High-precision surveying and mapping method based on dynamic remote sensing technology

Abstract

The invention discloses a high-precision surveying and mapping method based on a dynamic remote sensing technology, and relates to the technical field of remote sensing surveying and mapping, and the method comprises the following steps: S1, carrying out the construction of surface change features through a multi-temporal remote sensing image; s2, performing regional disturbance response deconstruction by using the change function index set; s3, performing disturbance layering constraint modeling by using an interval division result; s4, performing disturbance response projection calculation by using the fitting model and an interval division result; and S5, performing disturbance tensor trend modeling by using the point location updating solution graph. By setting a partition dynamic response feature deconstruction and hierarchical constraint fitting modeling mechanism, high-precision modeling is performed for different disturbance response intensity intervals, so that the problem of local change feature loss caused by a single modeling template in the prior art is effectively solved; and it is ensured that modeling results conforming to local features can be achieved in areas with different response intensities, so that the point location updating precision and the deformation extraction reliability are improved.

Description

Technical Field

[0001] The invention relates to the field of remote sensing technology, and in particular to a high-precision surveying and mapping method based on dynamic remote sensing technology. Background Art

[0002] In recent years, with the continuous improvement of remote sensing image resolution and observation frequency, the monitoring and mapping technology of dynamic surface changes based on remote sensing images has gradually become an important development direction in the field of high-precision mapping. Traditional methods mostly rely on single-phase images or image data of a small number of time nodes, which makes it difficult to accurately capture small surface disturbances and their evolution process, especially in areas with drastic dynamic changes and fine scales. There are problems of insufficient mapping accuracy and long update cycle. In order to achieve high-precision and continuous description of surface dynamic processes, there is an urgent need for a mapping method that can combine the change characteristics of multi-phase images, accurately extract surface change information and perform fine modeling.

[0003] In the existing technology, layered modeling of surface changes often adopts methods based on fixed templates or traditional least squares fitting, which fails to effectively utilize the partitioned response characteristics of dynamic change indicators, resulting in insufficient modeling accuracy in the disturbed area. Local slight change characteristics are easily lost due to averaging, making it difficult to support high-precision point updates and fine-grained disturbance trend extraction. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides a high-precision surveying and mapping method based on dynamic remote sensing technology to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a high-precision surveying and mapping method based on dynamic remote sensing technology, comprising the following steps: S1. Use multi-temporal remote sensing images to construct surface change characteristics and obtain a set of change function indicators; S2, using the variation function indicator set to deconstruct the regional disturbance response and obtain the response intensity interval division; S3, using the interval partitioning results to perform disturbance hierarchical constraint modeling to obtain a disturbance sensitive fitting model; S4. Use the fitting model and the interval division result to perform disturbance response projection solution to obtain a point update solution diagram; S5. Use the point update solution diagram to perform disturbance tensor trend modeling to obtain a regional disturbance trend map.

[0006] 2. Further optimize this technical solution, the step S1 provides a basic surface reflection function index set for subsequent zoning and modeling, for remote sensing image sequences , that is, the time span is The pixel image sequence is used to construct the change trend intensity index function , the dynamic fluctuation amplitude index and the cumulative perturbation index ; The change trend intensity index function : ; The dynamic fluctuation amplitude index function : ; Among them, ; The cumulative perturbation index function : ; Among them, is the perturbation time series weight function, .

[0007] To further optimize this technical solution, the coupling perturbation response function constructed in step S2 : ; Among them, represents the gradient of the index function in the spatial dimension, that is, the perturbation change direction.

[0008] To further optimize this technical solution, the interval division method in step S2 includes: The set of perturbation response intensity levels , where each level corresponds to a sub-interval on the value range: If and only if ; Among them, represents the minimum value of the perturbation response intensity within the given interval; represents the maximum value of the perturbation response intensity within the given interval; : For the quantile value of the dataset; : For the quantile value of the dataset.

[0009] To further optimize this technical solution, the regional perturbation response decomposition process in step S2 includes: Input: The index set output by step S1 ; Gradient field calculation: Calculate the gradient of each index function in the spatial dimension ; Coupling term construction: Construct the gradient direction coupling term and combine it into the response function ; Statistical modeling: Perform range distribution analysis on the entire map; Interval division: Divide the range into perturbation response level intervals according to the set rules; Output: Obtain the perturbation response intensity interval division map .

[0010] To further optimize this technical solution, in step S3, a perturbation-sensitive fitting function model is constructed, and its model formula is: ; where, represents the th perturbation response intensity interval; : Constant coefficient, used to maintain the consistency of physical dimensions; : Represents within the th response intensity interval, the local response sensitivity of the index function to the perturbation function ; : Represents the local variation function of the index function at the spatial position , used to construct the local response form of the fitting function.

[0011] To further optimize this technical solution, the perturbation-sensitive fitting modeling process in step S3 includes: Input: The index function output by step S1 ; The perturbation response function output by step S2 and its interval division ; Local sensitivity extraction: Calculate within each interval; Local variation function construction: Extract the local spatial variation function of each index function at ; Fitting function construction: Construct the perturbation-sensitive fitting function for each response interval using the perturbation-sensitive fitting function model formula Fitting model combination: Construct the overall fitting model ; where is an indicator function to determine whether the pixel belongs to the interval; ; Output: Perturbation-sensitive fitting function set and combined model .

[0012] To further optimize this technical solution, in step S4, the perturbation response-driven solution model is used to update the low-resolution pixel positions of the original remote sensing image to a high-resolution position expression map with response stratification characteristics. The model formula is as follows: ; where : represents the high-precision position update solution map of the final output; : is the indicator function for the interval; : point position reanalysis projection mapping operator; : point position change density divergence operator.

[0013] To further optimize this technical solution, in step S5, the dynamic perturbation deformation tensor trend model is used for spatial perturbation deformation tensor analysis. The formula model is as follows: , ; where : represents the perturbation main trend direction vector at position ; : represents the gradient vector of ; : symmetric perturbation deformation tensor; : arbitrary direction vector; : is the Euclidean norm; used to extract the direction that maximizes the tensor response intensity.

[0014] Further optimize this technical solution. During the use of the formula model in step S5, it includes: Gradient acquisition: First, calculate the two-dimensional gradient of the solution graph to describe the change in the local perturbation direction: ; Among them, : The partial derivative with respect to , representing the rate of change of along the direction; : The partial derivative with respect to , representing the rate of change of along the direction; Construct the deformation tensor: Use to form a symmetric positive definite tensor to describe the coupling of deformation intensity in the perturbation direction; Extract the main trend direction: At each pixel position , find the main trend direction by maximizing . This direction is the main eigenvector of the tensor, representing the most significant direction of local perturbation.

[0015] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a high-precision mapping method based on dynamic remote sensing technology as described in the first aspect of the present invention are implemented.

[0016] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, wherein: when the computer program instructions are executed by the processor, the steps of a high-precision mapping method based on dynamic remote sensing technology as described in the first aspect of the present invention are implemented.

[0017] Compared with the prior art, the present invention provides a high-precision mapping method based on dynamic remote sensing technology, having the following beneficial effects: The high-precision mapping method based on dynamic remote sensing technology effectively solves the problem of loss of local change features caused by a single modeling template in the prior art by setting up a mechanism for deconstructing the dynamic response characteristics of partitions and hierarchical constraint fitting modeling, and performing high-precision modeling for different disturbance response intensity intervals respectively. This method uses the disturbance response function constructed by the change index of multi-temporal images, first divides the region, and then independently fits according to the characteristics of each interval to ensure that modeling results conforming to local features can be achieved in different response intensity regions, thereby improving the accuracy of point position update and the reliability of deformation extraction. Overall, this method belongs to the field of remote sensing mapping and surface dynamic monitoring technology, and is particularly suitable for continuous mapping of small-scale surface disturbances, disaster monitoring, and urban refined management scenarios. Through the above design, this method has achieved significant improvements in the accuracy of capturing surface changes, the accuracy of depicting disturbance trends, and the efficiency of updating mapping data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic structural diagram of a high-precision mapping method based on dynamic remote sensing technology proposed by the present invention; Figure 2 It is a schematic flow diagram of the regional disturbance response deconstruction of a high-precision mapping method based on dynamic remote sensing technology proposed by the present invention; Figure 3 It is a schematic flow diagram of the disturbance-sensitive fitting modeling of a high-precision mapping method based on dynamic remote sensing technology proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0021] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar promotions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0023] Embodiment 1: Referring to Figures 1 to 3 , this is the first embodiment of the present invention. This embodiment provides a high-precision mapping method based on dynamic remote sensing technology, including the following steps: S1. Use multi-temporal remote sensing images to construct surface change feature sets, obtaining a set of change function indicators; Step S1 provides a basic surface reflection function index set for subsequent zoning and modeling. For the remote sensing image sequence , that is, the pixel image sequence with a time span of , construct the change trend intensity index function , the dynamic fluctuation amplitude index , and the cumulative perturbation index , where represents the time number of image acquisition, is the total number of remote sensing image frames, represents the two-dimensional position of each pixel in the surface image, represents the time, and the remote sensing feature value such as the reflectance or radiation value at the position .

[0024] The change trend intensity index function : ; This function represents the average change amplitude per unit time and reflects the frequency of perturbations.

[0025] The dynamic fluctuation amplitude index function : ; Among them, ; This function is the standard deviation and represents the severity of the change.

[0026] The cumulative perturbation index function : ; Among them, is the perturbation time series weight function, ; This function enhances the cumulative perception of long-term perturbation behavior.

[0027] The change trend intensity index function in step S1 , the dynamic fluctuation amplitude index and the cumulative perturbation index include when in use: First, for each pixel on consecutive remote sensing image frames, extract its time series values ; Calculate the three function indicators respectively to obtain the corresponding indicator maps , and these maps are function fields in the spatial dimension; Combine to form an indicator vector set , as the input for subsequent zoning; If there are multiple spectral channels, calculate independently on each channel to form a channel enhancement indicator set. For each spectral channel , the indicator on its corresponding channel is; ; Among them, represents the change trend intensity index on channel ; represents the dynamic fluctuation amplitude index on channel ; represents the cumulative perturbation index on channel .

[0028] S2. Use the change function indicator set to perform regional perturbation response deconstruction to obtain the response intensity interval division; Step S2 uses the change function indicator set constructed in step S1 as the input, and performs continuous feature quantification and zoning processing for the dynamic response characteristics of surface changes. By establishing a hierarchical set of surface perturbation response intensities, the dynamic deconstruction of change indicators is realized, so that the change characteristics have a clearer hierarchical division on the spatial scale. This step maps the spatially continuous change indicators to the discrete partition perturbation levels, thereby providing clear and targeted basic interval support for subsequent hierarchical fitting and model construction, and ensuring the internal logical consistency and operability of the phased processing of the entire process.

[0029] Step S2 performs spatial zoning and structural analysis of the dynamic response characteristics on the generated in step S1 to form an ordered division of the regional perturbation feature intensity, providing a targeted modeling basis for subsequent modeling steps; In step S2, a coupled perturbation response function is constructed: ; Among them, Indicates the gradient of the metric function in the spatial dimension, i.e., the perturbation change direction, refers to , and one of; The interval division method in step S2 includes: Set of perturbation response intensity levels , where each level corresponds to a sub-interval on the value range: If and only if ; where, represents the minimum value of the perturbation response intensity within the given interval; represents the maximum value of the perturbation response intensity within the given interval; : For the th quantile value of the dataset; : For the th quantile value of the dataset.

[0030] The regional perturbation response deconstruction process of step S2 includes: Input: The metric set output by step S1 ; Gradient field calculation: Calculate the gradient of each metric function in the spatial dimension ; Coupling term construction: Construct the gradient direction coupling term and combine it into a response function ; Statistical modeling: Conduct a value range distribution analysis on the entire map; Interval division: Divide the value range into perturbation response level intervals according to the set rules; Output: Obtain the perturbation response intensity interval division map .

[0031] Different from the traditional methods based on static threshold division or simple clustering division, step S2 introduces a quantitative expression of dynamic response characteristics in the partitioning process, and conducts an internal structure deconstruction based on the continuity and spatial correlation of the change response. This method no longer relies on external experience or static classification criteria, but sets intervals through the response index system derived by itself, enabling the surface change information to dynamically adapt to the change laws of spatio-temporal scales, and enhancing the physical meaning and application scope of the perturbation response division.

[0032] S3. Use the interval division result to perform disturbance stratification constraint modeling to obtain a disturbance-sensitive fitting model; Step S3 follows the disturbance intensity interval division result completed in step S2. Based on the differences in surface disturbance characteristics in different intervals, hierarchical constraint conditions are set, and high-precision fitting modeling is performed on the remote sensing point data. Through hierarchical association control, fine fitting is performed for the specific change trends of each disturbance level, enabling the model to more accurately capture the local response characteristics of surface changes at each level. The finally formed disturbance-sensitive fitting model not only has stability in the overall trend but also can reflect disturbance heterogeneity at the fine-grained scale, significantly enhancing the adaptability of the model in complex dynamic environments.

[0033] Step S3 performs modeling on the disturbance response intensity interval division map obtained in step S2 to construct a high-precision fitting model of remote sensing points with disturbance sensitivity to depict the fitting trend of ground object changes in areas with different intensity responses; In step S3, a disturbance-sensitive fitting function model is constructed, and its model formula is: ; where represents the th disturbance response intensity interval; : Constant coefficient, used to maintain the consistency of physical dimensions; : Represents the local response sensitivity of the index function to the disturbance function in the th response intensity interval; : Represents the local change function of the index function at the spatial position , which is used to construct the local response form of the fitting function. The formula of the local change function is as follows: ; In this formula, represents the first-order spatial partial derivative of the index function along the direction, which is used to measure the severity of disturbance of the index in the horizontal direction; represents the first-order spatial partial derivative of the index function along the direction, which is used to measure the unevenness of the response of the index in the vertical direction.

[0034] The disturbance-sensitive fitting modeling process in step S3 includes: Input: The index function output by step S1 ; The disturbance response function output by step S2 and its interval division ; Local sensitivity extraction: In each interval, calculate ; Local change function construction: Extract the local spatial change function of each index function at ; ; Fitting function construction: Use the disturbance-sensitive fitting function model formula to construct the disturbance-sensitive fitting function for each response interval Fitting model combination: Construct the overall fitting model ; where is the indicator function to judge whether the pixel belongs to the interval; ; Output: The set of disturbance-sensitive fitting functions and the combined model .

[0035] Different from the existing unified fitting or rough fitting methods, step S3 adopts a hierarchical constraint strategy based on the partitioning results, deeply combining the interval division and the fitting process to ensure that the characteristics of each disturbance level are independently and orderly expressed during the fitting process. This hierarchical fitting modeling method abandons the averaging bias caused by the unified processing of the overall data, effectively improving the sensitivity of the modeling results to abnormal disturbances or local drastic changes, and providing a more reliable basic data support for subsequent calculations and dynamic extractions.

[0036] S4. Use the fitting model and the interval division results to perform disturbance response projection calculations to obtain the point position update calculation map; In step S4, a disturbance response-driven calculation model is used to update the low-resolution pixel point positions of the original remote sensing image to a high-resolution point position expression map with response stratification characteristics. The model formula is as follows: ; where : represents the final output high-precision point position update calculation map; : is the indicator function for the interval; : Point position re - parsing projection mapping operator; : Point position change density divergence operator.

[0037] The perturbation response - driven solution model in step S4 includes the following processes during use: Input preparation: Output of step S2: Response intensity interval division ; Output of step S3: Perturbation - sensitive fitting function ; Response area assignment and matching: For each pixel , determine its belonging ; Projection mapping operation: For each Apply , project the fitting function back to the remote - sensing image coordinate system to achieve spatial point - position solution; Spatial density extraction: Use the divergence operator to extract the point - position change density in each region and enhance the perturbation - significant area; Reconstruction and combined output: Accumulate the solution results of each interval to form a high - precision point - position update map .

[0038] S5. Use the point - position update solution map to perform perturbation tensor trend modeling to obtain a regional perturbation trend map; Step S5 is based on the high - precision point - position update solution map generated in step S4. By accurately tracking the changes in point - position over a continuous time series, it extracts the dynamic deformation characteristics and judges the perturbation trend within the region. By constructing a spatial map of the perturbation trend, it realizes the quantitative description and directional identification of the surface perturbation evolution process, enabling the change trend to be presented systematically not only at the single - point analysis level but also at the regional scale, forming a clear and traceable perturbation development pattern, and laying a technical foundation for the dynamic application of high - precision surveying and mapping results.

[0039] In step S5, based on the high - precision point - position update map obtained in step S4, a spatial perturbation deformation tensor analysis model is constructed to extract the regional - level dynamic deformation characteristics and judge their trend directions, and finally output a regional perturbation trend map.

[0040] In step S5, a dynamic perturbation deformation tensor trend model is used for spatial perturbation deformation tensor analysis, and its formula model is as follows: , ; Among them, : represents the perturbed main trend direction vector at the position ; : represents 's gradient vector; : symmetric perturbed deformation tensor; : arbitrary direction vector; : is the Euclidean norm; Used to extract the direction that maximizes the tensor response intensity.

[0041] The formula model includes the following during use: Gradient acquisition: First, calculate the two-dimensional gradient of the solution graph to describe the local perturbation direction change: ; Among them, : The partial derivative with respect to , representing the rate of change of along the direction; : The partial derivative with respect to , representing the rate of change of along the direction; Construct the deformation tensor: Use to form a symmetric positive definite tensor to describe the deformation intensity coupling in the perturbation direction; Extract the main trend direction: At each pixel position , find the main trend direction by maximizing , and this direction is the main eigenvector of the tensor, representing the most significant direction of local perturbation.

[0042] The process of extracting the regional dynamic deformation trend in step S5 includes: Input preparation: Output of step S4: high-precision point position updated solution graph ; Gradient calculation: Obtain the two-dimensional gradient , representing the direction of point position perturbation change; Tensor construction: Calculate the perturbation deformation tensor ; Principal direction extraction: Solve for the maximum response direction of the tensor to obtain the perturbation main trend vector , and this direction is the principal eigenvector of the tensor, representing the most significant direction of local perturbation; Form a trend map: Combine the main trend directions at all positions to form a complete perturbation trend map; Map generation: For all points Summarize to form a perturbation trend map.

[0043] Different from traditional static change detection or methods for estimating deformation trends based on a small number of feature points, step S5 adopts a dynamic extraction mechanism based on a complete updated solution map to systematically integrate the point change behaviors within the region, thereby achieving continuous expression of trend information. This method breaks through the limitations of only being able to make local or phased judgments in the past. Through the overall perturbation map generation technology, the evolution process of regional dynamic changes has the characteristics of continuity, directionality, and systematicness, greatly enhancing the ability boundary of high-precision surveying and mapping in spatio-temporal dynamic monitoring.

[0044] Example two: This embodiment also provides a computer device applicable to a high-precision surveying and mapping method based on dynamic remote sensing technology, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement a high-precision surveying and mapping method based on dynamic remote sensing technology as proposed in the above embodiment.

[0045] This embodiment also provides a storage medium with a computer program stored thereon, and when this program is executed by a processor, it implements a high-precision surveying and mapping method based on dynamic remote sensing technology as proposed in the above embodiment.

[0046] The computer device can be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be achieved through WIFI, carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball or a touchpad provided on the casing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0047] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical discs, etc., which can store program codes.

[0048] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus or device and execute the instructions), or in combination with these instruction execution systems, apparatus or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus or device.

[0049] More specific examples (a non-exhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0050] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and the like.

[0051] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A high-precision mapping method based on dynamic remote sensing technology, characterized in that, It includes the following steps: S1. Use multi-temporal remote sensing images to construct surface change characteristics, and obtain a set of change function indicators; S2. Use the set of change function indicators to deconstruct the regional disturbance response, and obtain the response intensity interval division; S3. Use the interval division result to perform disturbance stratification constraint modeling, and obtain a disturbance-sensitive fitting model; S4. Use the fitting model and the interval division result to perform disturbance response projection calculation, and obtain a point position update calculation map; S5. Use the point position update calculation map to perform disturbance tensor trend modeling, and obtain a regional disturbance trend atlas.

2. The high-precision mapping method based on dynamic remote sensing technology according to claim 1, wherein, The step S1 provides a basic surface reflection function index set for subsequent zoning and modeling, for the remote sensing image sequence , that is, the pixel image sequence with a time span of , construct a change trend intensity index function , dynamic fluctuation amplitude index and cumulative disturbance index ; Change trend intensity index function :[[]]END]] ； Dynamic Fluctuation Amplitude Exponential Function : ； Among them, ; Cumulative perturbation exponential function : ； Among them, is the disturbance time series weight function, .

3. A high-precision mapping method based on dynamic remote sensing technology according to claim 1, characterized in that, Construct the coupled perturbation response function in step S2 : ； Among them, represents the gradient of the index function in the spatial dimension, that is, the perturbation change direction.

4. A high-precision mapping method based on dynamic remote sensing technology according to claim 3, characterized in that, The interval division method in step S2 includes: Set of disturbance response intensity levels , where each level corresponds to a sub-interval on the value range: if and only if ; Among them, represents the minimum value of the disturbance response intensity within the given interval; Indicates the maximum value of the disturbance response intensity within a given interval; : For the quantile value of the dataset; : For the quantile value of the dataset.

5. A high-precision mapping method based on dynamic remote sensing technology according to claim 3, characterized in that, The regional disturbance response deconstruction process in step S2 includes: Input: The metric set output by step S1 ; Gradient field calculation: Calculate the gradient of each metric function in the spatial dimension ; Coupling term construction: Construct the gradient direction coupling term and combine it into the response function ; Statistical modeling: perform range distribution analysis on the entire image; Interval division: The value range is divided into perturbation response level intervals according to the set rules; Output: Obtain the division diagram of the disturbance response intensity interval .

6. A high-precision mapping method based on dynamic remote sensing technology according to claim 1, characterized in that, In step S3, a disturbance-sensitive fitting function model is constructed, and its model formula is: ； Among them, represents the th disturbance response intensity interval; : A constant coefficient used to maintain the consistency of physical dimensions; : It represents that within the th response intensity interval, the index function has local response sensitivity to the perturbation function ; : represents the indicator function at the spatial position of the local change function, which is used to construct the local response form of the fitting function.

7. A high-precision mapping method based on dynamic remote sensing technology according to claim 6, characterized in that The disturbance-sensitive fitting modeling process in step S3 includes: Input: The metric function output by step S1 ; The disturbance response function output by step S2 and its interval division ; Local sensitivity extraction: Calculate within each interval ; Local change function construction: Extract the local spatial variation function of each metric function at ; ; Fitting function construction: Construct the perturbation-sensitive fitting function for each response interval using the perturbation-sensitive fitting function model formula ; Fitting model combination: Construct an overall fitting model ; Among them is an indicator function to determine whether a pixel belongs to the interval; ； Output: Set of perturbation-sensitive fitting functions and combined model .

8. A high-precision mapping method based on dynamic remote sensing technology according to claim 1, characterized in that, In step S4, a disturbance response-driven calculation model is used to update the low-resolution pixel point positions of the original remote sensing image into a high-resolution point position expression map with response stratification characteristics, and its model formula is as follows: ； Among them, : represents the high-precision point position update solution diagram of the final output; : is the indicator function for the interval; : Point re - parsing projection mapping operator; : Point change density divergence operator.

9. A high-precision mapping method based on dynamic remote sensing technology according to claim 1, characterized in that, In step S5, a dynamic disturbance deformation tensor trend model is used to perform spatial disturbance deformation tensor analysis, and its formula model is as follows: ， ； Among them, : represents the disturbance main trend direction vector at the position ; : represents the gradient vector of; : symmetric perturbation deformation tensor; : Any direction vector; : is the Euclidean norm; For extracting the direction that maximizes the tensor response intensity.

10. A high-precision mapping method based on dynamic remote sensing technology according to claim 9, characterized in that, The formula model in step S5 includes the following during use: Gradient acquisition: First, calculate the solution graph for the two-dimensional gradient used to describe the change in the local perturbation direction: ； Among them, : The partial derivative with respect to represents the rate of change along the direction of ; : The partial derivative with respect to , representing the rate of change along direction in ; Construct deformation tensor: Use , a symmetric positive definite tensor is formed to describe the deformation strength coupling in the perturbation direction; Extract the main trend direction: At each pixel location , the principal trend direction is found by maximizing , which is the tensor principal eigenvector representing the most significant direction of local perturbations. ​

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