Geometric correction method and system for multispectral remote sensing data

Abstract

The invention relates to the technical field of image restoration, in particular to a geometric correction method and system for multispectral remote sensing data, and the method comprises the steps: obtaining a remote sensing image of a target region, and determining a plurality of target partitions in the remote sensing image; determining the distortion degree of the target partition, and determining the model order weight of the target partition based on the distortion degree; according to the model order weight, determining an anti-distortion model order of the target partition; and performing distortion correction on the target partition by using an anti-distortion model corresponding to the anti-distortion model order. According to the geometric correction method for the multispectral remote sensing data, a more matched anti-distortion model order is determined for different regions of the image, the distortion correction precision of the image is greatly improved, each region of the image is adaptively corrected more reasonably and accurately, and the correction accuracy of the image is improved. Therefore, the real area scene presented by the image can be restored more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of image restoration, and in particular to a geometric correction method and system for multispectral remote sensing data. Background Art

[0002] Image geometric correction refers to correcting geometric deformation caused by factors such as camera perspective and lens distortion, so as to restore the image to a more natural and accurate geometric form. Usually, this deformation will cause straight lines in the image to look curved, and the proportions of objects to be distorted. The main purpose of geometric correction is to eliminate or reduce these visual errors. The radial anti-distortion model is one of the most common models for solving lens distortion. It is used to describe the distortion phenomenon from the center of the image to the outside. This distortion phenomenon makes the straight lines in the image no longer straight lines, but curved instead.

[0003] At present, when performing geometric correction on remote sensing image data through radial distortion model, the position of the image pixels is generally corrected with a fixed anti-distortion model order. However, since image distortion is generally smaller in the center of the image and more serious in the edge area of ​​the image, using the same order of anti-distortion model for the entire image may easily lead to an imbalance problem in which the correction effect is better in the center of the image while the edge may be under-corrected or over-corrected, which is not conducive to accurately restoring the real area scene presented by the image. Summary of the invention

[0004] In order to solve the technical problem that the use of a fixed anti-distortion model order for image correction easily leads to unbalanced distortion correction and poor correction effect, the purpose of the present invention is to provide a geometric correction method and system for multispectral remote sensing data. The technical solution adopted is as follows: The present invention provides a geometric correction method for multispectral remote sensing data, the method comprising: Acquire a remote sensing image of a target area, and determine a plurality of target subareas in the remote sensing image; Determining a degree of distortion of the target partition, and determining a model order weight of the target partition based on the degree of distortion; Determining the anti-distortion model order of the target partition according to the model order weight; The anti-distortion model corresponding to the order of the anti-distortion model is used to perform distortion correction on the target partition.

[0005] Furthermore, the step of determining a plurality of target subareas in the remote sensing image comprises: The remote sensing image is divided into a preset number of concentric circle target partitions with the intersection of the diagonals of the remote sensing image as the center of the circle and half the length of the diagonals as the maximum circle radius.

[0006] Furthermore, the step of determining the degree of distortion of the target partition includes: Determining the line curvature of each line in the target partition and the similarity of the curvature directions of any two lines; The distortion degree of the target partition is calculated by using the line curvature and the bending direction similarity.

[0007] Furthermore, the step of determining the model order weight of the target partition based on the degree of distortion includes: Determining a difference in degree of distortion of the target partition relative to other partitions among all the partitions; Determining the number of partitions of all partitions and the target radius of the target partition; The model order weight of the target partition is calculated using the distortion degree difference, the number of partitions and the target radius.

[0008] Furthermore, the step of determining the anti-distortion model order of the target partition according to the model order weight comprises: Determining an order weight sequence composed of the order weights of each model according to the order of the target partitions from inside to outside; Determine the maximum difference between any two adjacent model order weights in the order weight sequence; The model order weight is used as the region and the maximum difference is used as the division condition. The function divides the order weight sequence into multiple sub-weight sequences; Setting each target partition corresponding to the same sub-weight sequence to the same anti-distortion model order corresponding to the arrangement order; The maximum difference includes a first-order weight and a second-order weight.

[0009] Furthermore, the model order weight is used as the region, the maximum difference is used as the division condition, and the The step of dividing the function order weight sequence into multiple sub-weight sequences includes: According to the arrangement order, traverse the weights of each order model as a region in the order weight sequence; Determine the order weights of each preceding model before the first order weight and the order weights of the subsequent models after the first order weight and including the first order weight; use Function, which divides each preceding model order weight and each succeeding model order weight into two different sub-weight sequences; The sub-weight sequence is used as the order weight sequence, and multiple sub-weight sequences are iterated according to the iteration stop parameter.

[0010] Furthermore, in the step of iteratively obtaining a plurality of sub-weight sequences according to the iteration stop parameter, the step of determining the iteration stop parameter includes: Obtain the model order weight concentration of each sub-weight sequence formed in the current iteration and the average model order weight difference between any two sub-weight sequences; The current iteration stop parameter is calculated using the model order weight concentration and the average model order weight difference.

[0011] Furthermore, the method further comprises: Determine the transition area between each adjacent target partition; In the case where the transition region is formed adjacent to target partitions corresponding to the same sub-weight sequence, the transition region is set to have the same anti-distortion model order as that of the adjacent target partition.

[0012] Furthermore, after the step of determining the transition area between each adjacent target partition, the method further includes: In the case where the transition zone is formed by adjacent target partitions corresponding to different sub-weight sequences, determining an inner partition and an outer partition corresponding to the transition zone; Determining a reference anti-distortion model order of the inner partition or the outer partition; Determine the internal model order weight of the internal partition and the number of internal partitions of the sub-weight sequence to which the internal partition belongs; Determine the external model order weight of the external partition and the number of external partitions of the sub-weight sequence to which the external partition belongs; The anti-distortion model order of the transition zone is calculated using the reference anti-distortion model order, the internal model order weight, the number of internal partitions, the external model order weight, and the number of external partitions.

[0013] The present invention also provides a geometric correction system for multispectral remote sensing data, the system is used to implement the geometric correction method for multispectral remote sensing data as described in any one of the above items; the system comprises: A region division module is used to obtain a remote sensing image of a target area and determine a plurality of target regions in the remote sensing image; A weight calculation module, used to determine the degree of distortion of the target partition, and determine the model order weight of the target partition based on the degree of distortion; An order calculation module, used to determine the anti-distortion model order of the target partition according to the model order weight; The model correction module uses the anti-distortion model corresponding to the order of the anti-distortion model to perform distortion correction on the target partition.

[0014] The present invention has the following beneficial effects: The present invention is based on the discovery that different regions of an image have different degrees of distortion, and overcomes the problem of insufficient or excessive correction of some image regions when the overall image is corrected by an anti-distortion model of a fixed order. The image is divided into multiple sub-regions, and distortion parameters are fitted separately for each sub-region, so that a more suitable anti-distortion model order is determined according to the distortion state of different regions of the image, thereby greatly improving the distortion correction accuracy of the image, so that each region of the image can be adaptively corrected in a more reasonable and accurate manner, thereby more accurately restoring the real regional scene presented by the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of steps of a geometric correction method for multispectral remote sensing data provided by one embodiment of the present invention; Figure 2 A detailed flow chart of step S2 in a geometric correction method for multispectral remote sensing data provided by one embodiment of the present invention; Figure 3 A detailed flow chart of step S3 in a geometric correction method for multispectral remote sensing data provided by one embodiment of the present invention; Figure 4 A schematic diagram of the area division involved in a geometric correction method for multispectral remote sensing data provided by an embodiment of the present invention; Figure 5 A schematic diagram of the structure of the hardware operating environment of a geometric correction device for multispectral remote sensing data involved in an embodiment of the present invention; Figure 6 The figure is a schematic diagram of the framework structure of a geometric correction system for multispectral remote sensing data involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a geometric correction method and system for multispectral remote sensing data proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is a detailed description of a geometric correction method and system for multispectral remote sensing data provided by the present invention in conjunction with the accompanying drawings.

[0020] Embodiment 1: For a geometric correction method for multispectral remote sensing data provided by the present invention, please refer to Figure 1 , which shows a flowchart of the steps of a geometric correction method for multispectral remote sensing data provided by an embodiment of the present invention.

[0021] The method comprises: Step S1, obtaining a remote sensing image of a target area, and determining a plurality of target subareas in the remote sensing image; In this embodiment, the remote sensing image of the target area can be obtained by drone aerial photography and converted into grayscale. Due to reasons such as the camera viewing angle and lens design, radial distortion may occur, which makes the straight line appear curved in the image, especially when it is close to the edge of the image.

[0022] Since errors will occur when distorted images are interpreted by remote sensing, it is necessary to perform geometric correction on the distorted images. At present, the radial anti-distortion model is generally used to process images. The points in the image will be offset relative to the center of the image, and the degree of offset is related to the distance from the center of the image. The formula of the radial anti-distortion model can usually be expressed as: ; In the formula, Indicates the distance from any pixel to the center (point) of the image after adjustment. is the distance from any pixel to the center of the image before adjustment; it uses the calibrated radial distortion coefficient The more coefficients, the greater the order of the anti-distortion model, and the greater the degree of correction for distortion. Applying it to the image, adjusting the coordinates of each pixel point to restore it to its original position, the above is the existing technology and will not be repeated here.

[0023] Since radial distortion is often smaller in the center of the image and more severe in the edge of the image, there will be local errors when correcting with a single fixed anti-distortion model, which reduces the accuracy of correction. Therefore, the technical concept of this embodiment is mainly to set different anti-distortion model parameters to perform adaptive correction on different image areas.

[0024] After acquiring the remote sensing image, the remote sensing image can be divided into multiple target partitions as needed. The division method is not limited here. The rectangular remote sensing image can be divided into multiple equal parts, or the remote sensing image can be divided according to the actual distortion degree of different areas. Preferably, based on the rule that the distortion degree generally tends to increase from the center of the image to the outside, the remote sensing image can be divided into multiple target partitions in a concentric circle manner.

[0025] In a preferred embodiment, the step S1 comprises: The remote sensing image is divided into a preset number of concentric circle target partitions with the intersection of the diagonals of the remote sensing image as the center of the circle and half the length of the diagonals as the maximum circle radius.

[0026] Please refer to Figure 4 In order to determine the corresponding anti-distortion model order for different distortion levels, the image needs to be divided into regions first. Since the distortion level of the central area of ​​the captured remote sensing image is generally relatively small, and the distortion level increases toward the edge area, the present embodiment first divides the image into regions. Here, the concentric circle division method is used, with the intersection of the two diagonal lines of the image as the center and half of the image diagonal as the maximum circle radius to divide the image into multiple concentric circle regions. Figure 4 For example, this embodiment sets 9 target partitions. The specific number of target partitions can be set according to actual needs. This is only an example for explanation.

[0027] Figure 4 The solid concentric circles in the figure are the area division lines, which divide the image into multiple target areas. The dotted lines outside the solid lines represent the transition areas that can further smoothly connect the correction results of different sub-areas. The width of each concentric circle is the same, and the width of each transition area is the same. The distortion correction and smooth transition of the transition area will be described in the following embodiments.

[0028] Step S2, determining the degree of distortion of the target partition, and determining the model order weight of the target partition based on the degree of distortion; Specifically, the step of determining the degree of distortion of the target partition includes: Determining the line curvature of each line in the target partition and the similarity of the curvature directions of any two lines; The distortion degree of the target partition is calculated by using the line curvature and the bending direction similarity.

[0029] In order to determine the corresponding anti-distortion model order for different image regions, it is first necessary to analyze the degree of image distortion in different regions. A relatively higher anti-distortion model order is used for regions with a higher degree of distortion, and a relatively lower anti-distortion model order is used for regions with a lower degree of distortion.

[0030] The degree of image distortion is generally measured by the degree of curvature of the line features in the image. If the curvature direction of the lines in the image is the same and the greater the curvature, the greater the degree of image distortion in this area. Therefore, based on the above principle, the Sobel operator is used to detect the possible lines in any target partition, and then for each target partition, there are: ; In the formula, Represents the degree of distortion of the target partition image. and Represents the lines in this partition With lines The maximum principal direction vector of PCA (Principal Component Analysis), represents the angle between two direction vectors, is the number of lines in this partition, is the average of the curvatures of all lines in this partition, The maximum value of the curvature of all lines in this partition.

[0031] therefore, represents the similarity of the curvature direction of any two lines in this partition. Since PCA can decompose the main trend of the line, which represents the curvature direction of the line, then The larger the value is, the more lines in this partition have similar bending directions, which means that the image distortion of this partition is greater. Represents the degree of curvature of the lines in this partition. Since more obvious distortion will produce more curved curves, The larger the value of , the larger the average curvature of the lines in this partition, which means that the image distortion degree of this partition is greater. In summary, the corresponding distortion degree is determined for all concentric circle partitions for subsequent analysis.

[0032] For details, please refer to Figure 2 The step of determining the model order weight of the target partition based on the degree of distortion comprises: Step S20, determining the difference in distortion degree between the target partition and other partitions in all partitions; Step S21, determining the number of partitions of all partitions and the target radius of the target partition; Step S22: Calculate the model order weight of the target partition using the distortion degree difference, the number of partitions and the target radius.

[0033] Since different target partitions correspond to different degrees of distortion, in order to correct the distortion of the image, it is necessary to determine the model order of the corresponding image partition based on the anti-distortion model and the difference in the degree of distortion. It is known that the higher the model order, the greater the distortion correction capability, and the greater the degree of distortion of a certain partition of the image, the greater the distortion correction capability required to adjust, and the smaller the degree of distortion, the smaller the distortion correction capability required. Therefore, the corresponding anti-distortion model order can be determined for different partitions of the image through the above principle for adaptive correction.

[0034] Since the partitions closer to the edge of the image have a greater degree of distortion, the corresponding model order weights can be determined based on the positions of different partitions and the corresponding degree of distortion, as shown below: ; In the formula, For this image The model order weight for each partition. For the The degree of distortion of each partition, For the rest The degree of distortion of each partition, is the number of partitions for this image, and norm represents the linear normalization function. For the The radius of the partition (target radius). Represents the The difference in the degree of distortion of a partition relative to other partitions. The larger the value, the greater the degree of distortion of the corresponding partition. Therefore, a higher order of anti-distortion model is required, and the corresponding model order weight is larger. Then, combined with the relative position of this partition in the image, , The larger the corresponding partition is, the closer it is to the outer edge, and the greater the corresponding degree of distortion is, and the greater the corresponding model order weight is. Finally, the corresponding model order weight is determined for any partition and the result is normalized to within the range.

[0035] Step S3, determining the anti-distortion model order of the target partition according to the model order weight; For details, please refer to Figure 3 , the step S3 comprises: Step S30, determining an order weight sequence composed of the order weights of each model according to the arrangement order of the target partition from inside to outside; Step S31, determining the maximum difference between any two adjacent model order weights in the order weight sequence; Step S32, using the model order weight as the region and the maximum difference as the division condition, using The function divides the order weight sequence into multiple sub-weight sequences; Further, step S32 includes: According to the arrangement order, traverse the weights of each order model as a region in the order weight sequence; Determine the order weights of each preceding model before the first order weight and the order weights of the subsequent models after the first order weight and including the first order weight; use Function, which divides each preceding model order weight and each succeeding model order weight into two different sub-weight sequences; The sub-weight sequence is used as the order weight sequence, and multiple sub-weight sequences are iterated according to the iteration stop parameter.

[0036] Due to the gradual variation of the degree of image distortion, the model order weights corresponding to the above-mentioned different partitions tend to increase gradually from the inner circle to the outer circle. If different orders of anti-distortion models are assigned one by one according to the relative positions of different partitions, if there is only a slight difference in the degree of distortion between adjacent partitions, then the corrected image will have a serious tearing sensation at this position, and the purpose of accurate correction cannot be achieved. Therefore, it is necessary to determine the same or different orders based on the model order weights for different partitions, that is, to classify or group different target partitions, and target partitions of the same type or group (same sequence) are adapted to the same order of anti-distortion models.

[0037] Next, we need to analyze the degree of difference in model order weights between different partitions. If the difference in model order weights between two adjacent partitions is greater, different model orders need to be assigned for correction. If the difference in model order weights between two adjacent partitions is smaller, only the same model order needs to be assigned for correction. Therefore, a sequence (order weight sequence) is set according to the order of the model order weights of different partitions from inside to outside. , and then with the help of The function (function in Excel) performs iterative classification as follows: ; In the formula, Represents the number of model order weights and corresponding subsequences obtained for a certain classification. The parameter indicates that the algorithm Execution begins at The algorithm traverses the sequence from the initial position of the sequence. The parameter represents the maximum difference between any two adjacent model order weights in the sequence (the maximum difference includes the first-order weight and the second-order weight, the first-order weight is the larger order weight, and the second-order weight is the smaller order weight), that is, calculate any two adjacent The difference between the values ​​(values ​​of the model order weights) is then counted, and the two values ​​corresponding to the maximum difference are The number of elements before (not including) the first-order weight in the value (the number of elements, the number of model order weights, and the number of target partitions all represent the same number) is , and put the elements before the first-order weight and the elements including and after the first-order weight into two different sequences respectively, and continue to use this method for classification, and iterate in sequence.

[0038] The step of determining the iteration stop parameter includes: Obtain the model order weight concentration of each sub-weight sequence formed in the current iteration and the average model order weight difference between any two sub-weight sequences; The current iteration stop parameter is calculated using the model order weight concentration and the average model order weight difference.

[0039] During the iteration process, the sequence is continuously divided into multiple subsequences, and the model order weights between multiple subsequences are relatively large. However, if this method is iterated infinitely, the sequence will Each model order weight is divided into an independent sequence, which is the same as allocating the order one by one according to the relative position of the partition. Therefore, in order to avoid the above situation, it is necessary to define the iteration stop condition.

[0040] The purpose of this embodiment is to divide the models with similar order weights into one sequence, and divide the models with large differences in order weights into different sequences, so as to determine the corresponding model orders in a targeted manner. Therefore, the difference in weights inside and outside the sequence generated by each iteration round can be analyzed to determine the round at which the iteration needs to be stopped, as shown below: ; In the formula, For the current The iteration stop parameter corresponding to the round iteration. and They are the first The sequence and The average model order weight of the series, For the The number of sequences formed by round iterations. Representative The average of the standard deviations of the model order weights across all sequences of round iterations. Then It represents the average model order weight difference between any two sequences in this round of iteration. The larger the value, the better the sequence division effect. Then the iteration needs to be stopped in time, and the larger the corresponding stop parameter. Measures the concentration of the model order weights within each sequence. The smaller the value, the more similar the model order weights within the sequence are. The better the division effect is, and the larger the corresponding stop parameter is. The function normalizes the result to In the range selection iteration process When the value is greater than the preset stop threshold for the first time, the iterative calculation is stopped. The preset stop threshold can be set as needed, for example, it can be 0.88.

[0041] Step S33, setting each target partition corresponding to the same sub-weight sequence to the same anti-distortion model order corresponding to the arrangement order; So far, the model order weights of different partitions have been classified through the above steps. The model order weights in each category are relatively similar and the model order weights between different categories are relatively different. For multiple model order weights in the same category, the same anti-distortion model order can be set for the corresponding partitions. At the same time, according to the increasing trend of the distortion degree from the inside to the outside of the partition and the corresponding arrangement order, different anti-distortion model orders are set for different categories. For example: If the classification result is , , , then A first-order anti-distortion model is set for each partition image in .for In each partition image, a second-order anti-distortion model is set, that is, , and so on.

[0042] Step S4: using the anti-distortion model corresponding to the order of the anti-distortion model to perform distortion correction on the target partition.

[0043] At this point, the target partition image is processed through the anti-distortion model corresponding to different partitions in the remote sensing image, and the correct position of the corresponding pixel points in the image is calculated to correct the image distortion, thereby fully considering the different distortion characteristics of each area of ​​the image and ensuring the geometric accuracy of the overall image.

[0044] In addition, in a preferred embodiment, the method further comprises: Determine the transition area between each adjacent target partition; In the case where the transition region is formed adjacent to target partitions corresponding to the same sub-weight sequence, the transition region is set to have the same anti-distortion model order as that of the adjacent target partition.

[0045] After determining the corresponding anti-distortion model for different partitioned images, since different orders of anti-distortion models have different distortion correction capabilities for images, there is a problem of excessive unnaturalness at the junction of partitioned images. Therefore, a transition zone is set between any adjacent partitions during the partitioning process. The purpose is to set a suitable anti-distortion model in the transition zone to connect the corrected effects of two adjacent target partitions to make it a smoother transition.

[0046] Since the above embodiment classifies the model order weights of different partitions, and all partitions in the same classification sequence have the same model order, the transition areas between all partitions in the same classification sequence can be filled with the same model order as the sequence.

[0047] In another preferred embodiment, after the step of determining the transition area between each adjacent target partition, the method further comprises: In the case where the transition zone is formed by adjacent target partitions corresponding to different sub-weight sequences, determining an inner partition and an outer partition corresponding to the transition zone; Determining a reference anti-distortion model order of the inner partition or the outer partition; Determine the internal model order weight of the internal partition and the number of internal partitions of the sub-weight sequence to which the internal partition belongs; Determine the external model order weight of the external partition and the number of external partitions of the sub-weight sequence to which the external partition belongs; The anti-distortion model order of the transition zone is calculated using the reference anti-distortion model order, the internal model order weight, the number of internal partitions, the external model order weight, and the number of external partitions.

[0048] For the transition zone at the junction of partitions corresponding to different classification sequences, the anti-distortion model order of the corresponding transition zone needs to be determined according to the number of partitions and the difference in model order weights. If there are more partitions in the classification sequence corresponding to one of the two sides of the transition zone and the model order weight is larger, then the anti-distortion model order of this transition zone is more likely to be set to the model order corresponding to this partition, as shown below: ; In the formula, The order of the anti-distortion model for the transition zone formed by the partitions corresponding to a certain different classification sequence. The order of the anti-distortion model of the internal partition corresponding to this transition zone can also be replaced by , that is, the order of the anti-distortion model of the external partition, one of the two is selected as the reference anti-distortion model order; The number of partitions of the inner partition corresponding to this transition zone, The model order weight for the inner partition corresponding to this transition region. The number of partitions of the outer partition corresponding to this transition zone, The model order weight for the outer partition corresponding to this transition region. represents the rounding function. Then It represents the number of partitions and the difference in model order weight between the internal and external partitions corresponding to this transition zone. It is used as the weighting factor. The larger the value, the smaller the external partition (in terms of as reference) or internal partitions (with The larger the weight of (as a reference), the greater the order of the anti-distortion model in the transition zone should be, and vice versa.

[0049] By using the above method, the corresponding anti-distortion model order is determined for the transition zone at the junction of the partitions corresponding to different classifications to perform connection correction, thereby achieving a smooth transition at the junction of the partitions.

[0050] Embodiment 2: The embodiment of the present invention further provides a geometric correction device for multispectral remote sensing data. The geometric correction device for multispectral remote sensing data can be a data processing terminal such as a drone, a computer, or a server.

[0051] like Figure 5 As shown, Figure 5 It is a structural schematic diagram of the hardware operating environment of a geometric correction device for multispectral remote sensing data involved in an embodiment of the present invention.

[0052] like Figure 5 As shown, the geometric correction device for multi-spectral remote sensing data may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display (Display), an input unit such as a control panel, and the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WIFI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005 as a computer storage medium may include a geometric correction program for multi-spectral remote sensing data.

[0053] Those skilled in the art will understand that Figure 5The hardware structure shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0054] Continue to refer to Figure 5 , Figure 5 The memory 1005 as a computer-readable storage medium may include an operating system, a user interface module, a network communication module, and a geometric correction program for multispectral remote sensing data.

[0055] exist Figure 5 In the embodiment, the network communication module is mainly used to connect to the server and can communicate data with the server; and the processor 1001 can call the geometric correction program for multispectral remote sensing data stored in the memory 1005 and execute the steps in the above embodiments.

[0056] The hardware structure of the geometric correction device for multispectral remote sensing data is used to implement various embodiments of the geometric correction for multispectral remote sensing data of the present invention.

[0057] In addition, the present invention also provides a geometric correction system for multispectral remote sensing data, please refer to Figure 6 , the geometric correction system for multispectral remote sensing data includes: The region division module A10 is used to obtain a remote sensing image of a target area and determine a plurality of target regions in the remote sensing image; A weight calculation module A20, used to determine the degree of distortion of the target partition, and determine the model order weight of the target partition based on the degree of distortion; An order calculation module A30, used to determine the anti-distortion model order of the target partition according to the model order weight; The model correction module A40 uses the anti-distortion model corresponding to the anti-distortion model order to perform distortion correction on the target partition.

[0058] Furthermore, the area division module A10 is also used for: The remote sensing image is divided into a preset number of concentric circle target partitions with the intersection of the diagonals of the remote sensing image as the center of the circle and half the length of the diagonals as the maximum circle radius.

[0059] Furthermore, the weight calculation module A20 is also used for: Determining the line curvature of each line in the target partition and the similarity of the curvature directions of any two lines; The distortion degree of the target partition is calculated by using the line curvature and the bending direction similarity.

[0060] Furthermore, the weight calculation module A20 is also used for: Determining a difference in degree of distortion of the target partition relative to other partitions among all the partitions; Determining the number of partitions of all partitions and the target radius of the target partition; The model order weight of the target partition is calculated using the distortion degree difference, the number of partitions and the target radius.

[0061] Furthermore, the order calculation module A30 is also used for: Determining an order weight sequence composed of the order weights of each model according to the order of the target partitions from inside to outside; Determine the maximum difference between any two adjacent model order weights in the order weight sequence; The model order weight is used as the region and the maximum difference is used as the division condition. The function divides the order weight sequence into multiple sub-weight sequences; Setting each target partition corresponding to the same sub-weight sequence to the same anti-distortion model order corresponding to the arrangement order; The maximum difference includes a first-order weight and a second-order weight.

[0062] Furthermore, the order calculation module A30 is also used for: According to the arrangement order, traverse the weights of each order model as a region in the order weight sequence; Determine the order weights of each preceding model before the first order weight and the order weights of the subsequent models after the first order weight and including the first order weight; use Function, which divides each preceding model order weight and each succeeding model order weight into two different sub-weight sequences; The sub-weight sequence is used as the order weight sequence, and multiple sub-weight sequences are iterated according to the iteration stop parameter.

[0063] Furthermore, the order calculation module A30 is also used for: Obtain the model order weight concentration of each sub-weight sequence formed in the current iteration and the average model order weight difference between any two sub-weight sequences; The current iteration stop parameter is calculated using the model order weight concentration and the average model order weight difference.

[0064] Furthermore, the order calculation module A30 is also used for: Determine the transition area between each adjacent target partition; In the case where the transition region is formed adjacent to target partitions corresponding to the same sub-weight sequence, the transition region is set to have the same anti-distortion model order as that of the adjacent target partition.

[0065] Furthermore, the order calculation module A30 is also used for: In the case where the transition zone is formed by adjacent target partitions corresponding to different sub-weight sequences, determining an inner partition and an outer partition corresponding to the transition zone; Determining a reference anti-distortion model order of the inner partition or the outer partition; Determine the internal model order weight of the internal partition and the number of internal partitions of the sub-weight sequence to which the internal partition belongs; Determine the external model order weight of the external partition and the number of external partitions of the sub-weight sequence to which the external partition belongs; The anti-distortion model order of the transition zone is calculated using the reference anti-distortion model order, the internal model order weight, the number of internal partitions, the external model order weight, and the number of external partitions.

[0066] The specific implementation of the geometric correction system for multispectral remote sensing data of the present invention is basically the same as the above-mentioned embodiments of the geometric correction method for multispectral remote sensing data, and will not be repeated here.

[0067] In addition, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention stores a geometric correction program for multispectral remote sensing data, wherein when the geometric correction program for multispectral remote sensing data is executed by a processor, the steps of the geometric correction method for multispectral remote sensing data as described above are implemented.

[0068] Among them, the method implemented when the geometric correction program for multispectral remote sensing data is executed can refer to the various embodiments of the geometric correction method for multispectral remote sensing data of the present invention, and will not be repeated here.

[0069] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0071] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take 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 code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 produce 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.

[0073] 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.

[0074] 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.

[0075] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art are aware of the basic inventive concepts.

[0076] The above description is only a preferred embodiment of the present invention, and does not limit the scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the protection scope of the present invention.

Claims

1. A geometric correction method for multispectral remote sensing data, characterized in that: The method comprises: Acquire a remote sensing image of a target area, and determine a plurality of target subareas in the remote sensing image; Determining a degree of distortion of the target partition, and determining a model order weight of the target partition based on the degree of distortion; Determining the anti-distortion model order of the target partition according to the model order weight; The anti-distortion model corresponding to the order of the anti-distortion model is used to perform distortion correction on the target partition.

2. The geometric correction method for multispectral remote sensing data according to claim 1, characterized in that: The step of determining a plurality of target subareas in the remote sensing image comprises: The remote sensing image is divided into a preset number of concentric circle target partitions with the intersection of the diagonals of the remote sensing image as the center of the circle and half the length of the diagonals as the maximum circle radius.

3. The geometric correction method for multispectral remote sensing data according to claim 1, characterized in that: The step of determining the degree of distortion of the target partition comprises: Determining the line curvature of each line in the target partition and the similarity of the curvature directions of any two lines; The distortion degree of the target partition is calculated by using the line curvature and the bending direction similarity.

4. The geometric correction method for multispectral remote sensing data according to claim 1, characterized in that: The step of determining the model order weight of the target partition based on the degree of distortion comprises: Determining a difference in degree of distortion of the target partition relative to other partitions among all the partitions; Determining the number of partitions of all partitions and the target radius of the target partition; The model order weight of the target partition is calculated using the distortion degree difference, the number of partitions and the target radius.

5. The geometric correction method for multispectral remote sensing data according to claim 1, characterized in that: The step of determining the anti-distortion model order of the target partition according to the model order weight comprises: Determining an order weight sequence composed of the order weights of each model according to the order of the target partitions from inside to outside; Determine the maximum difference between any two adjacent model order weights in the order weight sequence; The model order weight is used as the region and the maximum difference is used as the division condition. The function divides the order weight sequence into multiple sub-weight sequences; Setting each target partition corresponding to the same sub-weight sequence to the same anti-distortion model order corresponding to the arrangement order; The maximum difference includes a first-order weight and a second-order weight.

6. The geometric correction method for multispectral remote sensing data according to claim 5, characterized in that: The model order weight is used as the region and the maximum difference is used as the division condition. The step of dividing the function order weight sequence into multiple sub-weight sequences includes: According to the arrangement order, traverse the weights of each order model as a region in the order weight sequence; Determine the order weights of each preceding model before the first order weight and the order weights of the subsequent models after the first order weight and including the first order weight; use Function, which divides each preceding model order weight and each succeeding model order weight into two different sub-weight sequences; The sub-weight sequence is used as the order weight sequence, and multiple sub-weight sequences are iterated according to the iteration stop parameter.

7. The geometric correction method for multispectral remote sensing data according to claim 6, characterized in that: In the step of iteratively obtaining a plurality of sub-weight sequences according to the iteration stop parameter, the step of determining the iteration stop parameter comprises: Obtain the model order weight concentration of each sub-weight sequence formed in the current iteration and the average model order weight difference between any two sub-weight sequences; The current iteration stop parameter is calculated using the model order weight concentration and the average model order weight difference.

8. The geometric correction method for multispectral remote sensing data according to claim 6, characterized in that: The method further comprises: Determine the transition area between each adjacent target partition; In the case where the transition region is formed adjacent to target partitions corresponding to the same sub-weight sequence, the transition region is set to have the same anti-distortion model order as that of the adjacent target partition.

9. The geometric correction method for multispectral remote sensing data according to claim 8, characterized in that: After the step of determining the transition area between each adjacent target partition, the method further comprises: In the case where the transition zone is formed by adjacent target partitions corresponding to different sub-weight sequences, determining an inner partition and an outer partition corresponding to the transition zone; Determining a reference anti-distortion model order of the inner partition or the outer partition; Determine the internal model order weight of the internal partition and the number of internal partitions of the sub-weight sequence to which the internal partition belongs; Determine the external model order weight of the external partition and the number of external partitions of the sub-weight sequence to which the external partition belongs; The anti-distortion model order of the transition zone is calculated using the reference anti-distortion model order, the internal model order weight, the number of internal partitions, the external model order weight, and the number of external partitions.

10. A geometric correction system for multispectral remote sensing data, characterized in that: The system is used to implement the geometric correction method for multispectral remote sensing data as described in any one of claims 1 to 9; the system comprises: A region division module is used to obtain a remote sensing image of a target area and determine a plurality of target regions in the remote sensing image; A weight calculation module, used to determine the degree of distortion of the target partition, and determine the model order weight of the target partition based on the degree of distortion; An order calculation module, used to determine the anti-distortion model order of the target partition according to the model order weight; The model correction module uses the anti-distortion model corresponding to the order of the anti-distortion model to perform distortion correction on the target partition.

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