Detection method and system for satellite image block adjustment ill-conditioned observation area

Abstract

The invention discloses a satellite image block adjustment ill-conditioned observation area detection method and system, and the method comprises the steps: obtaining a plurality of to-be-detected satellite images in a block, and a plurality of connection points corresponding to the satellite images; aiming at any connection point, obtaining an adjustment parameter variance estimation model of elevation constraint; setting a control image according to a satellite image, constructing an adjustment parameter constraint equation of an error correction model, and further obtaining a control image precision propagation variance estimation model; and on the basis of two error propagation laws, through evaluating the model estimation precision of the error correction model corresponding to different positions on each satellite image, whether there is an ill-conditioned observation area on the corresponding satellite image is detected. According to the method, before block adjustment, the propagation rule of the satellite image in the block is quantitatively expressed through modeling, the satellite image with an observation ill-conditioned area in the block can be detected, and the problems of high labor cost and poor reliability of a traditional post-event manual operation detection method are solved.

Description

Technical Field

[0001] The invention relates to a method and a system for detecting pathological observation areas of satellite image regional network adjustment, and belongs to the technical field of high-precision processing of optical satellite remote sensing images. Background Art

[0002] Satellite image block adjustment is a key technology to correct regional image geometric errors and improve the geometric accuracy of regional satellite images. It is also a necessary step for high-precision processing and application of satellite remote sensing images. Block adjustment technology uses the same-name image points between satellite images as the main observation values, and estimates the adjustment parameters using the geometric constraints of the spatial intersection of same-name rays. With the development of automated digital technologies such as image matching, image matching is currently commonly used to automatically extract connection point observations from the overlapping areas of regional images to construct a regional network. However, due to the possible differences in geometry, radiation, imaging angle, texture, and ground objects between satellite images in the regional network, it is easy to have uneven spatial distribution of connection points, low repetition rate, weak observation conditions of local images in the network, and even broken connections in local areas in the automatic matching network, resulting in the inability to guarantee the consistency of internal geometric accuracy of the block adjustment.

[0003] Due to the complex topological relationship of the images within the regional network, these observation pathological areas are usually buried in the entire regional network and are difficult to find before the regional network adjustment. In practical applications, they mainly rely on the experience of operators. After the regional network adjustment, a large number of manual inspections and accuracy index statistics can be used to find quality problems in the network, but it is difficult to directly locate the image with problems. However, large-scale high-precision remote sensing image data has become the basis for the research and application of intelligent remote sensing big data. In practical applications, the scale of regional networks is getting larger and larger, and the number of images to be adjusted often reaches more than a thousand. The operation method that relies on manual inspection has not only become very difficult but also consumes a lot of labor costs. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method and system for detecting pathological observation areas in satellite image regional network adjustment. Before regional network adjustment, the propagation law of satellite images in the regional network is quantitatively expressed by modeling, and theoretical accuracy abnormal image detection is performed, thereby automatically detecting satellite images with pathological observation areas in the regional network, thereby solving the problems of high labor cost and poor reliability in traditional post-manual detection methods.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention discloses a method for detecting pathological observation areas of satellite image regional network adjustment, comprising:

[0007] Acquire multiple satellite images to be detected in the regional network, and multiple connection points corresponding to the satellite images;

[0008] For any connection point, based on the preset error correction model, a variance estimation model of adjustment parameters with elevation constraints is constructed;

[0009] According to the satellite image, by setting the control image, constructing the adjustment parameter constraint equation of the error correction model;

[0010] According to the adjustment parameter variance estimation model and the adjustment parameter constraint equation, a control image accuracy propagation variance estimation model is obtained;

[0011] According to the control image accuracy propagation variance estimation model, based on the error propagation law, the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images is obtained by calculating the correction coefficient matrix; then based on the error propagation law, by evaluating the model estimation accuracy of the error correction model corresponding to different positions on each satellite image, it is detected whether there is a pathological observation area on the corresponding satellite image.

[0012] Further, the step of obtaining the elevation constrained adjustment parameter variance estimation model includes:

[0013] There are multiple overlapping satellite images in the regional network, so there are multiple image points with the same name corresponding to one connection point;

[0014] For any image point with the same name, the basic adjustment equation is constructed based on the preset error correction model and rational function model, and the spatial intersection constraint equation is obtained through parameter linearization.

[0015] For any connection point, the spatial intersection constraint equation of the connection point is established according to the spatial intersection constraint equation of all corresponding image points with the same name, and then the elevation constraint equation with additional prior accuracy is introduced to obtain the adjustment parameter variance estimation model of elevation constraint.

[0016] Furthermore, the expression of the elevation constraint adjustment parameter variance estimation model is as follows:

[0017] ;

[0018] In the formula, Residual vector representing the spatial intersection constraint equation of the i-th connection point;

[0019] Represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the adjustment parameters;

[0020] Represents the adjustment parameter correction number of the satellite image corresponding to the i-th connection point;

[0021] represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the ground coordinates;

[0022] Represents the object coordinates of the i-th connection point;

[0023] Represents the negative value of the current value vector of the basic adjustment equation of the i-th connection point;

[0024] represents the weight of the spatial intersection constraint equation of the i-th connection point;

[0025] Residual vector representing the elevation constraint equation of the i-th connection point; Indicates the correction number of elevation; Represents the weight of the elevation constraint equation of the i-th connection point.

[0026] Furthermore, the step of constructing the adjustment parameter constraint equation of the error correction model according to the satellite image by setting the control image includes:

[0027] Setting any satellite image in the regional network as a control image, dividing the control image evenly into a plurality of grids, and generating an evenly distributed virtual image point at the center of each grid;

[0028] For any virtual image point, the virtual image point is used as an observation value, the initial propagation accuracy of the control image is controlled, the basic adjustment equation of the virtual image point is constructed, and the adjustment parameter constraint equation of the virtual image point is obtained through parameter linearization;

[0029] According to the adjustment parameter constraint equations of all virtual image points, the adjustment parameter constraint equations corresponding to the control image are obtained.

[0030] Furthermore, the expression of the adjustment parameter constraint equation of the virtual image point is as follows:

[0031] ;

[0032] In the formula, Residual vector representing the adjustment parameter constraint equation of the kth virtual image point;

[0033] Represents the partial derivative matrix of the adjustment parameter constraint equation of the kth virtual image point with respect to the adjustment parameter;

[0034] Indicates the adjustment parameter correction number of the control image corresponding to the kth virtual image point;

[0035] Represents the negative value of the current value vector of the basic adjustment equation of the kth virtual image point;

[0036] Represents the weight matrix of the adjustment parameter constraint equation for the kth virtual image point.

[0037] Furthermore, the expression of the control image accuracy propagation variance estimation model is as follows:

[0038] ;

[0039] , , , ,

[0040] , , ;

[0041] In the formula, Represents the set of residual vectors of each constraint equation; Residual vector representing the spatial intersection constraint equations of all connection points; Residual vector representing the constraint equation of adjustment parameters for all virtual image points; Residual vector representing the additional constraint equation introduced for the object coordinates of the connection points;

[0042] Represents the set of partial derivative matrices of each constraint equation with respect to the adjustment parameters; Represents the partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the adjustment parameters; Represents the partial derivative matrix of the adjustment parameter constraint equations of all virtual image points with respect to the adjustment parameters;

[0043] represents the set of partial derivative matrices of the constraint equations with respect to the ground coordinates; The partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the ground coordinates; Represents the coefficient matrix of the additional constraint equation introduced;

[0044] A collection of negative values ​​representing the current value vectors of various basic adjustment equations; Represents the negative value of the current value vector of the basic adjustment equations of all connection points; Represents the negative value of the current value vector of the basic adjustment equations of all virtual image points;

[0045] Represents the set of weight matrices of each constraint equation; The weight matrix representing the spatial intersection constraint equations of all connection points; The weight matrix representing the constraint equations of adjustment parameters for all virtual image points; Represents the weight matrix of the additional constraint equation introduced; Indicates elevation accuracy;

[0046] Represents the vector of adjustment parameter corrections for all images; A vector of corrections to the object space coordinates of all connection points.

[0047] Furthermore, the expression of the correction factor matrix is:

[0048] ;

[0049] In the formula, represents the correction factor matrix; represents the first transition matrix; represents the second transition matrix; represents the third transition matrix; represents the fourth transition matrix.

[0050] Furthermore, the step of evaluating the model estimation accuracy of the error correction model corresponding to different positions on each satellite image to detect whether there is a pathological observation area on the corresponding satellite image includes:

[0051] Each satellite image to be detected is evenly divided into multiple grids, and a uniformly distributed virtual image point is generated at the center of each grid;

[0052] For any virtual image point, based on the error propagation law, the model covariance matrix of the virtual image point is calculated according to the correction coefficient matrix to obtain the model estimation accuracy of the error correction model of the virtual image point;

[0053] For each satellite image, the model estimation accuracy of the error correction model of all virtual image points included is used to calculate the mean, maximum and root mean square error of the model estimation accuracy;

[0054] In response to a maximum value of the model estimation accuracy of any satellite image being greater than a preset maximum value threshold, it is determined that a pathological observation area exists on the satellite image;

[0055] In response to the difference between the maximum and mean values ​​of the model estimation accuracy of any satellite image being greater than a preset multiple of the root mean square error value, it is determined that a pathological observation area exists on the satellite image.

[0056] Furthermore, the expression of the model covariance matrix of the virtual image point is as follows:

[0057] ;

[0058] In the formula, represents the model covariance matrix of the mth virtual image point; Represents the partial derivative matrix of the error correction model with respect to the adjustment parameters; represents the covariance matrix of the correction numbers;

[0059] The expression of the model estimation accuracy of the error correction model of the virtual image point is as follows:

[0060] ;

[0061] In the formula, represents the model estimation accuracy of the error correction model corresponding to the row direction of the mth virtual image point; represents the model estimation accuracy of the error correction model corresponding to the column direction of the mth virtual image point; Represents the model covariance matrix The element at row 1 and column 1 above; Show model covariance matrix The element in the last row and last column.

[0062] On the other hand, the present invention discloses a detection system for satellite image regional network adjustment pathological observation areas, which is applicable to the above-mentioned detection method for satellite image regional network adjustment pathological observation areas, comprising:

[0063] A data acquisition module, used to acquire a plurality of satellite images to be detected in a regional network, and a plurality of connection points corresponding to the satellite images;

[0064] The connection point module builds a variance estimation model of adjustment parameters with elevation constraints for any connection point based on the preset error correction model.

[0065] A control image module is used to construct an adjustment parameter constraint equation of the error correction model according to the satellite image by setting the control image;

[0066] A control image accuracy propagation module, used for obtaining a control image accuracy propagation variance estimation model according to the adjustment parameter variance estimation model and the adjustment parameter constraint equation;

[0067] The pathological observation area detection module is used to obtain the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images by calculating the correction coefficient matrix based on the control image accuracy propagation variance estimation model and the error propagation law; and then based on the error propagation law, by evaluating the model estimation accuracy of the error correction model corresponding to different positions on each satellite image, to detect whether there is a pathological observation area on the corresponding satellite image.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] The satellite image regional network adjustment pathological observation area detection method and system of the present invention, before the regional network adjustment, quantitatively expresses the propagation law of satellite images in the regional network through modeling, performs theoretical accuracy abnormal image detection, and then automatically detects the satellite images with pathological observation areas in the regional network, solves the problems of high labor cost and poor reliability in the traditional post-manual detection method, starts from the mathematical essence of regional network adjustment, more fully considers the mutual influence of connection point distribution, geometric conditions of satellite images and error models, and provides scientific guidance for regional network adjustment quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of a method for detecting pathological observation areas of satellite image regional network adjustment provided by Embodiment 1 of the present invention;

[0071] Figure 2 It is a schematic diagram of generating virtual image points provided by Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0072] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0073] Example 1

[0074] This embodiment 1 provides a method for detecting pathological observation areas in satellite image regional network adjustment, including:

[0075] Acquire multiple satellite images to be detected within the regional network and multiple connection points corresponding to the satellite images;

[0076] For any connection point, based on the preset error correction model, a variance estimation model of adjustment parameters with elevation constraints is constructed;

[0077] According to the satellite images, by setting the control images, the adjustment parameter constraint equations of the error correction model are constructed;

[0078] According to the adjustment parameter variance estimation model and the adjustment parameter constraint equation, the control image accuracy propagation variance estimation model is obtained;

[0079] According to the control image accuracy propagation variance estimation model and the error propagation law, the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images is obtained by calculating the correction coefficient matrix; then based on the error propagation law, the model estimation accuracy of the error correction model corresponding to different positions on each satellite image is evaluated to detect whether there is a pathological observation area on the corresponding satellite image.

[0080] The technical concept of the present invention is to quantitatively express the propagation law of satellite images in the regional network through modeling before regional network adjustment, perform theoretical accuracy abnormal image detection, and then automatically detect satellite images with pathological observation areas in the regional network. The problems of high labor cost and poor reliability in traditional post-manual detection methods are solved. Starting from the mathematical essence of regional network adjustment, the mutual influence of connection point distribution, geometric conditions of satellite images and error models is more fully considered, providing scientific guidance for regional network adjustment quality control.

[0081] like Figure 1 As shown, the specific steps are as follows:

[0082] Step 1: Obtain multiple satellite images to be detected within the regional network and multiple connection points corresponding to the satellite images.

[0083] Obtain the satellite images to be detected in the regional network, obtain the connection points through automatic matching of satellite images, and combine the image error correction model to be used in regional network adjustment The error correction model commonly used in regional block adjustment is usually about the image points of satellite images. The translation transformation model or affine transformation model. The commonly used affine transformation models are as follows:

[0084]

[0085] In the formula, represents the row direction error correction model; represents the first model coefficient; represents the second model coefficient; represents the third model coefficient; represents the column direction error correction model; represents the fourth model coefficient; represents the fifth model coefficient; represents the sixth model coefficient;

[0086] in, They are all model coefficients and also the adjustment parameters of each satellite image in the regional block adjustment.

[0087] By introducing the affine transformation model into the image side of the satellite image rational function model, a single-view image imaging model with an additional image side error correction model can be constructed. The model form is as follows:

[0088]

[0089] In the formula, Indicates the pixel row number; represents the row direction error correction model; A rational function model of satellite images representing the direction of image point rows; Indicates the pixel column number; represents the column direction error correction model; Represents the rational function model of satellite images with respect to the direction of image point array;

[0090] Represents image point The latitude of the corresponding ground point in the spatial geodetic coordinate system; Represents image point The longitude of the corresponding ground point in the spatial geodetic coordinate system; Represents image point The elevation of the corresponding ground point in the spatial geodetic coordinate system.

[0091] Step 2: For any connection point, based on the preset error correction model, a variance estimation model of adjustment parameters with elevation constraints is constructed.

[0092] Specifically, the basic adjustment equation is constructed based on the spatial intersection geometric relationship of the tie points, taking the tie points as observation values. Considering that there are multiple overlapping satellite images in a complex regional network, there are multiple image points with the same name corresponding to one tie point.

[0093] For any image point with the same name, the basic adjustment equation is constructed and the spatial intersection constraint equation is obtained through parameter linearization.

[0094] For any connection point, the spatial intersection constraint equation of the connection point is established according to the spatial intersection constraint equation of all corresponding image points with the same name, and then the elevation constraint equation with additional prior accuracy is introduced to obtain the variance estimation model of adjustment parameters with elevation constraint.

[0095] Specifically, assuming that The connection point The image points with the same name are , its basic adjustment equation can be expressed as:

[0096]

[0097] In the formula, Indicates the image point with the same name The corresponding basic adjustment equation of the image row direction; Represents the same image point The corresponding basic adjustment equation in the image column direction.

[0098] Then, the image points with the same name can be obtained by further parameter linearization: The spatial intersection constraint equation is:

[0099]

[0100]

[0101] ,

[0102] In the formula, Represents the same image point The residual vector of the spatial intersection constraint equation; Represents the same image point The adjustment parameter correction number of the satellite image; Indicates The object coordinates of the connection points; Represents the same image point The weights of the spatial intersection constraint equation;

[0103] Represents the same image point The partial derivative matrix of the spatial intersection constraint equation with respect to the adjustment parameters; Represents the same image point The partial derivative matrix of the spatial intersection constraint equation with respect to the ground coordinates; Represents the same image point The negative value of the current value vector of the basic adjustment equation;

[0104] represents the sign of partial derivative; They are all model coefficients of the error correction model and also the adjustment parameters of each satellite image in the regional block adjustment.

[0105] Further, with the common object coordinates To constrain, establish The spatial intersection constraint equation of the connection points is expressed as follows:

[0106]

[0107] In the formula, Residual vector representing the spatial intersection constraint equation of the i-th connection point;

[0108] Represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the adjustment parameters;

[0109] Represents the adjustment parameter correction number of the satellite image corresponding to the i-th connection point;

[0110] represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the ground coordinates;

[0111] Represents the object coordinates of the i-th connection point;

[0112] Represents the negative value of the current value vector of the basic adjustment equation of the i-th connection point;

[0113] Represents the weight of the spatial intersection constraint equation of the i-th connection point.

[0114] However, due to the correlation between the geometric accuracy of the satellite image plane and the elevation direction, the spatial intersection constraint equation of the above connection point cannot accurately express the geometric accuracy transmitted from other images in the network even under good regional network connection conditions, and thus cannot accurately detect pathological observations in the network. To address this problem, the present invention introduces an elevation constraint with additional priori accuracy based on the spatial intersection constraint equation, while overcoming the coupling of the geometric accuracy of the image plane and the elevation direction, and establishes an adjustment parameter variance estimation model of the elevation constraint that can quantitatively describe the accuracy propagation in the regional network, as follows:

[0115]

[0116] In the formula, Residual vector representing the elevation constraint equation of the i-th connection point; Indicates the correction number of elevation; Represents the weight of the elevation constraint equation of the i-th connection point.

[0117] Step 3: Based on the satellite images, by setting the control images, the adjustment parameter constraint equations of the error correction model are constructed.

[0118] In order to detect the pathological observation area in the regional network, the present invention first selects any satellite image in the regional network as the control image, and achieves the effect of the control image by directly adding a certain accuracy constraint to the adjustment parameters in the error correction model of the control image, so that it has a higher prior positioning accuracy, and detects the pathological observations therein by propagating this accuracy in the regional network.

[0119] Specifically, the steps of obtaining the control image accuracy propagation variance estimation model include:

[0120] Set any satellite image in the regional network as the control image, such as Figure 2 As shown in the figure, the control image is evenly divided into multiple grids, and a uniformly distributed virtual image point is generated at the center of each grid. The coordinates of the image point are Since the function of the control image is to provide a high initial propagation accuracy for the regional network, that is, the imaging model of the control image has high accuracy, the error correction model attached to the image side of the rational function model is should be close to 0, that is , then based on this constraint, taking the virtual image point as the observation value, we can get The basic adjustment equation of virtual image points is:

[0121]

[0122] In the formula, Represents the basic adjustment equation of the image row direction corresponding to the kth virtual image point; It represents the basic adjustment equation of the image column direction corresponding to the k-th virtual image point;

[0123] Then the adjustment parameter constraint equation of the error correction model constructed for the kth virtual image point is:

[0124] ;

[0125] ,

[0126] In the formula, Residual vector representing the adjustment parameter constraint equation of the kth virtual image point;

[0127] Represents the partial derivative matrix of the adjustment parameter constraint equation of the kth virtual image point with respect to the adjustment parameter;

[0128] Indicates the adjustment parameter correction number of the control image corresponding to the kth virtual image point;

[0129] Represents the negative value of the current value vector of the basic adjustment equation of the kth virtual image point;

[0130] Represents the weight matrix of the adjustment parameter constraint equation for the kth virtual image point.

[0131] Step 4: Based on the adjustment parameter variance estimation model and the adjustment parameter constraint equation, the propagation variance estimation model for controlling image accuracy is obtained.

[0132] The adjustment parameter variance estimation model of all connection points in the integrated regional network and the adjustment parameter constraint equation of all control images are expressed as follows:

[0133]

[0134] Combining the above equations, we can further get the control image accuracy propagation variance estimation model, which is expressed as follows:

[0135]

[0136] , , , ,

[0137] , ,

[0138] In the formula, Represents the set of residual vectors of each constraint equation; Residual vector representing the spatial intersection constraint equations of all connection points; Residual vector representing the constraint equation of adjustment parameters for all virtual image points; Residual vector representing the additional constraint equation introduced for the object coordinates of the connection points;

[0139] Represents the set of partial derivative matrices of each constraint equation with respect to the adjustment parameters; Represents the partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the adjustment parameters; Represents the partial derivative matrix of the adjustment parameter constraint equations of all virtual image points with respect to the adjustment parameters;

[0140] represents the set of partial derivative matrices of the constraint equations with respect to the ground coordinates; The partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the ground coordinates; Represents the partial derivative matrix of the introduced additional constraint equation with respect to the ground coordinates;

[0141] A collection of negative values ​​representing the current value vectors of various basic adjustment equations; Represents the negative value of the current value vector of the basic adjustment equations of all connection points; Represents the negative value of the current value vector of the basic adjustment equations of all virtual image points;

[0142] Represents the set of weight matrices of each constraint equation; The weight matrix representing the spatial intersection constraint equations of all connection points; The weight matrix representing the constraint equations of adjustment parameters for all virtual image points; Represents the weight matrix of the additional constraint equation introduced; Indicates elevation accuracy;

[0143] Represents the vector of adjustment parameter corrections for all images; A vector of corrections to the object space coordinates of all connection points.

[0144] Step 5: According to the control image accuracy propagation variance estimation model and based on the error propagation law, the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images is obtained by calculating the correction coefficient matrix.

[0145] According to the error propagation law, the correction number The corresponding estimation accuracy is the same as the adjustment parameters in the corresponding error correction model. Therefore, the theoretical accuracy of the adjustment parameters in the error correction model can be obtained by estimating the accuracy of the correction number.

[0146] According to the error propagation law, the expression of the correction factor matrix is ​​as follows:

[0147]

[0148] In the formula, represents the correction factor matrix; represents the first transition matrix; represents the second transition matrix; represents the third transition matrix; represents the fourth transition matrix.

[0149] Further according to the correction number covariance matrix Covariance factor matrix relationship, ,in is the mean error of the observation value, which can be set as a constant according to the matching accuracy of the connection point observation value in the present invention. Since the adjustment parameter and its correction number have the same covariance matrix, its main diagonal elements can be used to represent the theoretical accuracy of the adjustment parameter in the error correction model, and then the theoretical accuracy of the nth adjustment parameter in the error correction model is obtained. The expression is as follows:

[0150]

[0151] In the formula, Represents the theoretical accuracy of the nth adjustment parameter in the error correction model; Represents the correction covariance matrix The element at the nth row and nth column above; represents the mean error of the observation; Represents the cofactor matrix The element at row n and column n above.

[0152] Step 6: Based on the error propagation law, the model estimation accuracy of the error correction model corresponding to different positions on each satellite image is evaluated to detect whether there is a pathological observation area on the corresponding satellite image.

[0153] Whether the observation conditions are pathological can be quantitatively expressed on each satellite image through the accuracy of its error correction model.

[0154] Therefore, in step five, the theoretical accuracy of the adjustment parameters in the error correction model including all images has been obtained, which is further converted into the model estimation accuracy in the error correction model corresponding to each satellite image, which is used to detect pathological observation areas.

[0155] 1. Calculation of model estimation accuracy in the error correction model.

[0156] In order to estimate the accuracy of the adjustment parameters in the error correction model and express the differences in the internal observation conditions of a single satellite image, the same strategy as that of generating virtual image points for the control image is adopted here to generate virtual image points on each satellite image to be detected. The number of virtual image points can be the same as that of the control image. By evaluating the model estimation accuracy of the error correction model corresponding to the virtual image points, the pathological observation area is detected.

[0157] The specific steps are as follows:

[0158] For any satellite image, it is evenly divided into multiple grids, and a uniformly distributed virtual image point is generated at the center of each grid;

[0159] For any virtual image point, based on the error propagation law, the model covariance matrix of each virtual image point on each satellite image is estimated, where the expression of the model covariance matrix of the mth virtual image point is as follows:

[0160]

[0161] In the formula, represents the model covariance matrix of the mth virtual image point; represents the covariance matrix of the correction numbers; Represents the partial derivative matrix of the error correction model with respect to the adjustment parameters, and the expression is as follows:

[0162]

[0163] Finally, the model estimation accuracy of the error correction model corresponding to the mth virtual image point is obtained for:

[0164]

[0165] In the formula, represents the model estimation accuracy of the error correction model corresponding to the row direction of the mth virtual image point; represents the model estimation accuracy of the error correction model corresponding to the column direction of the mth virtual image point; Represents the model covariance matrix The element at row 1 and column 1 above; Show model covariance matrix The element in the last row and last column.

[0166] What needs to be explained here is the model covariance matrix There are only two rows and two columns of elements.

[0167] 2. Pathological observation and detection.

[0168] For each satellite image, according to the model estimation accuracy of the error correction model of all virtual image points contained in it, the corresponding mean, maximum and root mean square error values ​​are counted, and pathological observation detection is carried out at two levels: between images and within images.

[0169] Between images: Since the model estimation accuracy corresponding to weakly connected images is usually significantly different from that of normal connected images, the weakly connected one or more satellite images can be detected by clustering the judgment or accuracy. Here, the maximum value of the model estimation accuracy is used for accuracy clustering. In response to the maximum value of the model estimation accuracy corresponding to any satellite image being greater than the preset maximum value threshold, it is determined that there is a pathological observation area on the satellite image.

[0170] The table of the maximum values ​​of the model estimation accuracy corresponding to the ten satellite images obtained by this embodiment 1 is as follows:

[0171]

[0172] It can be clearly seen that the maximum value of the model estimation accuracy corresponding to the sixth satellite image is significantly different from that of other satellite images. The maximum value threshold can be set to 1 to screen out the sixth satellite image and determine that there is a pathological observation area on the satellite image.

[0173] Inside the image: The difference in accuracy at different locations inside the image is mainly caused by the uneven distribution of connection points. Therefore, the root mean square error and the maximum value are used as indicators for detection. If the difference between the maximum and the mean of the model estimation accuracy corresponding to any satellite image is greater than the preset multiple of the root mean square error value, it is determined that there is a pathological observation area on the satellite image.

[0174] The preset multiple in this embodiment is three times.

[0175] Example 2

[0176] This embodiment 2 provides a detection system for pathological observation areas of satellite image regional network adjustment, which is applicable to the detection method for pathological observation areas of satellite image regional network adjustment of embodiment 1, including:

[0177] A data acquisition module, used to acquire a plurality of satellite images to be detected in a regional network, and a plurality of connection points corresponding to the satellite images;

[0178] The connection point module builds a variance estimation model of adjustment parameters with elevation constraints for any connection point based on the preset error correction model.

[0179] A control image module is used to construct an adjustment parameter constraint equation of the error correction model according to the satellite image by setting the control image;

[0180] A control image accuracy propagation module, used for obtaining a control image accuracy propagation variance estimation model according to the adjustment parameter variance estimation model and the adjustment parameter constraint equation;

[0181] The pathological observation area detection module is used to obtain the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images by calculating the correction coefficient matrix based on the control image accuracy propagation variance estimation model and the error propagation law; and then based on the error propagation law, by evaluating the model estimation accuracy of the error correction model corresponding to different positions on each satellite image, to detect whether there is a pathological observation area on the corresponding satellite image.

[0182] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0183] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0186] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for detecting pathological observation areas in satellite image regional network adjustment, characterized in that: include: Acquire multiple satellite images to be detected in the regional network, and multiple connection points corresponding to the satellite images; For any connection point, based on the preset error correction model, a variance estimation model of adjustment parameters with elevation constraints is constructed; According to the satellite image, by setting the control image, constructing the adjustment parameter constraint equation of the error correction model; According to the adjustment parameter variance estimation model and the adjustment parameter constraint equation, a control image accuracy propagation variance estimation model is obtained; According to the control image accuracy propagation variance estimation model, based on the error propagation law, the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images is obtained by calculating the correction coefficient matrix; Based on the error propagation law, the model estimation accuracy of the error correction model corresponding to different positions on each satellite image is evaluated to detect whether there are pathological observation areas on the corresponding satellite image.

2. The method for detecting sick observation areas of satellite imagery block adjustment according to claim 1, wherein: The step of obtaining the elevation-constrained adjustment parameter variance estimation model comprises: There are multiple overlapping satellite images in the regional network, so there are multiple image points with the same name corresponding to one connection point; For any image point with the same name, the basic adjustment equation is constructed based on the preset error correction model and rational function model, and the spatial intersection constraint equation is obtained through parameter linearization. For any connection point, the spatial intersection constraint equation of the connection point is established according to the spatial intersection constraint equation of all corresponding image points with the same name, and then the elevation constraint equation with additional prior accuracy is introduced to obtain the adjustment parameter variance estimation model of elevation constraint.

3. The method for detecting sick observation areas of satellite imagery block adjustment according to claim 2, characterized in that: The expression of the elevation constraint adjustment parameter variance estimation model is as follows: ； In the formula, Residual vector representing the spatial intersection constraint equation of the i-th connection point; Represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the adjustment parameters; Represents the adjustment parameter correction number of the satellite image corresponding to the i-th connection point; represents the partial derivative matrix of the spatial intersection constraint equation of the i-th connection point with respect to the ground coordinates; Represents the object coordinates of the i-th connection point; Represents the negative value of the current value vector of the basic adjustment equation of the i-th connection point; represents the weight of the spatial intersection constraint equation of the i-th connection point; Residual vector representing the elevation constraint equation of the i-th connection point; Indicates the correction number of elevation; Represents the weight of the elevation constraint equation of the i-th connection point.

4. The method for detecting pathological observation areas of satellite image block adjustment according to claim 1, characterized in that: According to the satellite image, by setting the control image, the step of constructing the adjustment parameter constraint equation of the error correction model includes: Setting any satellite image in the regional network as a control image, dividing the control image evenly into a plurality of grids, and generating an evenly distributed virtual image point at the center of each grid; For any virtual image point, the virtual image point is used as an observation value, the initial propagation accuracy of the control image is controlled, the basic adjustment equation of the virtual image point is constructed, and the adjustment parameter constraint equation of the virtual image point is obtained through parameter linearization; According to the adjustment parameter constraint equations of all virtual image points, the adjustment parameter constraint equations corresponding to the control image are obtained.

5. The method for detecting pathological observation areas of satellite imagery regional network adjustment according to claim 4 is characterized in that: The expression of the adjustment parameter constraint equation of the virtual image point is as follows: ； In the formula, Residual vector representing the adjustment parameter constraint equation of the kth virtual image point; Represents the partial derivative matrix of the adjustment parameter constraint equation of the kth virtual image point with respect to the adjustment parameter; It represents the adjustment parameter correction number of the control image corresponding to the kth virtual image point; Represents the negative value of the current value vector of the basic adjustment equation of the kth virtual image point; Represents the weight matrix of the adjustment parameter constraint equation for the kth virtual image point.

6. The method for detecting pathological observation areas of satellite image block adjustment according to claim 1, characterized in that: The expression of the control image accuracy propagation variance estimation model is as follows: ； , , , , , , ； In the formula, Represents the set of residual vectors of each constraint equation; Residual vector representing the spatial intersection constraint equations of all connection points; Residual vector representing the constraint equation of adjustment parameters for all virtual image points; Residual vector representing the additional constraint equation introduced for the object coordinates of the connection points; Represents the set of partial derivative matrices of each constraint equation with respect to the adjustment parameters; Represents the partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the adjustment parameters; Represents the partial derivative matrix of the adjustment parameter constraint equations of all virtual image points with respect to the adjustment parameters; represents the set of partial derivative matrices of the constraint equations with respect to the ground coordinates; The partial derivative matrix of the spatial intersection constraint equations of all connection points with respect to the ground coordinates; Represents the coefficient matrix of the additional constraint equation introduced; A collection of negative values ​​representing the current value vectors of various basic adjustment equations; Represents the negative value of the current value vector of the basic adjustment equations of all connection points; Represents the negative value of the current value vector of the basic adjustment equations of all virtual image points; Represents the set of weight matrices of each constraint equation; The weight matrix representing the spatial intersection constraint equations of all connection points; The weight matrix representing the constraint equations of adjustment parameters for all virtual image points; Represents the weight matrix of the additional constraint equation introduced; Indicates elevation accuracy; Represents the vector of adjustment parameter corrections for all images; A vector of corrections to the object space coordinates of all connection points.

7. The method for detecting pathological observation areas of satellite image block adjustment according to claim 6 is characterized in that: The expression of the correction factor matrix is: ； In the formula, represents the correction factor matrix; represents the first transition matrix; represents the second transition matrix; represents the third transition matrix; represents the fourth transition matrix.

8. The method for detecting pathological observation areas of satellite image block adjustment according to claim 1, characterized in that: The step of evaluating the model estimation accuracy of the error correction model corresponding to different positions on each satellite image to detect whether there is a pathological observation area on the corresponding satellite image includes: Each satellite image to be detected is evenly divided into multiple grids, and a uniformly distributed virtual image point is generated at the center of each grid; For any virtual image point, based on the error propagation law, the model covariance matrix of the virtual image point is calculated according to the correction coefficient matrix to obtain the model estimation accuracy of the error correction model of the virtual image point; For each satellite image, the model estimation accuracy of the error correction model of all virtual image points included is used to calculate the mean, maximum and root mean square error of the model estimation accuracy; In response to a maximum value of the model estimation accuracy of any satellite image being greater than a preset maximum value threshold, it is determined that a pathological observation area exists on the satellite image; In response to the difference between the maximum and mean values ​​of the model estimation accuracy of any satellite image being greater than a preset multiple of the root mean square error value, it is determined that a pathological observation area exists on the satellite image.

9. The method for detecting pathological observation areas of satellite image block adjustment according to claim 8, characterized in that: The expression of the model covariance matrix of the virtual image point is as follows: ； In the formula, represents the model covariance matrix of the mth virtual image point; Represents the partial derivative matrix of the error correction model with respect to the adjustment parameters; represents the covariance matrix of the correction numbers; The expression of the model estimation accuracy of the error correction model of the virtual image point is as follows: ； In the formula, represents the model estimation accuracy of the error correction model corresponding to the row direction of the mth virtual image point; represents the model estimation accuracy of the error correction model corresponding to the column direction of the mth virtual image point; Represents the model covariance matrix The element at row 1 and column 1 above; Show model covariance matrix The element in the last row and last column.

10. A detection system for pathological observation areas of satellite imagery network adjustment, applicable to the detection method for pathological observation areas of satellite imagery network adjustment according to any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to acquire a plurality of satellite images to be detected in a regional network, and a plurality of connection points corresponding to the satellite images; The connection point module builds a variance estimation model of adjustment parameters with elevation constraints for any connection point based on the preset error correction model. A control image module is used to construct an adjustment parameter constraint equation of the error correction model according to the satellite image by setting the control image; A control image accuracy propagation module, used for obtaining a control image accuracy propagation variance estimation model according to the adjustment parameter variance estimation model and the adjustment parameter constraint equation; A pathological observation area detection module is used to obtain the theoretical accuracy of the adjustment parameters of the error correction model of all satellite images by calculating the correction factor matrix based on the control image accuracy propagation variance estimation model and the error propagation law; Based on the error propagation law, the model estimation accuracy of the error correction model corresponding to different positions on each satellite image is evaluated to detect whether there are pathological observation areas on the corresponding satellite image.

Images