model reconstructs different remote sensing geometric models in the geocentric rectangular coordinate system. For different sensor images, the error equation is established based on different multimodal remote sensing data models: For satellite optical and SAR remote sensing images, the error equations are constructed using the RFM model in the geocentric rectangular coordinate system; For aerial optical images, the error equations are constructed using the collinearity equation model in the geocentric rectangular coordinate system; For aerial SAR images, the error equation is constructed using the range coplanarity equation in the geocentric rectangular coordinate system and the orbit attitude refinement model; For satellite laser altimetry data, the error equation is constructed using the laser point positioning model or distance model in the geocentric rectangular coordinate system; For airborne Lidar data, it is directly used as control data in the space network; For ground control points, they are converted to the geocentric rectangular coordinate system as known values: The RFM model positioning of optical and SAR images adopts the refined model of image-space affine transformation; the sensor position and attitude refined model of the image and laser altimetry data rigorous model adopts the quadratic polynomial model of time.

8. The method for refining a multi-modal air-space remote sensing collaborative mapping regional network according to claim 7, characterized in that: The sensor position and attitude refinement model is as follows: ， in( a i , b i , c i , e i , f i , g i )( i =0,1,2) are the polynomial model coefficients of the sensor position and attitude system error, t is the time parameter, (X0,Y0,Z0) are the initial values ​​of the sensor position, is the initial value of the sensor attitude, (X S ,Y S ,Z S ) is the refined value of the sensor position, It is the refined value of sensor attitude.

9. The method for refining a multi-modal space-air remote sensing collaborative mapping regional network according to claim 1, characterized in that: In step S8, before the overall solution of the multimodal remote sensing refined parameters, the error equation constructed by the multimodal remote sensing is normalized and the ground point coordinates are modified. Before solving, the multimodal remote sensing error equation group of the same points with the same name is used as a unit, and the unknown variables of the ground point coordinates are eliminated. After the correction values ​​of each remote sensing orientation parameter are obtained, the ground point coordinates are obtained by intersection calculation to obtain the encrypted point information.

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