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

Abstract

The present invention discloses a high-precision surveying and mapping method based on dynamic remote sensing technology, which relates to the field of remote sensing and mapping technology, and includes the following steps: S1, using multi-phase remote sensing images to construct surface change features; S2, using a change function indicator set to deconstruct regional disturbance response; S3, using interval division results to perform disturbance hierarchical constraint modeling; S4, using a fitting model and interval division results to perform disturbance response projection solution; S5, using a point update solution diagram to perform disturbance tensor trend modeling. By setting up a partitioned dynamic response feature deconstruction and hierarchical constraint fitting modeling mechanism, high-precision modeling is performed for different disturbance response intensity intervals, effectively solving the problem of local change feature loss caused by a single modeling template in the prior art, ensuring that modeling results that meet local features can be achieved in different response intensity areas, thereby improving the accuracy of point updates and the reliability of deformation extraction.

Description

Technical Field

[0001] The present 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, surface dynamic change monitoring and mapping technology based on remote sensing images has gradually become an important development direction in the field of high-precision surveying and mapping. Traditional methods rely on single-phase images or image data from a small number of time nodes, making it difficult to accurately capture small surface disturbances and their evolution. This is especially true in areas with drastic dynamic changes and fine-scale scales, resulting in insufficient surveying and mapping accuracy and long update cycles. In order to achieve high-precision and continuous description of surface dynamic processes, a surveying and mapping method is urgently needed that can combine the change characteristics of multi-phase images, accurately extract surface change information, and perform detailed 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. As a result, the modeling accuracy in the disturbed area is insufficient, and the local small 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:

[0006] 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:

[0007] S1. Use multi-temporal remote sensing images to construct surface change characteristics and obtain a set of change function indicators;

[0008] S2. Use the variation function indicator set to deconstruct the regional disturbance response and obtain the response intensity interval division;

[0009] S3. Use the interval partitioning results to perform disturbance hierarchical constraint modeling to obtain a disturbance sensitive fitting model;

[0010] S4. Use the fitting model and the interval division result to perform disturbance response projection solution to obtain a point update solution diagram;

[0011] S5. Use the point update solution graph to perform disturbance tensor trend modeling to obtain a regional disturbance trend map.

[0012] 2. Further optimize this technical solution, 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 Pixel image sequence, construct the change trend intensity index function , Dynamic Volatility Index and the cumulative disturbance index ;

[0013] Changing trend strength indicator function :

[0014] ;

[0015] Dynamic Volatility Index Function :

[0016] ;

[0017] in, ;

[0018] Cumulative perturbation exponential function :

[0019] ;

[0020] in, is the disturbance time series weight function, .

[0021] Further optimize this technical solution, in step S2, the coupled disturbance response function is constructed :

[0022] ;

[0023] in, Represents the gradient of the indicator function in the spatial dimension, that is, the direction of disturbance change.

[0024] To further optimize this technical solution, the interval division method in step S2 includes:

[0025] Disturbance response intensity level set , where each level corresponds to A subinterval of the range:

[0026] If and only if ;

[0027] in, Indicates that in a given The minimum value of the disturbance response intensity within the interval;

[0028] Indicates that in a given The maximum value of the disturbance response intensity within the interval;

[0029] :against The dataset Percentile value;

[0030] :against The dataset Percentile value.

[0031] To further optimize this technical solution, the regional disturbance response deconstruction process of step S2 includes:

[0032] Input: The indicator set output by step S1 ;

[0033] Gradient field calculation: Calculate the gradient of each indicator function in the spatial dimension ;

[0034] Coupling term construction: Construct gradient direction coupling terms and combine them into response functions ;

[0035] Statistical Modeling: Perform value range distribution analysis on the entire map;

[0036] Interval division: divide the value range into disturbance response level intervals;

[0037] Output: Get the disturbance response intensity interval division diagram .

[0038] To further optimize this technical solution, a disturbance-sensitive fitting function model is constructed in step S3, and its model formula is:

[0039] ;

[0040] in, Indicates the disturbance response intensity intervals;

[0041] : Constant coefficient, used to maintain physical dimension consistency;

[0042] :Indicates that In the response intensity range, the indicator function Perturbation function local response sensitivity;

[0043] : Indicates the indicator function In spatial position The local variation function is used to construct the local response form of the fitting function.

[0044] To further optimize this technical solution, the disturbance-sensitive fitting modeling process in step S3 includes:

[0045] enter:

[0046] The indicator function output in step S1 ;

[0047] The disturbance response function output in step S2 and its interval division ;

[0048] Local sensitivity extraction:

[0049] In each Calculation within the interval ;

[0050] Local change function construction:

[0051] Extract each indicator function in The local spatial variation function at ;

[0052] Fitting function construction:

[0053] Use the disturbance sensitive fitting function model formula to construct the disturbance sensitive fitting function for each response interval

[0054] Fitting a combination of models:

[0055] Build an overall fitting model ;

[0056] in Is the indicator function, judging whether the pixel belongs to interval;

[0057] ;

[0058] Output:

[0059] Perturbation-sensitive fitting function set and combination models .

[0060] To further optimize this technical solution, in step S4, a disturbance response driven solution model is used to update the low-resolution pixel points of the original remote sensing image into a high-resolution point expression map with response layering characteristics. The model formula is as follows:

[0061] ;

[0062] in, : represents the final output high-precision point update solution diagram;

[0063] :For the Indicator function of interval;

[0064] : point reanalysis projection mapping operator;

[0065] : Point change density divergence operator.

[0066] To further optimize the technical solution, 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:

[0067] ,

[0068] ;

[0069] in, : Indicates location The disturbance main trend direction vector at ;

[0070] :express The gradient vector of

[0071] : Symmetric perturbation deformation tensor;

[0072] : arbitrary direction vector;

[0073] : is the Euclidean norm;

[0074] Used to extract the direction that maximizes the strength of the tensor response.

[0075] To further optimize the technical solution, the formula model in step S5 includes the following during use:

[0076] Gradient acquisition:

[0077] First calculate the solution graph The two-dimensional gradient of is used to describe the change in the direction of the local disturbance:

[0078] ;

[0079] in, : about The partial derivative of Direction rate of change;

[0080] : about The partial derivative of Direction rate of change;

[0081] Construct the deformation tensor:

[0082] use , forming a symmetric positive definite tensor that describes the deformation intensity coupling in the perturbation direction;

[0083] Extract the main trend direction:

[0084] At each pixel location , by maximizing To find the main trend direction , which is the principal eigenvector of the tensor and represents the most significant direction of the local perturbation.

[0085] In a second aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a high-precision surveying and mapping method based on dynamic remote sensing technology as described in the first aspect of the present invention are implemented.

[0086] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a high-precision surveying and mapping method based on dynamic remote sensing technology as described in the first aspect of the present invention are implemented.

[0087] Compared with the existing technology, the present invention provides a high-precision surveying and mapping method based on dynamic remote sensing technology, which has the following beneficial effects:

[0088] This high-precision mapping method based on dynamic remote sensing technology, by setting up a partitioned dynamic response feature deconstruction and hierarchical constraint fitting modeling mechanism, performs high-precision modeling for different disturbance response intensity intervals, effectively solving the problem of local change feature loss caused by a single modeling template in the existing technology. This method uses the disturbance response function constructed by multi-phase image change indicators to first divide the region, and then independently fits it according to the characteristics of each interval to ensure that modeling results that meet local characteristics can be achieved in different response intensity areas, thereby improving the accuracy of point updates 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 micro-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

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 any creative work.

[0090] Figure 1 This is a structural diagram of a high-precision surveying and mapping method based on dynamic remote sensing technology proposed in the present invention;

[0091] Figure 2 This is a schematic diagram of the regional disturbance response deconstruction process of a high-precision surveying and mapping method based on dynamic remote sensing technology proposed in the present invention;

[0092] Figure 3 This is a schematic diagram of the disturbance-sensitive fitting modeling process of a high-precision surveying and mapping method based on dynamic remote sensing technology proposed in the present invention. DETAILED DESCRIPTION

[0093] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0094] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0095] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it constitute a separate or selective embodiment that is mutually exclusive with other embodiments.

[0096] Example 1:

[0097] Reference Figures 1 to 3 , which is the first embodiment of the present invention, provides a high-precision surveying and mapping method based on dynamic remote sensing technology, comprising the following steps:

[0098] S1. Use multi-temporal remote sensing images to construct surface change characteristics and obtain a set of change function indicators;

[0099] Step S1 provides a basic surface reflection function index set for subsequent partitioning and modeling, targeting remote sensing image sequences , that is, the time span is Pixel image sequence, construct the change trend intensity index function , Dynamic Volatility Index and the cumulative disturbance index ,in Indicates 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, Indicates the Time, location Remote sensing characteristic values such as reflectivity or radiation value.

[0100] Changing trend strength indicator function :

[0101] ;

[0102] This function represents the average change amplitude per unit time and reflects the frequency of disturbance.

[0103] Dynamic Volatility Index Function :

[0104] ;

[0105] in, ;

[0106] This function is the standard deviation, which represents the severity of the change.

[0107] Cumulative perturbation exponential function :

[0108] ;

[0109] in, is the disturbance time series weight function, ;

[0110] This function enhances the cumulative perception of long-term disturbance behavior.

[0111] Changing trend strength indicator function in step S1 , Dynamic Volatility Index and the cumulative disturbance index When used, include:

[0112] First, for each pixel In continuous Remote sensing image frames, extract their time series values ;

[0113] Calculate the three function indicators separately and get the corresponding indicator graph , these graphs are function fields in spatial dimensions;

[0114] Will Combination constitutes indicator vector set , as input for subsequent partitioning;

[0115] If there are multiple spectral channels, each channel is calculated independently to form a channel enhancement index set. For each spectral channel , and the indicators on the corresponding channels are;

[0116] ;

[0117] in, Indicates channel The indicator of the strength of the changing trend;

[0118] Indicates channel Dynamic volatility index on ;

[0119] Indicates channel The cumulative disturbance index on .

[0120] S2. Use the variation function indicator set to deconstruct the regional disturbance response and obtain the response intensity interval division;

[0121] Step S2 uses the set of change function indicators constructed in step S1 as input and performs continuous feature quantification and zoning processing based on the dynamic response characteristics of surface change. By establishing a set of levels of surface disturbance response intensity, a dynamic deconstruction of change indicators is achieved, providing a clearer hierarchical division of change characteristics across spatial scales. This step maps spatially continuous change indicators to discrete zoned disturbance levels, providing clear and targeted basic interval support for subsequent hierarchical fitting and model construction, ensuring the inherent logical consistency and operability of the phased processing process.

[0122] Step S2 generates the Perform structural analysis of spatial partitioning and dynamic response characteristics to form an orderly division of regional disturbance characteristic intensity, providing a targeted modeling basis for subsequent modeling steps;

[0123] In step S2, the coupled disturbance response function is constructed :

[0124] ;

[0125] in, Represents the gradient of the index function in the spatial dimension, that is, the direction of disturbance change, refer to 、 and One of the following;

[0126] The interval division method in step S2 includes:

[0127] Disturbance response intensity level set , where each level corresponds to A subinterval of the range:

[0128] If and only if ;

[0129] in, Indicates that in a given The minimum value of the disturbance response intensity within the interval;

[0130] Indicates that in a given The maximum value of the disturbance response intensity within the interval;

[0131] :against The dataset Percentile value;

[0132] :against The dataset Percentile value.

[0133] The regional disturbance response deconstruction process in step S2 includes:

[0134] Input: The indicator set output by step S1 ;

[0135] Gradient field calculation: Calculate the gradient of each indicator function in the spatial dimension ;

[0136] Coupling term construction: Construct gradient direction coupling terms and combine them into response functions ;

[0137] Statistical Modeling: Perform value range distribution analysis on the entire map;

[0138] Interval division: divide the value range into disturbance response level intervals;

[0139] Output: Get the disturbance response intensity interval division diagram .

[0140] Unlike traditional methods based on static thresholds or simple clustering, step S2 introduces a quantitative representation of dynamic response characteristics during the partitioning process and deconstructs the internal structure based on the continuity and spatial correlation of the change response. This approach no longer relies on external experience or static classification standards, but instead uses a self-derived response index system to set intervals. This allows surface change information to dynamically adapt to changes in temporal and spatial scales, enhancing the physical significance and applicability of disturbance response partitioning.

[0141] S3. Use the interval partitioning results to perform disturbance hierarchical constraint modeling to obtain a disturbance sensitive fitting model;

[0142] Step S3 follows the disturbance intensity interval division results completed in step S2. Based on the differences in surface disturbance characteristics within different intervals, hierarchical constraints are set to perform high-precision fitting modeling of remote sensing point data. Through hierarchical correlation control, a fine-grained fitting is performed based on the specific changing trends of each disturbance level, enabling the model to more accurately capture the local response characteristics of surface changes at each level. The resulting disturbance-sensitive fitting model is not only stable in terms of overall trends but also reflects disturbance heterogeneity at a fine-grained scale, significantly enhancing the model's adaptability in complex dynamic environments.

[0143] Step S3 divides the disturbance response intensity interval obtained in step S2 into two parts. Conduct modeling and construct a high-precision fitting model of remote sensing points with disturbance sensitivity to characterize the fitting trend of ground feature changes in different intensity response areas;

[0144] In step S3, a disturbance-sensitive fitting function model is constructed, and its model formula is:

[0145] ;

[0146] in, Indicates the disturbance response intensity intervals;

[0147] : Constant coefficient, used to maintain physical dimension consistency;

[0148] :Indicates that In the response intensity range, the indicator function Perturbation function local response sensitivity;

[0149] : Indicates the indicator function In spatial position The local variation function is used to construct the local response form of the fitting function. The formula of the local variation function is as follows:

[0150] ;

[0151] In this formula, Represents the indicator function Along The first-order spatial partial derivative of the direction is used to measure the intensity of the disturbance of the indicator in the horizontal direction;

[0152] Represents the indicator function Along The first-order spatial partial derivative in the direction is used to measure the response non-uniformity of the indicator in the vertical direction.

[0153] The disturbance-sensitive fitting modeling process in step S3 includes:

[0154] enter:

[0155] The indicator function output in step S1 ;

[0156] The disturbance response function output in step S2 and its interval division ;

[0157] Local sensitivity extraction:

[0158] In each Calculation within the interval ;

[0159] Local change function construction:

[0160] Extract each indicator function in The local spatial variation function at ;

[0161] Fitting function construction:

[0162] Use the disturbance sensitive fitting function model formula to construct the disturbance sensitive fitting function for each response interval

[0163] Fitting a combination of models:

[0164] Build an overall fitting model ;

[0165] in Is the indicator function, judging whether the pixel belongs to interval;

[0166] ;

[0167] Output:

[0168] Perturbation-sensitive fitting function set and combination models .

[0169] Unlike existing methods based on uniform or extensive fitting, step S3 employs a hierarchical constraint strategy based on partitioned results, deeply integrating interval division with the fitting process to ensure that each disturbance level characteristic is independently and orderly expressed during the fitting process. This hierarchical fitting modeling approach eliminates the averaging bias caused by uniform processing of the entire data, effectively increasing the sensitivity of the modeling results to abnormal disturbances or localized drastic changes, and providing more reliable basic data support for subsequent solutions and dynamic extraction.

[0170] S4. Use the fitting model and the interval division result to perform disturbance response projection solution to obtain a point update solution diagram;

[0171] In step S4, the disturbance response driven solution model is used to update the low-resolution pixel points of the original remote sensing image to a high-resolution point expression map with response layering characteristics. The model formula is as follows:

[0172] ;

[0173] in, : represents the final output high-precision point update solution diagram;

[0174] :For the Indicator function of interval;

[0175] : point reanalysis projection mapping operator;

[0176] : Point change density divergence operator.

[0177] The disturbance response driven solution model in step S4 includes the following processes during use:

[0178] Input preparation:

[0179] Step S2 output: response intensity interval division ;

[0180] Step S3 output: disturbance sensitive fitting function ;

[0181] Response zone assignment matching:

[0182] For each pixel , determine its ;

[0183] Projection mapping operation:

[0184] For each application , project the fitting function back to the remote sensing image coordinate system to achieve spatial point solution;

[0185] Spatial density extraction:

[0186] Using the divergence operator Extract the density of point changes in each area and enhance the disturbance-significant areas;

[0187] Reconstructed combined output:

[0188] Accumulate the solution results of each interval to form a high-precision point update map .

[0189] S5. Use the point update solution graph to perform disturbance tensor trend modeling to obtain a regional disturbance trend map;

[0190] Step S5, based on the high-precision point update solution generated in step S4, accurately tracks point position changes over a continuous time series to extract regional dynamic deformation characteristics and determine disturbance trends. By constructing a spatial map of disturbance trends, the evolution of surface disturbances can be quantitatively characterized and directional identified. This allows trends to be systematically presented at the regional scale, not just at the single-point analysis level, forming a clear and traceable disturbance development model and laying the technical foundation for the dynamic application of high-precision surveying and mapping results.

[0191] Step S5: Update the high-precision point map obtained in step S4. On this basis, a spatial disturbance deformation tensor analysis model is constructed to extract the dynamic deformation characteristics at the regional level and determine its trend direction, and finally output the regional disturbance trend map.

[0192] 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:

[0193] ,

[0194] ;

[0195] in, : Indicates location The disturbance main trend direction vector at ;

[0196] :express The gradient vector of

[0197] : Symmetric perturbation deformation tensor;

[0198] : arbitrary direction vector;

[0199] : is the Euclidean norm;

[0200] Used to extract the direction that maximizes the strength of the tensor response.

[0201] The formula model includes:

[0202] Gradient acquisition:

[0203] First calculate the solution graph The two-dimensional gradient of is used to describe the change in the direction of the local disturbance:

[0204] ;

[0205] in, : about The partial derivative of Direction rate of change;

[0206] : about The partial derivative of Direction rate of change;

[0207] Construct the deformation tensor:

[0208] use , forming a symmetric positive definite tensor that describes the deformation intensity coupling in the perturbation direction;

[0209] Extract the main trend direction:

[0210] At each pixel location , by maximizing To find the main trend direction , which is the principal eigenvector of the tensor and represents the most significant direction of the local perturbation.

[0211] The regional dynamic deformation trend extraction process in step S5 includes:

[0212] Input preparation:

[0213] Step S4 output: High-precision point update solution diagram ;

[0214] Gradient calculation:

[0215] Get 2D gradient , indicating the direction of change of the point disturbance;

[0216] Tensor construction:

[0217] Calculate the perturbation deformation tensor ;

[0218] Main direction extraction:

[0219] Solve the maximum response direction of the tensor to obtain the main trend vector of the disturbance , this direction is the principal eigenvector of the tensor, indicating the most significant direction of local perturbation;

[0220] Forming a trend map:

[0221] Combine the main trend directions of all positions to form a complete disturbance trend map;

[0222] Graph generation:

[0223] All points Summarize to form a disturbance trend map.

[0224] Unlike traditional static change detection or deformation trend estimation methods based on a small number of feature points, step S5 employs a dynamic extraction mechanism based on a fully updated solution graph, systematically integrating the behavior of point changes within the region, thereby achieving continuous expression of trend information. This method breaks through the limitations of previous local or phased judgments. By generating a holistic disturbance map, it imbues the evolution of regional dynamic changes with continuity, directionality, and systematicity, significantly expanding the capabilities of high-precision surveying and mapping for spatiotemporal dynamic monitoring.

[0225] Example 2:

[0226] This embodiment also provides a computer device suitable for 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 computer-executable instructions to implement a high-precision surveying and mapping method based on dynamic remote sensing technology as proposed in the above embodiment.

[0227] This embodiment further provides a storage medium storing a computer program, which, when executed by a processor, implements a high-precision surveying and mapping method based on dynamic remote sensing technology as proposed in the above embodiment.

[0228] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0229] If a function is implemented as 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, or the portion that contributes to the prior art, or a portion of the 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, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0230] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For 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 conjunction with, an instruction execution system, apparatus, or device.

[0231] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting, or processing it in another suitable manner as necessary, and then storing it in a computer memory.

[0232] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0233] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A high-precision surveying and mapping method based on dynamic remote sensing technology, characterized in that: The following steps are involved: S1. Use multi-temporal remote sensing images to construct surface change characteristics and obtain a set of change function indicators; S2. Use the variation function indicator set to deconstruct the regional disturbance response and obtain the response intensity interval division; The regional disturbance response deconstruction process of step S2 includes: Input: The indicator set output by step S1 ; Among them, the trend strength indicator function , Dynamic Volatility Index and the cumulative disturbance index ; Gradient field calculation: Calculate the gradient of each indicator function in the spatial dimension ,in, Represents the gradient of the index function in the spatial dimension, that is, the direction of disturbance change; Coupling term construction: Construct gradient direction coupling terms and combine them into response functions ; Statistical Modeling: Perform value range distribution analysis on the entire map; Interval division: divide the value range into disturbance response level intervals, disturbance response intensity level sets , where each level corresponds to A subinterval of the range; Output: Get the disturbance response intensity interval division diagram , Indicates the disturbance response intensity intervals; S3. Use the interval partitioning results to perform disturbance hierarchical constraint modeling to obtain a disturbance sensitive fitting model; The disturbance-sensitive fitting modeling process in step S3 includes: enter: The indicator function output in step S1 ; The disturbance response function output in step S2 and its interval division ; Local sensitivity extraction: In each Calculation within the interval , :Indicates that In the response intensity range, the indicator function Perturbation function local response sensitivity; Local change function construction: Extract each indicator function in The local spatial variation 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 a combination of models: Build an overall fitting model ; in Is the indicator function, judging whether the pixel belongs to interval; Output: Perturbation-sensitive fitting function set and combination models ; 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 graph to perform disturbance tensor trend modeling to obtain a regional disturbance trend map.

2. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 1, characterized in that: The step S1 provides a basic surface reflection function index set for subsequent partitioning and modeling, and for remote sensing image sequences , that is, the time span is Pixel image sequence, construct the change trend intensity index function , Dynamic Volatility Index and the cumulative disturbance index ; Changing trend strength indicator function : ； Dynamic Volatility Index Function : ； in, ; Cumulative perturbation exponential function : ； in, is the disturbance time series weight function, .

3. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 1, characterized in that: In step S2, the coupled disturbance response function is constructed : ； in, Represents the gradient of the indicator function in the spatial dimension, that is, the direction of disturbance change.

4. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 3 is characterized in that: The interval division method in step S2 includes: Disturbance response intensity level set , where each level corresponds to A subinterval of the range: If and only if ; in, Indicates that in a given The minimum value of the disturbance response intensity within the interval; Indicates that in a given The maximum value of the disturbance response intensity within the interval; :against The dataset Percentile value; :against The dataset Percentile value.

5. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 3 is characterized in that: The regional disturbance response deconstruction process of step S2 includes: Input: The indicator set output by step S1 ; Gradient field calculation: Calculate the gradient of each indicator function in the spatial dimension ; Coupling term construction: Construct gradient direction coupling terms and combine them into response functions ; Statistical Modeling: Perform value range distribution analysis on the entire map; Interval division: divide the value range into disturbance response level intervals; Output: Get the disturbance response intensity interval division diagram .

6. The high-precision surveying and 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: ； in, Indicates the disturbance response intensity intervals; : Constant coefficient, used to maintain physical dimension consistency; :Indicates that In the response intensity range, the indicator function Perturbation function local response sensitivity; : Indicates the indicator function In spatial position The local variation function is used to construct the local response form of the fitting function.

7. The high-precision surveying and 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: enter: The indicator function output in step S1 ; The disturbance response function output in step S2 and its interval division ; Local sensitivity extraction: In each Calculation within the interval ; Local change function construction: Extract each indicator function in The local spatial variation 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 a combination of models: Build an overall fitting model ; in Is the indicator function, judging whether the pixel belongs to interval; ； Output: Perturbation-sensitive fitting function set and combination models .

8. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 1, characterized in that: In step S4, a disturbance response driven solution model is used to update the low-resolution pixel points of the original remote sensing image into a high-resolution point expression map with response layering characteristics. The model formula is as follows: ； in, : represents the final output high-precision point update solution diagram; :For the Indicator function of interval; : point reanalysis projection mapping operator; : Point change density divergence operator.

9. The high-precision surveying and 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: ， ； in, : Indicates location The disturbance main trend direction vector at ; :express The gradient vector of : Symmetric perturbation deformation tensor; : arbitrary direction vector; : is the Euclidean norm; Used to extract the direction that maximizes the strength of the tensor response.

10. The high-precision surveying and mapping method based on dynamic remote sensing technology according to claim 9, characterized in that: The formula model in step S5 includes the following steps during use: Gradient acquisition: First calculate the solution graph The two-dimensional gradient of is used to describe the change in the direction of the local disturbance: ； in, : about The partial derivative of Direction rate of change; : about The partial derivative of Direction rate of change; Construct the deformation tensor: use , forming a symmetric positive definite tensor that describes the deformation intensity coupling in the perturbation direction; Extract the main trend direction: At each pixel location , by maximizing To find the main trend direction , which is the principal eigenvector of the tensor and represents the most significant direction of the local perturbation.

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