CN110631555A - Historical image ortho-rectification method based on adjustment of second-order polynomial control-point-free area network - Google Patents
Historical image ortho-rectification method based on adjustment of second-order polynomial control-point-free area network Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention discloses a second-order polynomial control point-free block adjustment-based correction method. The problem of the manufacturing of an orthoscopic product of (historical) images lacking imaging parameters is solved. 1. Adequate research and application of (historical) images of missing imaging parameters. 2. And measuring enough homonymous points from any two or more images to form encrypted points, and solving the second-order multi-form imaging model coefficient and all encrypted point coordinates of each image by using block adjustment. 3. And realizing the orthorectification of the historical images. By utilizing the method, the orthorectification of the historical image with missing imaging parameters can be realized under the condition of no control point; according to the invention, control points do not need to be acquired on site, the difficulty that historical image control points are difficult to acquire is overcome, the adjustment calculation of the regional network is simple, the residual errors are uniformly distributed, and the precision is higher; the invention improves the correction efficiency and reduces the cost.
Description
Technical Field
The invention relates to the field of photogrammetry and remote sensing, in particular to a control point-free block adjustment theory which is applied to orthorectification of historical images (image imaging parameters are unknown).
Technical Field
Due to research needs, it is necessary to produce orthoscopic imagery products of historical (satellite or aerial) imagery. Since parameters of the historical satellite are unknown, such as internal and external orientation elements and camera lens distortion parameters, methods requiring satellite parameters, such as collinear equations and rational function models, cannot be used to correct the historical image. The polynomial model does not depend on parameters of the satellite, only needs a sufficient amount of control points, and therefore the model is suitable for processing historical images. And the control points cannot be measured due to the change of the landform and the landform of the historical image coverage area, such as karst landform, hills, deserts and other landforms. The distortion of the image in terms of the height difference is important, and therefore the height difference correction is necessary. All unprocessed image products will exhibit image point displacement, i.e., projection difference, due to the ground relief. The existence of positive and negative height differences relative to a certain reference surface causes the straight line displacement between the ground point on the picture and the vertical projection point on the reference surface on the picture. In the method, an elevation term is added to the polynomial model, and a three-dimensional quadratic polynomial model is established so as to correct the image, so that deformation caused by the elevation term is eliminated while deformation influence is eliminated. The method comprises the steps of selecting and adding an encryption point (TP), carrying out block adjustment to obtain the coordinate value of the encryption point, and participating in the image correction process, thus solving the problem of no control point.
Disclosure of Invention
The invention provides an ortho-rectification method based on second-order polynomial non-control-point block adjustment, which aims to solve the problem that historical image ortho-rectification products lack imaging parameters and cannot measure control points are difficult to manufacture.
The method comprises the following specific steps:
1 image correction model selection
The most basic premise is to establish a corresponding effective correction model, so far, the image correction model has produced more kinds, different experts have different classification opinions, and some models are divided into: empirical models and physical models; some are classified into geometric models and physical models. The choice of a correction model depends largely on the projection mode of the satellite sensor, but the general model does not need to take this into account. The model selected at the time of correction may be different for different image products, but must be selected appropriately. Since parameters of the satellite, such as internal and external orientation elements and camera lens distortion parameters, are unknown, methods requiring satellite parameters, such as collinear equations and rational function models, cannot be used to correct the image. The polynomial model does not depend on parameters of the satellite, only needs a sufficient amount of control points, and therefore the model is suitable for processing historical images. Because there is no control point, the invention selects the encryption point manually to improve the correction precision. The second order polynomial model is selected without excessive control points.
2 control point and encryption point selection
Since there are no control points and the GCP cannot be measured in the field, control points can be selected from the historical ortho images. The control point selection mainly comprises the following steps: manual visual acquisition and automatic matching of images. The selection of the control points requires attention to the characteristics of the points, the number of points selected, and the distribution of the points. The obvious characteristic ground features on the image can be used as the selection of control points, such as main road intersections, building corners, river intersections, ground features with irregular natural shapes and the like. However, it is often necessary to select features as control points according to the resolution of the image. The number of GCPs to be used with the polynomial model is chosen to be (n +1) (n +2)/2, where n is the degree of the polynomial and 6 quadratic polynomials are required. However, the effect of correcting the image according to the number of control points selected by the principle is not the best, so that under the adjustment model of the local area network, the TP can be added, which is generally 2 to 6 times of the required GCP point number. The encryption points are automatically generated and then manually adjusted, the distribution of the control points and the TP needs to cover the whole image area as much as possible, the control points need to be distributed uniformly, and the image edge needs to have enough control points.
Because the block adjustment model can accurately calculate the coordinate values of the encrypted points according to the existing control point coordinates. The selected area network adjustment based on the polynomial orthorectification belongs to the air belt method area network adjustment in nature. The aerial tape method area network adjustment model regards GCP as an observed value, and each nonlinear correction parameter is solved through integral adjustment. According to the correction model used herein, i.e. a quadratic polynomial orthorectification model.
And 3, performing orthorectification correction on the image after determining an image second-order polynomial correction model and TP coordinates by using the block adjustment and the image orthorectification second-order polynomial block adjustment. In the middle of the process from the input image to the output image, the complete output image, i.e. the corrected image, can be obtained only by implementing the processing of spatial transformation and gray level resampling.
Drawings
FIG. 1 is a view showing a projected differential view caused by the ground relief in vertical photography
Fig. 2 is a schematic diagram of a DISP image control point method corresponding to google earth.
Fig. 3 is a schematic diagram of zonal relief.
FIG. 4 is a graph comparing 107-a the original image and the corrected result (a is the original image and b is the corrected image) by using the encryption point and the control point.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It is to be understood that the embodiments described are only some of the embodiments of the invention, and not all of them. All other embodiments, which can be made by those skilled in the art without any inventive presupposition based on the embodiments of the present invention, belong to the scope of the present invention.
Example (b):
in this embodiment, the image of the GRONA satellite system is selected and selected, the coverage area is the image of the Guangxi karst area, and the image shooting year is between 1962 and 1969. The imaging mode is panoramic projection. CORONA belongs to keyhole 4(KH-1-4) series, and the selected image of Mision 1035-1 belongs to KH-4A series, and has resolution of 2.7m and imaging width of 17X231km2The satellite height is 185km., and the satellite images of other missions belong to KH-4B series, the resolution is 1.8m, and the imaging width is 13.8X188km2The satellite height is 150 km.
Step 1: the geometric correction can be directly carried out on the image by solving the conversion parameters through the adjustment principle. Commonly used polynomial models are two-dimensional polynomial models and three-dimensional polynomial models. Since the coverage area of the selected GRONA image belongs to karst landform, hills, hilly and cluster depression and the like, the deformation of the image in the aspect of height difference is in an important position, and therefore height difference correction is required. All unprocessed image products exhibit a displacement of image points, i.e. a difference in projection, due to the relief of the ground, as shown in fig. 1.
When vertically photographing stripsIn the case of the outer orientation element, when the element value is 0 in the angular direction, the dot displacement due to the topographic relief is (Z · d)/H. Z is the height difference, namely elevation, between the ground point corresponding to the image point and the reference surface; h is the height of the platform relative to the reference surface; d is the distance from the image point to the image base point. The projection difference Δ z can be divided into x, y displacement components in two directions as follows: Δ x ═ x · Z)/M, Δ y ═ y · Z)/M. And x and y are coordinates of image points. Since the GRONA satellite imager belongs to the push-broom imager, x is 0, so that
I.e. projection differences are only generated in the y-direction. And the displacement of the image point in the y direction is: Δ y is (y · Z)/H. The idea of the polynomial orthorectification model is to add the correction of projection errors caused by the topography difference on the basis of the original model. The projection difference correction formula is that Deltax is (x.Z)/M, and Delay is (y.Z)/M. x and y are image point coordinates corresponding to the ground points; and delta x and delta y are image point displacements in the x and y directions caused by terrain relief, Z is the height difference between the ground point corresponding to the image point and the reference surface, and M is the satellite flight height. For a push-broom imager, since x is 0 and thus Δ x is 0, only y-direction is the presence of a dot shift, i.e. the projection difference occurs only in the scan direction, the polynomial orthorectification formula can be implemented as:
β＝(β0,β1,β2,β3,β4,β5)Tand α ═ α (α)0,α1,α2,α3,α4,α5)TIs a parameter to be determined; x and Y are ground coordinates; x, y are image plane coordinates.
The method uses a three-dimensional quadratic polynomial model to perform orthorectification on images in sixties of Guangxi, and adopts block adjustment to solve the encryption points.
Step 2: and selecting a control point. Since DISP images do not conform to a reference image or map of the same era at a long time, some problems are encountered in selecting a control point. The Google earth image is used to select control points for the 60 s image, as shown in FIG. 2.
And point selection can be aided by purchasing a 60's photo of the coverage area portion. In order to obtain a more accurate corrected image, the GCP must be uniformly distributed over the image. Moreover, the correction model chosen here is a quadratic polynomial, so the minimum requirement is to choose 6 points.
And step 3: and adding an encryption point. For higher accuracy requirements, the number of control points may be much larger than required, up to 30-40 points. However, due to the above-mentioned limitations, the number of control points often cannot meet the basic requirements of the calibration. And under the condition that the control points are insufficient, the encryption points are selected and added, the adjustment of the area network is carried out to obtain the coordinate values of the encryption points, and the coordinate values participate in the image correction process, so that the problem of insufficient control points can be solved. To ensure sufficient point locations, encryption points are added to the image group and the point locations are uniformly distributed. The 16 control points and the 25 encryption points are uniformly distributed, the encryption points are selected in the image overlapping area as much as possible, at least three encryption points or control points are ensured in the overlapping area, and 6 point positions are ensured in each scene image. In the adjustment process, 16 control points participate in 53 times of operation, 25 encryption points participate in 84 times of operation, 274 condition equations can be formed, the necessary observation number is 267, and the redundant observation number is 7, which all meet the adjustment requirement.
And 4, step 4: and establishing a block adjustment model. The quadratic polynomial orthorectification model has the following specific equation of block adjustment error:
wherein: h is M/(M + Z), lx＝x-[α0+α1X+α2Y+α3XY+α4X2+α5Y2]，ly＝y-[β0+β1X+β2Y+β3XY+β4X2+β5Y2-y·Z/M]。
For known plateau points in the above formula, Δ x, Δ y, Δ z are not present; for a known plane point, Δ x, Δ y are not present; for known elevation points, Δ z is not present; since Z is the point elevation value inserted by DEM, it is known, and M is the satellite orbital height (known), h is a known value. Since the elevation values are derived from interpolation, then are known, and Δ z is not present.
And 5: a polynomial orthorectification based block adjustment method.
And calculating the image point coordinates of the encrypted points according to a small number of control points by using a block adjustment method based on polynomial orthorectification to obtain polynomial model coefficients of all images in the block and the ground coordinates of the encrypted points. The invention uses a quadratic polynomial orthorectification model with the addition of altitude difference correction and introduces an encryption point. And obtaining the coordinate value of the encrypted point through block adjustment, and then correcting each scene image. Due to the large number of images, the DISP image is divided into many areas for the block adjustment. As shown in FIG. 3, the 9 scenes 106a-110a in Session 1106-2 and 143c-146c in Session 1135-1 are selected for block adjustment. At least 6 points of coordinate values are required for each scene to calculate 12 correction parameters in the correction model for quadratic polynomial correction.
In the specific operation, the calculation of the initial value and the adjustment, the image correction coefficient and the ground coordinates of the encryption point are required, and the process is as follows:
step 6: a unified coordinate system. And solving conversion coefficients by utilizing a polynomial correction model through encryption points between adjacent images, and unifying all image point coordinates into a reference coordinate system. The image 106-a is used as a reference to go down to 110-a, and the image coordinates are unified from left to right. A first order polynomial is used to calculate the conversion coefficient of the image coordinates between adjacent pictures. For example, by establishing a polynomial conversion model between coordinate points of the same-name images in the overlapping region between the images 106-a and 107-a, calculating a conversion parameter alpha between the image points1，α2，α3The coordinates of the image points in the image 107-a are then transformed into a coordinate system based on the image 106-a. By analogy, unifying the image coordinate system of the 9-scene images.
And 7: and solving the initial value of the ground coordinate of the encryption point. And solving the plane coordinate initial value of the encryption point by using the solved conversion coefficient. To obtain the initial value of the encryption point, first, a conversion parameter between the plane coordinate and the image coordinate (after being unified) needs to be obtained.
And 8: and acquiring the elevation value of the encrypted point as a known value. And acquiring an elevation value from the DEM as an elevation value according to the plane coordinate of the encryption point.
And step 9: and solving the initial value of the orthorectification coefficient of each image. Solving the initial value alpha of the conversion parameter according to the initial values of the coordinates of the GCP point and the encryption pointi,βi(i＝0,…,5)。
And step 9: and solving the image orthorectification coefficient and the ground coordinates of the encryption point. And solving the correction value of the orthorectification coefficient of each image and the correction value of the ground coordinate of the encryption point according to the block adjustment, and then solving the correction coefficient and the coordinate of the encryption point by using an approach calculation method.
Step 10: and (5) performing adjustment processing data of the area network. The plane point coordinate system adopts a reference plane WGS-84 and a projection UTM-49N. The image in fig. 3 is 48N, but for uniformity, 49N is used for all corrected projections. After the coordinates of the encrypted points are obtained, the conversion parameter alpha of each scene image is calculated by combining the used control pointsij,βij(i is 0, …, 5; j is 1, …, 9), and then image correction is performed by using a polynomial orthorectification formula, for example, as shown in fig. 4, where the corrected image is 107-a.
Claims (4)
1. A second-order polynomial control point-free block adjustment-based ortho-rectification method is characterized by comprising the following specific steps:
1) selecting an image correction model: because various parameters of the satellite are unknown, such as internal and external orientation elements and camera lens distortion parameters, a universal second-order polynomial model is selected;
2) selecting a control point and an encryption point: since there is no control point and the GCP cannot be measured in the field, the control point can be selected from the historical ortho-image;
3) adjustment of the area network and image orthorectification: after the second-order polynomial area network adjustment determines the second-order polynomial correction model of the image and the TP coordinates, the orthorectification correction of the image can be carried out; in the middle of the process from the input image to the output image, the complete output image, i.e. the corrected image, can be obtained only by implementing the processing of spatial transformation and gray level resampling.
2. The method according to claim 1, wherein the step 1) is specifically:
selecting an image correction model: in the process of correcting historical images, strict imaging parameters of the satellite cannot be acquired; methods requiring satellite parameters such as the collinearity equation cannot be used; the polynomial model does not depend on various parameters of the satellite, and only needs a sufficient amount of control points, so that the model is suitable for processing the historical images; because no control point exists, the invention manually selects the encryption point and selects the second-order polynomial model without excessive control points in order to improve the correction precision.
3. The method according to claim 1, wherein the step 2) is specifically:
(1) the control point selection mainly comprises the following steps: the method comprises the steps of manually and visually acquiring and automatically matching images, wherein the selection of control points needs to pay attention to the characteristics of points, the number of selected points and the distribution of the points, obvious characteristic ground objects on images can be used as the selection of the control points, such as main road intersections, building corners, river intersections, natural irregular ground objects and the like, but the ground objects used as the control points are often selected according to the resolution of the images, and when a polynomial model is used, the selection principle of the number of GCPs is (n +1) (n +2)/2, wherein n is the degree of the polynomial, and 6 quadratic polynomials are needed;
(2) addition of encryption points: however, the effect of correcting the image by the number of control points selected according to the principle is not the best, so under the adjustment model of the area network, TP can be added, which is generally 2 times to 6 times the number of required GCP points, the encrypted points are automatically generated, and then fine adjustment is performed manually, the distribution of the control points and TP needs to cover the whole image area as much as possible, and the control points need to be distributed uniformly, and the image edge needs to have enough control points.
4. The method according to claim 1, wherein the step 3) is specifically:
the block adjustment model can accurately calculate the coordinate value of the encryption point according to the existing control point coordinate, and the block adjustment selected based on the polynomial orthorectification belongs to the air belt regional block adjustment essentially, the air belt regional block adjustment model regards GCP as an observed value, each nonlinear correction parameter is solved through integral adjustment, and the correction model used in the text, namely the quadratic polynomial orthorectification model, is adopted.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112082576A (en) * | 2020-09-09 | 2020-12-15 | 桂林理工大学 | Satellite image orthorectification method under 'three-condition-free' condition |
| CN112924465A (en) * | 2021-02-02 | 2021-06-08 | 长江空间信息技术工程有限公司(武汉) | Underground well chamber defect detection method and corresponding device thereof |
| CN116503756A (en) * | 2023-05-25 | 2023-07-28 | 数字太空(北京)科技股份公司 | Method for establishing surface texture reference surface based on ground control point database |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738605A (en) * | 2009-12-17 | 2010-06-16 | 中国测绘科学研究院 | SAR (synthetic aperture radar) image polynomial orthorectification method |
| CN101814102A (en) * | 2010-03-16 | 2010-08-25 | 中交第二公路勘察设计研究院有限公司 | Road survey and design method based on high-resolution satellite image |
| CN108596153A (en) * | 2018-05-10 | 2018-09-28 | 四川省冶地工程勘察设计有限公司 | Remote sensing image defends piece vertical control point extraction method and data processing method |
-
2019
- 2019-08-27 CN CN201910796639.8A patent/CN110631555A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101738605A (en) * | 2009-12-17 | 2010-06-16 | 中国测绘科学研究院 | SAR (synthetic aperture radar) image polynomial orthorectification method |
| CN101814102A (en) * | 2010-03-16 | 2010-08-25 | 中交第二公路勘察设计研究院有限公司 | Road survey and design method based on high-resolution satellite image |
| CN108596153A (en) * | 2018-05-10 | 2018-09-28 | 四川省冶地工程勘察设计有限公司 | Remote sensing image defends piece vertical control point extraction method and data processing method |
Non-Patent Citations (2)
| Title |
|---|
| 程建新，王树文: ""基于规划需求的遥感影像正射纠正处理方法探讨与研究"", 《科技创新导报》 * |
| 黄国满等: ""基于多项式正射纠正模型的机载SAR 影像区域网平差"", 《武汉大学学报•信息科学版》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112082576A (en) * | 2020-09-09 | 2020-12-15 | 桂林理工大学 | Satellite image orthorectification method under 'three-condition-free' condition |
| CN112924465A (en) * | 2021-02-02 | 2021-06-08 | 长江空间信息技术工程有限公司(武汉) | Underground well chamber defect detection method and corresponding device thereof |
| CN112924465B (en) * | 2021-02-02 | 2023-01-17 | 长江空间信息技术工程有限公司(武汉) | Underground well chamber defect detection method and corresponding device thereof |
| CN116503756A (en) * | 2023-05-25 | 2023-07-28 | 数字太空(北京)科技股份公司 | Method for establishing surface texture reference surface based on ground control point database |
| CN116503756B (en) * | 2023-05-25 | 2024-01-12 | 数字太空(北京)科技股份公司 | Method for establishing surface texture reference surface based on ground control point database |
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