CN107917699B - Method for improving aerial three quality of mountain landform oblique photogrammetry - Google Patents
Method for improving aerial three quality of mountain landform oblique photogrammetry Download PDFInfo
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- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/02—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/40—Correcting position, velocity or attitude
- G01S19/41—Differential correction, e.g. DGPS [differential GPS]
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Abstract
The invention relates to a method for improving the aerial three quality of mountain landform oblique photogrammetry data, which comprises the following steps: 1) determining the absolute flight height of each route according to the ground resolution requirement and the mountainous area fluctuation condition; 2) acquiring tilt image data, GNSS data and IMU data of a mountain area through aerial remote sensing flight; 3) sorting the oblique image data, the GNSS data and the IMU data; 4) the whole empty three and the empty three are firstly partitioned and then merged to obtain corresponding empty three results and empty three reports; 5) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the mountain real-scene three-dimensional modeling to form clear and accurate three-dimensional geographic information data. The method and the device perform rapid post-processing on the oblique photogrammetric data of the mountain landform, quantitatively analyze the quality of aerial survey data products, judge and select an optimized result in time, and solve the problem of sky and sky caused by the mountain landform, so that the working efficiency can be improved, and the loss can be reduced.
Description
Technical Field
The invention relates to a method for improving the air-to-three quality of mountain landform inclined image data, in particular to a method for improving the air-to-three quality of mountain landform inclined photogrammetry.
Background
Performing aerial photography for oblique photogrammetry data of mountain landforms has several problems, including:
1. if the mountainous area has large fluctuation, under the condition of ensuring the same ground resolution, the remote sensing aircraft needs to carry out oblique photography on the air lines with different absolute flight heights, thus easily generating loopholes, changing the image overlapping rate and being not beneficial to sky three; under the condition of ensuring the same absolute navigational height, the obtained oblique images have different ground resolutions, and the same ground object on the adjacent images has different sizes, so that the images are not beneficial to matching;
2. wind with uncertain direction and speed is easily generated in mountainous areas, so that plants on the mountains shake, images are blurred, and feature point extraction and image matching are not facilitated; in addition, wind with uncertain direction and speed in the mountains can cause the aircraft to shake, increase the inclination angle and even possibly cause the aircraft to bump into the mountains or overturn, so that the image inclination angle is increased, the precision is poor and the method is not beneficial to the sky three;
3. the landform in the mountainous area is easy to generate shadows, and the images are different in light and shadow effect and different in color at different time intervals and different in date, so that the matching of the images is not facilitated;
4. the mountainous area is generally covered by large-area plants, the plants belong to objects with low-degree texture and simple geometry, and similar plants are similar on images and are not easy to distinguish, so that the characteristic point extraction and the image matching are not facilitated.
Therefore, for oblique photogrammetry of mountain landforms, the three-dimensional problems are more likely to occur in the mountains than in the flat ground and hills, and if the three-dimensional problems are poor, the problems of faults, distortion, stretching, blurring, uneven colors, holes and the like may occur in the later real-scene three-dimensional modeling, so that the task of operation fails.
Disclosure of Invention
The invention aims to provide a method for improving the aerial triangulation quality of the oblique photogrammetry in the mountainous area, which aims at carrying out aerial triangulation on oblique image data in the mountainous area, quantitatively analyzing the quality of aerial survey data products, judging and selecting an optimized result in time, and solving the aerial triangulation problem caused by the mountainous area, so that the working efficiency can be improved, and the loss can be reduced.
In order to achieve the purpose, the invention has the following technical scheme:
the invention relates to a method for improving the aerial three quality of mountain landform oblique photogrammetry, which comprises the following steps:
1) determining the absolute flight height of each route according to the ground resolution requirement and the mountainous area fluctuation condition;
2) acquiring tilt image data, GNSS data and IMU data of a mountain area through aerial remote sensing flight;
3) sorting the oblique image data, the GNSS data and the IMU data;
4) the whole empty three and the empty three are firstly partitioned and then merged to obtain corresponding empty three results and empty three reports;
5) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the mountain real-scene three-dimensional modeling to form clear and accurate three-dimensional geographic information data.
The absolute flight height of each flight path in the step 1) comprises a plurality of same heights or a plurality of different heights, and the method can process data of the same absolute flight height and data of different absolute flight heights; obtaining the relative altitude and the absolute altitude of each route according to the ground resolution requirement and the mountain area fluctuation condition in the step 1), wherein the relational expression of the relative altitude and the ground resolution is as follows:
in the formula, h is the relative altitude of the flight path, f is the focal length of the camera lens, GSD is the ground resolution, a is the pixel size of the camera CCD array, and f and a are constant values.
Wherein, the relation between the absolute altitude and the relative altitude is as follows:
H＝h+h′
in the formula, H is the absolute flight height of the flight path, H is the relative flight height of the flight path, and H' is the altitude of the mountainous area.
If the fluctuation of the mountainous area is small, namely the height difference inside the mountainous area is small, the relative flight heights are basically the same under the requirement of the same ground resolution, so that all the flight lines are at the same absolute flight height;
if the mountainous area has large fluctuation, namely the height difference inside the mountainous area is large, the flight lines are at different absolute flight heights under the requirement of the same ground resolution;
on the contrary, if all the flight lines are required to be at the same absolute flight height, the data corresponding to the mountainous area with small fluctuation have the same ground resolution; the data corresponding to the mountainous areas with large undulations have different ground resolutions.
Wherein, the oblique image data in the step 2) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 2) comprises longitude, latitude and elevation data, and the IMU data in the step 2) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi);
wherein the finishing in the step 3) is as follows: according to the number of days of remote sensing flight, respectively sorting out positioning and attitude determining data corresponding to the images acquired every day and positioning and attitude determining data corresponding to all the images;
wherein, the whole empty three in the step 4) refers to: performing null processing on all the acquired data; the first blocking and empty three and then merging empty three means: respectively carrying out space-three on the data acquired every day, then combining all the space-three results, and carrying out space-three on the combined data; the whole air separation is carried out under the conditions of different time phases and different inclination angles; the blocking empty three is carried out under the conditions of the same time phase and the same inclination angle, so the blocking empty three is more stable than the integral empty three, and the empty three is carried out again based on the stable empty three result after the blocking empty three is combined, and the result is also more stable than the integral empty three;
the integral three-in-one method and the method for partitioning three-in-one and combining three-in-one comprise SURF feature point detection, SURF feature point description and RANSAC accurate matching, and specifically comprise the following steps:
1) SURF feature point detection: detecting the characteristic points by using the maximum value of a Hessian matrix determinant, and assuming that I is an image, X (X, y) is one point in the image, and the scale is sigma, the Hessian matrix at the point X is as follows:
wherein L isxx(X, σ) is the convolution of the second-order derivative of Gaussian at point X with image I, with the remaining terms having similar meanings;
taking the determinant of the Hessian matrix as the response of the spot at X (X, y, sigma) to accelerate the operation efficiency, the specific formula is as follows:
Det(Happrox)＝DxxDyy-(0.9Dxx)2
calculating a speckle response value of each point in the image to form a response image, judging whether a certain point is a candidate feature point or not by comparing the response values of the certain point in the scale space and the upper and lower scale spaces, and if the response value is larger or smaller than 26 neighborhood values, taking the point as a final candidate feature point and calculating the position and scale parameters of the point;
2) SURF feature point principal direction assignment: firstly, performing Haar wavelet operation, wherein the specific parameters are as follows: 6s is the radius, the characteristic point is the center, the side length is 4s, and Haar wavelet response values of the points in the x and y directions are obtained, wherein s is the space scale; and then carrying out Gaussian weighting operation, wherein the specific parameters are as follows: a fan-shaped sliding window with the opening angle of pi/3 and the step length of 0.2 radian sliding window, and the Haar wavelet response values dx and dy of the images in the window are accumulated to obtain a vector (m)ω,θω)：
Solving a direction with the maximum value in a plurality of directions of the accumulated value of the Haar wavelet response values, and taking the direction as the main direction of the feature point;
3) SURF feature point feature vector generation: and constructing a regular sub-window with the side length of 20s, the feature point as the center, the direction consistent with the main direction of the feature point and the size of 4 multiplied by 4. Processing the image by using a Haar wavelet with the side length of 2 sigma to obtain response values dx and dy in x and y directions, and calculating the response value of each sub-window by using Gaussian weighting to obtain a feature vector of each sub-window:
υsub-window＝[∑dx∑dy∑|dx|∑|dy]
A set of descriptor feature vectors contains 4 × 4 × 4 ═ 64-dimensional feature vectors, and complete information of one feature point can be obtained: spatial scale, coordinates, 64-dimensional vector features;
4) the RANSAC algorithm matches exactly: and matching a right view image according to the characteristic points of the left view image, matching the right view image according to the characteristic points of the right view image, screening, storing the matching point pairs into a new array if the matching can be successful for two times, performing RANSAC model estimation, judging correct matching point pairs, and performing n times of iterative computation to obtain final matching points and a conversion matrix.
Wherein, still include the following step:
performing aerotriangulation on the oblique image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, removing residual errors and gross errors, and when the optimal iteration times are reached, if the aerotriangulation results are not parallel to an X-Y plane, stopping iterative computation and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; continuously performing space-three calculation by taking a feasible space-three result as a new initial value, iteratively calculating position information and attitude information of the oblique image data, removing residual errors and gross errors, observing the number of connection points after each calculation and whether a flight line set is parallel to an X-Y plane after each calculation, selecting the space-three result as a new initial value to continuously perform space-three when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual errors and the gross errors, observing the number of the connection points after each optimization and whether the flight line set is parallel to the X-Y plane after each calculation, and finishing optimization if the GNSS data is not measured by RTK when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane to obtain a space-three result and a space-three report; if the GNSS data adopts RTK measurement, selecting the secondary air-three result as a new initial value to continue air-three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of the optimized connection points and whether the airline set is parallel to the X-Y plane after each calculation, selecting the secondary air-three result as a final result when the number of the connection points reaches the maximum and the airline set is parallel to the X-Y plane, comparing the two secondary air-three final results, and selecting the connection points with more connection points and high precision for the live-action three-dimensional modeling.
The repeated iterative calculation and optimization are used for eliminating residual errors and gross errors, so that the position and the posture of the image are correct, the number of connecting points is sufficient, the number of triangular grids used for modeling is sufficient, and the quality of the three-dimensional model is improved.
Due to the adoption of the technical scheme, the invention has the advantages that:
the method is simple to operate, different strategies can be used for calculation, and then the optimal solution is found from different results, so that the achievement quality is optimal;
2, the method is convenient and quick, can perform quantitative analysis, has high precision, can improve the working efficiency and reduce the repeated work and the work loss;
3, the problem caused by the mountain landform can be solved, and the task failure is avoided.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a hollow three-flow diagram of the present invention;
FIG. 3 is a schematic diagram of the three aerial results of the present invention stored in ray sets not parallel to the X-Y plane;
FIG. 4 is a normal diagram of the hollow three results of the present invention;
in the figure, 1, routes are gathered; 2. collecting two aerial lines;
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to the attached drawings 1-4, the method for improving the aerial three-dimensional quality of the mountain landform oblique photogrammetry of the invention comprises the following steps:
1) determining the absolute flight height of each route according to the ground resolution requirement and the mountainous area fluctuation condition;
2) acquiring tilt image data, GNSS data and IMU data of a mountain area through aerial remote sensing flight;
3) sorting the oblique image data, the GNSS data and the IMU data;
4) the whole empty three and the empty three are firstly partitioned and then merged to obtain corresponding empty three results and empty three reports;
5) and comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for the mountain real-scene three-dimensional modeling to form clear and accurate three-dimensional geographic information data.
The absolute flight height of each flight path in the step 1) comprises a plurality of same heights or a plurality of different heights, and the method can process data of the same absolute flight height and data of different absolute flight heights; obtaining the relative altitude and the absolute altitude of each route according to the ground resolution requirement and the mountain area fluctuation condition in the step 1), wherein the relational expression of the relative altitude and the ground resolution is as follows:
in the formula, h is the relative altitude of the flight path, f is the focal length of the camera lens, GSD is the ground resolution, a is the pixel size of the camera CCD array, and f and a are constant values.
Wherein, the relation between the absolute altitude and the relative altitude is as follows:
H＝h+h′
in the formula, H is the absolute flight height of the flight path, H is the relative flight height of the flight path, and H' is the altitude of the mountainous area.
If the fluctuation of the mountainous area is small, namely the height difference inside the mountainous area is small, the relative flight heights are basically the same under the requirement of the same ground resolution, so that all the flight lines are at the same absolute flight height;
if the mountainous area has large fluctuation, namely the height difference inside the mountainous area is large, the flight lines are at different absolute flight heights under the requirement of the same ground resolution;
on the contrary, if all the flight lines are required to be at the same absolute flight height, the data corresponding to the mountainous area with small fluctuation have the same ground resolution; the data corresponding to the mountainous areas with large undulations have different ground resolutions.
Wherein, the oblique image data in the step 2) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 2) comprises longitude, latitude and elevation, and the IMU data in the step 2) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi);
wherein the finishing in the step 3) is as follows: according to the number of days of remote sensing flight, respectively sorting out positioning and attitude determining data corresponding to the images acquired every day and positioning and attitude determining data corresponding to all the images;
wherein, the whole empty three in the step 4) refers to: performing null processing on all the acquired data; the first blocking and empty three and then merging empty three means: respectively carrying out space-three on the data acquired every day, then combining all the space-three results, and carrying out space-three on the combined data; the whole air separation is carried out under the conditions of different time phases and different inclination angles; the blocking empty three is carried out under the conditions of the same time phase and the same inclination angle, so the blocking empty three is more stable than the integral empty three, and the empty three is carried out again based on the stable empty three result after the blocking empty three is combined, and the result is also more stable than the integral empty three;
the integral three-in-one method and the method for partitioning three-in-one and combining three-in-one comprise SURF feature point detection, SURF feature point description and RANSAC accurate matching, and specifically comprise the following steps:
1) SURF feature point detection: detecting the characteristic points by using the maximum value of a Hessian matrix determinant, and assuming that I is an image, X (X, y) is one point in the image, and the scale is sigma, the Hessian matrix at the point X is as follows:
wherein L isxx(X, σ) is the convolution of the second-order derivative of Gaussian at point X with image I, with the remaining terms having similar meanings;
taking the determinant of the Hessian matrix as the response of the spot at X (X, y, sigma) to accelerate the operation efficiency, the specific formula is as follows:
Det(Happrox)＝DxxDyy-(0.9Dxx)2
calculating a speckle response value of each point in the image to form a response image, judging whether a certain point is a candidate feature point or not by comparing the response values of the certain point in the scale space and the upper and lower scale spaces, and if the response value is larger or smaller than 26 neighborhood values, taking the point as a final candidate feature point and calculating the position and scale parameters of the point;
2) SURF feature point principal direction assignment: firstly, performing Haar wavelet operation, wherein the specific parameters are as follows: 6s is the radius, the characteristic point is the center, the side length is 4s, and Haar wavelet response values of the points in the x and y directions are obtained, wherein s is the space scale; and then carrying out Gaussian weighting operation, wherein the specific parameters are as follows: a fan-shaped sliding window with the opening angle of pi/3 and the step length of 0.2 radian sliding window, and the Haar wavelet response values dx and dy of the images in the window are accumulated to obtain a vector (m)ω,θω)：
Solving a direction with the maximum value in a plurality of directions of the accumulated value of the Haar wavelet response values, and taking the direction as the main direction of the feature point;
3) SURF feature point feature vector generation: and constructing a regular sub-window with the side length of 20s, the feature point as the center, the direction consistent with the main direction of the feature point and the size of 4 multiplied by 4. Processing the image by using a Haar wavelet with the side length of 2 sigma to obtain response values dx and dy in x and y directions, and calculating the response value of each sub-window by using Gaussian weighting to obtain a feature vector of each sub-window:
υsub-window＝[∑dx∑dy∑|dx|∑|dy]
A set of descriptor feature vectors contains 4 × 4 × 4 ═ 64-dimensional feature vectors, and complete information of one feature point can be obtained: spatial scale, coordinates, 64-dimensional vector features;
4) the RANSAC algorithm matches exactly: and matching a right view image according to the characteristic points of the left view image, matching the right view image according to the characteristic points of the right view image, screening, storing the matching point pairs into a new array if the matching can be successful for two times, performing RANSAC model estimation, judging correct matching point pairs, and performing n times of iterative computation to obtain final matching points and a conversion matrix.
Wherein, still include the following step:
performing aerotriangulation on the oblique image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, removing residual errors and gross errors, and when the optimal iteration times are reached, if the aerotriangulation results are not parallel to an X-Y plane, stopping iterative computation and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; continuously performing space-three calculation by taking a feasible space-three result as a new initial value, iteratively calculating position information and attitude information of the oblique image data, removing residual errors and gross errors, observing the number of connection points after each calculation and whether a flight line set is parallel to an X-Y plane after each calculation, selecting the space-three result as a new initial value to continuously perform space-three when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual errors and the gross errors, observing the number of the connection points after each optimization and whether the flight line set is parallel to the X-Y plane after each calculation, and finishing optimization if the GNSS data is not measured by RTK when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane to obtain a space-three result and a space-three report; if the GNSS data adopts RTK measurement, selecting the secondary air-three result as a new initial value to continue air-three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of the optimized connection points and whether the airline set is parallel to the X-Y plane after each calculation, selecting the secondary air-three result as a final result when the number of the connection points reaches the maximum and the airline set is parallel to the X-Y plane, comparing the two secondary air-three final results, and selecting the connection points with more connection points and high precision for the live-action three-dimensional modeling.
Wherein the optimal number of iterations is 6-10.
The repeated iterative calculation and optimization are used for eliminating residual errors and gross errors, so that the position and the posture of the image are correct, the number of connecting points is sufficient, the number of triangular grids used for modeling is sufficient, and the quality of the three-dimensional model is improved.
Table 1 is a null three-precision report table of the embodiment of the present invention:
as can be seen from the above table, the accuracy of the space triplet is basically unchanged and tends to be stable, the number of the connecting points can be seen to be the maximum, at this time, whether the flight line set is parallel to the X-Y plane needs to be observed, and if so, the space triplet is normally ended.
Absolute height: refers to the vertical distance of an aircraft above the ground or sea surface from the surface of the earth or sea, also known as the "altitude". I.e. the vertical distance from the standard atmospheric sea level, or the average sea level as the standard height. The heights of the terrain and the ground objects marked on the navigation map are calculated according to the absolute height.
Relative height: the aircraft is a vertical distance above a specified location.
SURF: the method is called Speed-up robust features, namely an accelerated robustness feature algorithm, and is a high-robustness local feature point detector. The algorithm can be used for object recognition or three-dimensional reconstruction in the field of computer vision.
RANSAC: the method is an abbreviation of Random Sample Consensus, and is an algorithm for calculating mathematical model parameters of data according to a group of Sample data sets containing abnormal data to obtain effective Sample data. The RANSAC algorithm is often used in computer vision. For example, the matching point problem of a pair of cameras and the calculation of a fundamental matrix are simultaneously solved in the field of stereoscopic vision.
X-Y plane: is a plane parallel to the mean sea level.
And (4) route collection: is a collection of several oblique images.
Oblique photogrammetry: the oblique photogrammetry technology is a high and new technology developed in recent years in the international surveying and mapping field, which overturns the limitation that the prior orthoimage can only be shot from a vertical angle, and introduces a user into a real and intuitive world which accords with human vision by carrying a plurality of sensors on the same flight platform and acquiring images from five different angles of one vertical angle, four oblique angles and the like. The aerial oblique image not only can truly reflect the ground object condition, but also embeds accurate geographic information, richer image information and higher user experience by adopting an advanced positioning technology, thereby greatly expanding the application field of the remote sensing image and leading the industry application of the remote sensing image to be deeper. Because the oblique images provide richer geographic information for users and more friendly user experience, the technology is widely applied to the industries of emergency command, homeland security, city management, house tax and the like in developed countries such as Europe and America.
And (3) real scene three-dimensional modeling: it is meant that a high resolution, three-dimensional model with realistic texture maps is automatically generated from a series of two-dimensional photographs, or a set of oblique images. If the oblique shots carry coordinate information, the model's geographic location information is also accurate. The model has vivid effect, comprehensive elements and measurement precision, brings people with personally on-the-scene feeling, can be used for metrology application and is a real reduction of the real world.
An IMU: the inertial measurement unit is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object;
GNSS: i.e. the abbreviation of Global Navigation Satellite System. Beginning in the middle of the 90 s of the 20 th century, the european union has been dedicated to the Global Navigation Satellite System project called Global Navigation Satellite System in order to break the monopoly of the united states in the Satellite positioning, Navigation and time service markets, obtain great market benefits, and increase employment opportunities of european people. The plan is implemented in two steps: the first step is to establish a first generation global navigation satellite system (now called GNSS-1, the later established EGNOS) that makes use of the united states GPS system and russian GLONASS system; the second step is to establish a second generation global navigation satellite system, i.e. the built Galileo satellite navigation positioning system, which is completely independent of the GPS system in the united states and the GLONASS system in russia. Therefore, the GNSS is not a single constellation system but a comprehensive constellation system including GPS, GLONASS and the like as soon as being published; differential GNSS refers to a technique for reducing the positioning error of the GPS system or the GLONASS system by utilizing additional data from a reference GNSS receiver whose position is known.
And (3) combining navigation data: the integrated navigation data is the combined navigation data of satellite navigation data (GNSS data) and inertial navigation data (IMU data), and comprises the position information and the attitude information of a target object.
Connection points (Tiepoints): in the overlapping range of the stereopair, the image construction points of the same object point on different images are called homonymous image points, and a large number of automatically or manually generated homonymous image points are collectively called connection points.
Image matching: the process of identifying the same name point between two or more images through a certain matching algorithm.
And (3) air separation: the aerial triangulation is a measuring method for encrypting control points indoors according to a small number of field control points to obtain the elevation and the plane position of the encrypted points in the stereo photogrammetry. The main purpose of the method is to provide absolutely directional control points for mapping regions lacking field control points. The aerial triangulation is generally divided into two types, namely simulated aerial triangulation, namely optical mechanical aerial triangulation; and resolving the aerial triangulation, namely commonly called computerised encryption. The simulated aerial triangulation is aerial triangulation performed on an all-purpose stereo measurement instrument (such as a multiplier). It recovers the space model similar to or corresponding to the shooting course on the instrument, selects the encrypted points according to the mapping requirement, and determines the elevation and plane position. The method for encrypting the control point indoors utilizes the intrinsic geometric characteristics of the photo in the aerial photogrammetry. The method is characterized in that aerial camera films which are continuously shot and have certain overlap are utilized, and a corresponding flight path model or area network model (optical or digital) on the same site is established by a photogrammetry method according to a small number of field control points, so that the plane coordinates and the elevation of an encrypted point are obtained. The method is mainly used for measuring the topographic map.
RTK: namely a real-time dynamic differential method. The method is a new common GPS measurement method, the former static, rapid static and dynamic measurements all need to be solved afterwards to obtain centimeter-level precision, the RTK is a measurement method capable of obtaining centimeter-level positioning precision in real time in the field, a carrier phase dynamic real-time difference method is adopted, the method is a major milestone of GPS application, the appearance of the method is project lofting and terrain mapping, new eosin is brought to various control measurements, and the field operation efficiency is greatly improved. The RTK positioning technology is a real-time dynamic positioning technology based on a carrier phase observation value, and can provide a three-dimensional positioning result of a measuring station in a specified coordinate system in real time and achieve centimeter-level precision. In the RTK mode of operation, the base station transmits its observations to the rover station along with the coordinate information of the rover station via the data chain. The rover station not only receives data from the reference station through a data chain, but also acquires GPS observation data, forms differential observation values in the system for real-time processing, and simultaneously gives centimeter-level positioning results for less than one second. The rover can be in a static state and a moving state; the method can be used for initializing on a fixed point and then entering dynamic operation, can also be directly started under a dynamic condition, and can complete the searching and solving of the ambiguity of the whole cycle under a dynamic environment. After the unknown number is fixed in the whole week, each epoch can be processed in real time, and the rover can give centimeter-level positioning results at any time as long as the tracking and necessary geometric figures of more than four satellite phase observed values can be kept.
Roll, Pitch, Heading: roll, pitch and yaw angles, are the systems of angular elements commonly employed by Inertial Measurement Units (IMUs) to describe sensor attitude;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. Not all embodiments are exhaustive. All obvious changes and modifications which are obvious to the technical scheme of the invention are covered by the protection scope of the invention.
Claims (3)
1. A method for improving the aerial three quality of mountain landform oblique photogrammetry is characterized by comprising the following steps:
1) determining the absolute flight height of each route according to the ground resolution requirement and the mountainous area fluctuation condition;
the absolute flight height of each route comprises a plurality of same heights or comprises a plurality of different heights; obtaining the relative altitude and the absolute altitude of each route according to the ground resolution requirement and the mountain area fluctuation condition in the step 1), wherein the relational expression of the relative altitude and the ground resolution is as follows:
in the formula, h is the relative altitude of a flight path, f is the focal length of a camera lens, GSD is the ground resolution, a is the pixel size of a camera CCD array, and f and a are constant values;
wherein, the relation between the absolute altitude and the relative altitude is as follows:
H＝h+h′
in the formula, H is the absolute flight height of the flight path, H is the relative flight height of the flight path, and H' is the altitude of the mountainous area;
if the fluctuation of the mountainous area is small, namely the height difference inside the mountainous area is small, the relative flight heights are basically the same under the requirement of the same ground resolution, so that all the flight lines are at the same absolute flight height;
if the mountainous area has large fluctuation, namely the height difference inside the mountainous area is large, the flight lines are at different absolute flight heights under the requirement of the same ground resolution;
on the contrary, if all the flight lines are required to be at the same absolute flight height, the data corresponding to the mountainous area with small fluctuation have the same ground resolution; the data corresponding to the mountainous area with large fluctuation have different ground resolutions;
2) acquiring tilt image data, GNSS data and IMU data of a mountain area through aerial remote sensing flight;
3) sorting the oblique image data, the GNSS data and the IMU data;
4) the whole empty three and the empty three are firstly partitioned and then merged to obtain corresponding empty three results and empty three reports;
the whole empty three in the step 4) refers to: performing null processing on all the acquired data; the first blocking and empty three and then merging empty three means: respectively carrying out space-three on the data acquired every day, then combining all the space-three results, and carrying out space-three on the combined data; the whole air separation is carried out under the conditions of different time phases and different inclination angles; the blocking empty three is carried out under the conditions of the same time phase and the same inclination angle, so the blocking empty three is more stable than the integral empty three, and the empty three is carried out again based on the stable empty three result after the blocking empty three is combined, and the result is also more stable than the integral empty three;
the integral three-in-one method and the method for partitioning three-in-one and combining three-in-one comprise SURF feature point detection, SURF feature point description and RANSAC accurate matching, and specifically comprise the following steps:
(1) SURF feature point detection: detecting the characteristic points by using the maximum value of a Hessian matrix determinant, and assuming that I is an image, X (X, y) is one point in the image, and the scale is sigma, the Hessian matrix at the point X is as follows:
wherein L isxx(X, σ) is the convolution of the second-order derivative of Gaussian at point X with image I, with the remaining terms having similar meanings;
taking the determinant of the Hessian matrix as the response of the spot at X (X, y, sigma) to accelerate the operation efficiency, the specific formula is as follows:
Det(Happrox)＝DxxDyy-(0.9Dxx)2
calculating a speckle response value of each point in the image to form a response image, judging whether a certain point is a candidate feature point or not by comparing the response values of the certain point in the scale space and the upper and lower scale spaces, and if the response value is larger or smaller than 26 neighborhood values, taking the point as a final candidate feature point and calculating the position and scale parameters of the point;
(2) SURF feature point principal direction assignment: firstly, performing Haar wavelet operation, wherein the specific parameters are as follows: 6s is the radius, the characteristic point is the center, the side length is 4s, and Haar wavelet response values of the points in the x and y directions are obtained, wherein s is the space scale; and then carrying out Gaussian weighting operation, wherein the specific parameters are as follows: a fan-shaped sliding window with the opening angle of pi/3 and the step length of 0.2 radian sliding window, and the Haar wavelet response values dx and dy of the images in the window are accumulated to obtain a vector (m)ω,θω)：
Solving a direction with the maximum value in a plurality of directions of the accumulated value of the Haar wavelet response values, and taking the direction as the main direction of the feature point;
(3) SURF feature point feature vector generation: constructing a regular sub-window with the side length of 20s, the center of the feature point, the direction consistent with the main direction of the feature point and the size of 4 multiplied by 4; processing the image by using a Haar wavelet with the side length of 2 sigma to obtain response values dx and dy in x and y directions, and calculating the response value of each sub-window by using Gaussian weighting to obtain a feature vector of each sub-window:
υsub-window＝[∑dx∑dy∑|dx|∑|dy]
A set of descriptor feature vectors contains 4 × 4 × 4 ═ 64-dimensional feature vectors, and complete information of one feature point can be obtained: spatial scale, coordinates, 64-dimensional vector features;
(4) the RANSAC algorithm matches exactly: matching a right view image according to the characteristic points of the left view image, matching the right view image according to the characteristic points of the right view image, screening, storing the matching point pairs into a new array if the matching can be successful for two times, carrying out RANSAC model estimation, judging correct matching point pairs, and carrying out iterative computation for n times to obtain final matching points and a conversion matrix;
5) comparing the empty three results and the empty three reports under different conditions, and selecting the optimal result for mountain real-scene three-dimensional modeling to form clear and accurate three-dimensional geographic information data;
performing aerotriangulation on the oblique image data, the GNSS data and the combined navigation data taking angle elements of Roll (phi), Pitch (theta) and Heading (psi) as initial values of IMU data, respectively performing iterative computation on position information and attitude information of the oblique image data, removing residual errors and gross errors, and when the optimal iteration times are reached, if the aerotriangulation results are not parallel to an X-Y plane, stopping iterative computation and judging that the results fail; if all the aerial sets are parallel to the X-Y plane, the iterative computation is not continued, and the result is judged to be feasible; when the optimal iteration times are not reached, if the three results of the air exist and the aerial sets are not parallel to the X-Y plane, continuously performing iterative computation on position information and posture information of the oblique image data, eliminating residual errors and gross errors until all the aerial sets are parallel to the X-Y plane, and judging that the results are feasible; continuously performing space-three calculation by taking a feasible space-three result as a new initial value, iteratively calculating position information and attitude information of the oblique image data, removing residual errors and gross errors, observing the number of connection points after each calculation and whether a flight line set is parallel to an X-Y plane after each calculation, selecting the space-three result as a new initial value to continuously perform space-three when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane, iteratively optimizing the position information and the attitude information of the oblique image data, removing the residual errors and the gross errors, observing the number of the connection points after each optimization and whether the flight line set is parallel to the X-Y plane after each calculation, and finishing optimization if the GNSS data is not measured by RTK when the number of the connection points reaches the maximum and the flight line set is parallel to the X-Y plane to obtain a space-three result and a space-three report; if the GNSS data adopts RTK measurement, selecting the secondary air-three result as a new initial value to continue air-three, iteratively optimizing the position information and the attitude information of the oblique image data, eliminating residual errors and gross errors, observing the number of the optimized connection points and whether the airline set is parallel to the X-Y plane after each calculation, selecting the secondary air-three result as a final result when the number of the connection points reaches the maximum and the airline set is parallel to the X-Y plane, comparing the two secondary air-three final results, and selecting the connection points with more connection points and high precision for the live-action three-dimensional modeling.
2. The method of claim 1, wherein the method comprises the steps of: the oblique image data in the step 2) comprises color digital image data of five angles of front view, back view, left view, right view and downward view; the GNSS data in the step 2) comprises longitude, latitude and elevation, and the IMU data in the step 2) comprises angle elements Roll (phi), Pitch (theta) and Heading (psi).
3. The method of claim 1, wherein the method comprises the steps of: the finishing in the step 3) is as follows: and respectively sorting out positioning and attitude determining data corresponding to the images acquired every day and positioning and attitude determining data corresponding to all the images according to the number of days of remote sensing flight.
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